[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JDQC8DJMEWM0ACY8TSTA7B":3,"skill-same:01M6JDQC8DJMEWM0ACY8TSTA7B":74},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":42,"linkOnly":43,"markdownContent":44,"markdownBody":45,"frontMatter":46,"refName":47,"commitSha":48,"skillDirectory":49,"forksCount":13,"repositoryHtmlUrl":50,"repositoryHomepageUrl":6,"ownerHtmlUrl":51,"ownerBlogUrl":6,"ownerDescription":52,"updatedAt":53,"install":54,"resources":67},"01M6JDQC8DJMEWM0ACY8TSTA7B","aiops",null,"Generic AIOps (AI for IT Operations) patterns and best practices for 2025. Provides comprehensive implementation strategies for intelligent monitoring, automation, incident response, and observability across any infrastructure. Framework-agnostic approach supporting multiple monitoring platforms, cloud providers, and automation tools.","superhealth\u002Fskills-hub","superhealth","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6292112?v=4","01M6JA6E7F21BYK5W3E4P270TT","01M6JA6EC0W8MPHKDJVBZDSEZ0",0,"MIT","https:\u002F\u002Fgithub.com\u002Fsuperhealth\u002Fskills-hub\u002Fblob\u002Fmaster\u002Fskills\u002Fazeem-2\u002Faiops\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[5,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41],"implementation","for","framework-agnostic","2025","it","strategies","across","incident","patterns","incident response","operations","it operations","Providers","Tools","cloud","monitoring","observability","best practices","automation","2026-11-10 10:12:30",false,"---\nname: aiops\ndescription: Generic AIOps (AI for IT Operations) patterns and best practices for 2025. Provides comprehensive implementation strategies for intelligent monitoring, automation, incident response, and observability across any infrastructure. Framework-agnostic approach supporting multiple monitoring platforms, cloud providers, and automation tools.\nlicense: MIT\n---\n\n# AIOps - AI for IT Operations\n\nThis skill provides comprehensive patterns for implementing AIOps strategies in 2025, including intelligent monitoring, automated incident response, predictive analytics, and observability best practices. The patterns are designed to be framework-agnostic and applicable across different infrastructure platforms.\n\n## When to Use This Skill\n\nUse this skill when you need to:\n- Implement AIOps strategies for modern infrastructure\n- Build intelligent monitoring and alerting systems\n- Create automated incident response workflows\n- Deploy predictive maintenance solutions\n- Implement self-healing capabilities\n- Build observability platforms with AI\u002FML\n- Optimize multi-cloud operations\n- Create chaos engineering practices\n- Implement generative AI for operations\n- Build digital twins for infrastructure\n\n## 1. AIOps Architecture Patterns\n\n### Core AIOps Platform Architecture\n\n```python\n# aiops\u002Fcore\u002Farchitecture.py\nfrom abc import ABC, abstractmethod\nfrom typing import Dict, List, Optional, Any, Union\nfrom dataclasses import dataclass, field\nfrom datetime import datetime, timedelta\nfrom enum import Enum\nimport asyncio\nimport json\nimport logging\n\nlogger = logging.getLogger(__name__)\n\nclass AlertSeverity(str, Enum):\n    \"\"\"Alert severity levels\"\"\"\n    CRITICAL = \"critical\"\n    HIGH = \"high\"\n    MEDIUM = \"medium\"\n    LOW = \"low\"\n    INFO = \"info\"\n\nclass IncidentStatus(str, Enum):\n    \"\"\"Incident status\"\"\"\n    OPEN = \"open\"\n    INVESTIGATING = \"investigating\"\n    IDENTIFIED = \"identified\"\n    MONITORING = \"monitoring\"\n    RESOLVED = \"resolved\"\n    CLOSED = \"closed\"\n\n@dataclass\nclass Metric:\n    \"\"\"Metric data point\"\"\"\n    name: str\n    value: float\n    timestamp: datetime\n    labels: Dict[str, str] = field(default_factory=dict)\n    source: str = \"\"\n    unit: str = \"\"\n\n@dataclass\nclass LogEntry:\n    \"\"\"Structured log entry\"\"\"\n    timestamp: datetime\n    level: str\n    message: str\n    service: str\n    trace_id: Optional[str] = None\n    span_id: Optional[str] = None\n    labels: Dict[str, str] = field(default_factory=dict)\n    stack_trace: Optional[str] = None\n\n@dataclass\nclass Trace:\n    \"\"\"Distributed trace data\"\"\"\n    trace_id: str\n    spans: List[Dict[str, Any]]\n    duration: timedelta\n    service_map: Dict[str, List[str]]\n    error_count: int = 0\n\n@dataclass\nclass Alert:\n    \"\"\"Alert representation\"\"\"\n    id: str\n    name: str\n    severity: AlertSeverity\n    status: IncidentStatus\n    description: str\n    source: str\n    timestamp: datetime\n    labels: Dict[str, str] = field(default_factory=dict)\n    annotations: Dict[str, str] = field(default_factory=dict)\n    fingerprint: str = \"\"\n    related_entities: List[str] = field(default_factory=list)\n\nclass DataSource(ABC):\n    \"\"\"Abstract data source interface\"\"\"\n\n    @abstractmethod\n    async def collect_metrics(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime\n    ) -> List[Metric]:\n        \"\"\"Collect metrics from data source\"\"\"\n        pass\n\n    @abstractmethod\n    async def query_logs(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime,\n        limit: int = 100\n    ) -> List[LogEntry]:\n        \"\"\"Query logs from data source\"\"\"\n        pass\n\n    @abstractmethod\n    async def get_trace(self, trace_id: str) -> Optional[Trace]:\n        \"\"\"Get trace data\"\"\"\n        pass\n\nclass AIOpsEngine:\n    \"\"\"Core AIOps processing engine\"\"\"\n\n    def __init__(self):\n        self.data_sources: List[DataSource] = []\n        self.alert_processors: List[AlertProcessor] = []\n        self.ml_models: Dict[str, Any] = {}\n        self.knowledge_base: KnowledgeBase = KnowledgeBase()\n        self.automation_engine = AutomationEngine()\n\n    def register_data_source(self, data_source: DataSource) -> None:\n        \"\"\"Register a data source\"\"\"\n        self.data_sources.append(data_source)\n        logger.info(f\"Registered data source: {type(data_source).__name__}\")\n\n    def register_alert_processor(self, processor: 'AlertProcessor') -> None:\n        \"\"\"Register an alert processor\"\"\"\n        self.alert_processors.append(processor)\n        logger.info(f\"Registered alert processor: {type(processor).__name__}\")\n\n    async def process_alert(self, alert: Alert) -> Dict[str, Any]:\n        \"\"\"Process incoming alert through AIOps pipeline\"\"\"\n        logger.info(f\"Processing alert: {alert.id} - {alert.name}\")\n\n        # 1. Enrich alert with context\n        enriched_alert = await self._enrich_alert(alert)\n\n        # 2. Run through ML models for classification and prediction\n        analysis = await self._analyze_alert(enriched_alert)\n\n        # 3. Determine appropriate response\n        response_plan = await self._generate_response_plan(enriched_alert, analysis)\n\n        # 4. Execute automated actions if applicable\n        if response_plan.get(\"auto_execute\", False):\n            await self._execute_response(enriched_alert, response_plan)\n\n        # 5. Update knowledge base\n        await self.knowledge_base.update_from_alert(enriched_alert, analysis, response_plan)\n\n        return {\n            \"alert_id\": alert.id,\n            \"status\": \"processed\",\n            \"analysis\": analysis,\n            \"response_plan\": response_plan\n        }\n\n    async def _enrich_alert(self, alert: Alert) -> Alert:\n        \"\"\"Enrich alert with additional context\"\"\"\n        # Get related metrics\n        for source in self.data_sources:\n            try:\n                metrics = await source.collect_metrics(\n                    query=f\"{alert.name}[5m]\",\n                    start_time=alert.timestamp - timedelta(minutes=5),\n                    end_time=alert.timestamp\n                )\n\n                # Add metrics to alert annotations\n                alert.annotations[f\"metrics_{type(source).__name__}\"] = json.dumps([\n                    {\"name\": m.name, \"value\": m.value} for m in metrics\n                ])\n            except Exception as e:\n                logger.error(f\"Failed to enrich alert with metrics: {e}\")\n\n        # Get related logs\n        for source in self.data_sources:\n            try:\n                logs = await source.query_logs(\n                    query=f\"service:{alert.source} level:error\",\n                    start_time=alert.timestamp - timedelta(minutes=5),\n                    end_time=alert.timestamp,\n                    limit=10\n                )\n\n                # Add logs to alert context\n                alert.annotations[\"recent_errors\"] = json.dumps([\n                    {\"timestamp\": l.timestamp.isoformat(), \"message\": l.message}\n                    for l in logs\n                ])\n            except Exception as e:\n                logger.error(f\"Failed to enrich alert with logs: {e}\")\n\n        return alert\n\n    async def _analyze_alert(self, alert: Alert) -> Dict[str, Any]:\n        \"\"\"Analyze alert using ML models\"\"\"\n        analysis = {\n            \"classification\": None,\n            \"severity_score\": 0.0,\n            \"predicted_impact\": \"low\",\n            \"recommendations\": [],\n            \"related_incidents\": []\n        }\n\n        # Classification model\n        if \"classification\" in self.ml_models:\n            features = self._extract_features(alert)\n            analysis[\"classification\"] = self.ml_models[\"classification\"].predict([features])[0]\n\n        # Severity prediction\n        if \"severity\" in self.ml_models:\n            features = self._extract_features(alert)\n            analysis[\"severity_score\"] = self.ml_models[\"severity\"].predict_proba([features])[0][1]\n\n        # Impact prediction\n        if \"impact\" in self.ml_models:\n            features = self._extract_features(alert)\n            analysis[\"predicted_impact\"] = self.ml_models[\"impact\"].predict([features])[0]\n\n        # Find related incidents from knowledge base\n        analysis[\"related_incidents\"] = await self.knowledge_base.find_similar_incidents(alert)\n\n        return analysis\n\n    async def _generate_response_plan(\n        self,\n        alert: Alert,\n        analysis: Dict[str, Any]\n    ) -> Dict[str, Any]:\n        \"\"\"Generate automated response plan\"\"\"\n        plan = {\n            \"priority\": alert.severity,\n            \"actions\": [],\n            \"escalation_rules\": [],\n            \"auto_execute\": False,\n            \"estimated_resolution_time\": None\n        }\n\n        # Determine if auto-execution is safe\n        if (analysis[\"classification\"] in [\"known_issue\", \"false_positive\"] and\n            alert.severity in [AlertSeverity.LOW, AlertSeverity.MEDIUM]):\n            plan[\"auto_execute\"] = True\n\n        # Generate actions based on classification\n        classification = analysis.get(\"classification\")\n\n        if classification == \"resource_exhaustion\":\n            plan[\"actions\"].append({\n                \"type\": \"scale\",\n                \"target\": alert.source,\n                \"params\": {\"replicas\": \"+20%\"}\n            })\n            plan[\"actions\"].append({\n                \"type\": \"notify\",\n                \"channel\": \"devops\",\n                \"message\": f\"Resource exhaustion detected in {alert.source}\"\n            })\n\n        elif classification == \"known_issue\":\n            # Apply known fix\n            known_fix = await self.knowledge_base.get_known_fix(alert.name)\n            if known_fix:\n                plan[\"actions\"].append(known_fix)\n\n        elif classification == \"false_positive\":\n            plan[\"actions\"].append({\n                \"type\": \"suppress\",\n                \"duration\": \"1h\",\n                \"reason\": \"False positive detected\"\n            })\n\n        # Add escalation rules\n        if alert.severity == AlertSeverity.CRITICAL:\n            plan[\"escalation_rules\"].append({\n                \"condition\": \"no_response_5m\",\n                \"action\": \"escalate_to_oncall\",\n                \"timeout\": 300\n            })\n\n        return plan\n\n    def _extract_features(self, alert: Alert) -> List[float]:\n        \"\"\"Extract features for ML models\"\"\"\n        # Simple feature extraction - in production, use more sophisticated methods\n        features = [\n            float(alert.severity == AlertSeverity.CRITICAL),\n            float(alert.severity == AlertSeverity.HIGH),\n            float(alert.severity == AlertSeverity.MEDIUM),\n            len(alert.description),\n            len(alert.labels),\n            alert.timestamp.hour \u002F 24.0,\n            alert.timestamp.weekday() \u002F 7.0\n        ]\n        return features\n\n    async def _execute_response(\n        self,\n        alert: Alert,\n        response_plan: Dict[str, Any]\n    ) -> None:\n        \"\"\"Execute automated response actions\"\"\"\n        for action in response_plan.get(\"actions\", []):\n            try:\n                await self.automation_engine.execute(action)\n                logger.info(f\"Executed action for alert {alert.id}: {action['type']}\")\n            except Exception as e:\n                logger.error(f\"Failed to execute action: {e}\")\n```\n\n### Monitoring Platform Integrations\n\n```python\n# aiops\u002Fintegrations\u002Fmonitoring.py\nimport aiohttp\nimport asyncio\nfrom typing import List, Dict, Any, Optional\nfrom datetime import datetime\nfrom ..core.architecture import DataSource, Metric, LogEntry, Trace, Alert\n\nclass PrometheusDataSource(DataSource):\n    \"\"\"Prometheus metrics integration\"\"\"\n\n    def __init__(self, url: str):\n        self.url = url.rstrip(\"\u002F\")\n        self.session = None\n\n    async def _get_session(self) -> aiohttp.ClientSession:\n        if not self.session:\n            self.session = aiohttp.ClientSession()\n        return self.session\n\n    async def collect_metrics(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime\n    ) -> List[Metric]:\n        \"\"\"Query Prometheus for metrics\"\"\"\n        session = await self._get_session()\n\n        params = {\n            \"query\": query,\n            \"start\": start_time.timestamp(),\n            \"end\": end_time.timestamp(),\n            \"step\": \"15s\"\n        }\n\n        url = f\"{self.url}\u002Fapi\u002Fv1\u002Fquery_range\"\n\n        async with session.get(url, params=params) as response:\n            data = await response.json()\n            metrics = []\n\n            if data[\"status\"] == \"success\":\n                result = data[\"data\"]\n                if result[\"resultType\"] == \"matrix\":\n                    for series in result[\"result\"]:\n                        labels = series[\"metric\"]\n                        values = series[\"values\"]\n\n                        for timestamp, value in values:\n                            metrics.append(Metric(\n                                name=query,\n                                value=float(value),\n                                timestamp=datetime.fromtimestamp(float(timestamp)),\n                                labels=labels,\n                                source=\"prometheus\"\n                            ))\n\n            return metrics\n\n    async def query_logs(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime,\n        limit: int = 100\n    ) -> List[LogEntry]:\n        \"\"\"Prometheus doesn't store logs - use Loki integration\"\"\"\n        return []\n\n    async def get_trace(self, trace_id: str) -> Optional[Trace]:\n        \"\"\"Prometheus doesn't store traces - use Jaeger\u002FTempo integration\"\"\"\n        return None\n\nclass LokiDataSource(DataSource):\n    \"\"\"Loki log aggregation integration\"\"\"\n\n    def __init__(self, url: str):\n        self.url = url.rstrip(\"\u002F\")\n        self.session = None\n\n    async def _get_session(self) -> aiohttp.ClientSession:\n        if not self.session:\n            self.session = aiohttp.ClientSession()\n        return self.session\n\n    async def collect_metrics(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime\n    ) -> List[Metric]:\n        \"\"\"Loki doesn't store metrics\"\"\"\n        return []\n\n    async def query_logs(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime,\n        limit: int = 100\n    ) -> List[LogEntry]:\n        \"\"\"Query Loki for logs\"\"\"\n        session = await self._get_session()\n\n        params = {\n            \"query\": query,\n            \"start\": start_time.isoformat(),\n            \"end\": end_time.isoformat(),\n            \"limit\": str(limit)\n        }\n\n        url = f\"{self.url}\u002Floki\u002Fapi\u002Fv1\u002Fquery_range\"\n\n        async with session.get(url, params=params) as response:\n            data = await response.json()\n            logs = []\n\n            if data[\"status\"] == \"success\":\n                result = data[\"data\"]\n                for series in result[\"result\"]:\n                    stream = series[\"stream\"]\n                    for timestamp, line in series[\"values\"]:\n                        logs.append(LogEntry(\n                            timestamp=datetime.fromtimestamp(float(timestamp) \u002F 1e9),\n                            level=stream.get(\"level\", \"info\"),\n                            message=line,\n                            service=stream.get(\"service\", \"unknown\"),\n                            labels=stream\n                        ))\n\n            return logs\n\n    async def get_trace(self, trace_id: str) -> Optional[Trace]:\n        \"\"\"Loki doesn't store traces\"\"\"\n        return None\n\nclass JaegerDataSource(DataSource):\n    \"\"\"Jaeger distributed tracing integration\"\"\"\n\n    def __init__(self, url: str):\n        self.url = url.rstrip(\"\u002F\")\n        self.session = None\n\n    async def _get_session(self) -> aiohttp.ClientSession:\n        if not self.session:\n            self.session = aiohttp.ClientSession()\n        return self.session\n\n    async def collect_metrics(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime\n    ) -> List[Metric]:\n        \"\"\"Jaeger doesn't store metrics\"\"\"\n        return []\n\n    async def query_logs(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime,\n        limit: int = 100\n    ) -> List[LogEntry]:\n        \"\"\"Jaeger doesn't store logs\"\"\"\n        return []\n\n    async def get_trace(self, trace_id: str) -> Optional[Trace]:\n        \"\"\"Get trace from Jaeger\"\"\"\n        session = await self._get_session()\n\n        url = f\"{self.url}\u002Fapi\u002Ftraces\u002F{trace_id}\"\n\n        async with session.get(url) as response:\n            data = await response.json()\n\n            if data[\"data\"] and data[\"data\"][0][\"spans\"]:\n                spans = data[\"data\"][0][\"spans\"]\n                service_map = {}\n                error_count = 0\n\n                for span in spans:\n                    service = span[\"process\"][\"serviceName\"]\n                    if service not in service_map:\n                        service_map[service] = []\n                    service_map[service].append(span[\"spanID\"])\n\n                    if span.get(\"tags\"):\n                        for tag in span[\"tags\"]:\n                            if tag.get(\"key\") == \"error\" and tag.get(\"value\"):\n                                error_count += 1\n\n                return Trace(\n                    trace_id=trace_id,\n                    spans=spans,\n                    duration=timedelta(\n                        microseconds=data[\"data\"][0][\"duration\"]\n                    ),\n                    service_map=service_map,\n                    error_count=error_count\n                )\n\n            return None\n\nclass DatadogDataSource(DataSource):\n    \"\"\"Datadog APM integration\"\"\"\n\n    def __init__(self, api_key: str, app_key: str, site: str = \"datadoghq.com\"):\n        self.api_key = api_key\n        self.app_key = app_key\n        self.site = site\n        self.base_url = f\"https:\u002F\u002Fapi.{site}\"\n        self.session = None\n\n    async def _get_session(self) -> aiohttp.ClientSession:\n        if not self.session:\n            headers = {\n                \"DD-API-KEY\": self.api_key,\n                \"DD-APPLICATION-KEY\": self.app_key\n            }\n            self.session = aiohttp.ClientSession(headers=headers)\n        return self.session\n\n    async def collect_metrics(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime\n    ) -> List[Metric]:\n        \"\"\"Query Datadog metrics\"\"\"\n        session = await self._get_session()\n\n        params = {\n            \"query\": query,\n            \"from\": int(start_time.timestamp()),\n            \"to\": int(end_time.timestamp())\n        }\n\n        url = f\"{self.base_url}\u002Fapi\u002Fv1\u002Fquery\"\n\n        async with session.get(url, params=params) as response:\n            data = await response.json()\n            metrics = []\n\n            if \"series\" in data:\n                for series in data[\"series\"]:\n                    for point in series[\"pointlist\"]:\n                        metrics.append(Metric(\n                            name=series[\"metric\"],\n                            value=point[1],\n                            timestamp=datetime.fromtimestamp(point[0]),\n                            labels={k: str(v) for k, v in series[\"tag_set\"].items()},\n                            source=\"datadog\",\n                            unit=series.get(\"unit\", \"\")\n                        ))\n\n            return metrics\n\n    async def query_logs(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime,\n        limit: int = 100\n    ) -> List[LogEntry]:\n        \"\"\"Query Datadog logs\"\"\"\n        session = await self._get_session()\n\n        payload = {\n            \"query\": query,\n            \"time\": {\n                \"from\": start_time.isoformat(),\n                \"to\": end_time.isoformat()\n            },\n            \"limit\": limit\n        }\n\n        url = f\"{self.base_url}\u002Fapi\u002Fv2\u002Flogs\u002Fevents\u002Fsearch\"\n\n        async with session.post(url, json=payload) as response:\n            data = await response.json()\n            logs = []\n\n            for log in data.get(\"data\", []):\n                attributes = log.get(\"attributes\", {})\n\n                logs.append(LogEntry(\n                    timestamp=datetime.fromisoformat(attributes[\"timestamp\"]),\n                    level=attributes.get(\"status\", \"info\"),\n                    message=attributes.get(\"message\", \"\"),\n                    service=attributes.get(\"service\", \"unknown\"),\n                    trace_id=attributes.get(\"trace_id\"),\n                    labels=attributes.get(\"tags\", [])\n                ))\n\n            return logs\n\n    async def get_trace(self, trace_id: str) -> Optional[Trace]:\n        \"\"\"Get trace from Datadog\"\"\"\n        session = await self._get_session()\n\n        url = f\"{self.base_url}\u002Fapi\u002Fv1\u002Ftrace\u002F{trace_id}\"\n\n        async with session.get(url) as response:\n            data = await response.json()\n\n            # Parse Datadog trace format\n            # Implementation depends on Datadog API response structure\n            return None\n```\n\n## 2. Machine Learning for Operations\n\n### Anomaly Detection Models\n\n```python\n# aiops\u002Fml\u002Fanomaly_detection.py\nimport numpy as np\nimport pandas as pd\nfrom typing import List, Dict, Tuple, Optional\nfrom datetime import datetime, timedelta\nfrom sklearn.ensemble import IsolationForest\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nimport joblib\nimport asyncio\nfrom collections import deque\n\nclass AnomalyDetector:\n    \"\"\"Generic anomaly detection for metrics and logs\"\"\"\n\n    def __init__(self, model_type: str = \"isolation_forest\"):\n        self.model_type = model_type\n        self.models: Dict[str, Any] = {}\n        self.scalers: Dict[str, StandardScaler] = {}\n        self.is_trained: Dict[str, bool] = {}\n        self.feature_history: Dict[str, deque] = {}\n\n    async def detect_metric_anomaly(\n        self,\n        metric_name: str,\n        current_value: float,\n        historical_values: List[float],\n        window_size: int = 100\n    ) -> Tuple[bool, float]:\n        \"\"\"Detect anomalies in time series metrics\"\"\"\n        if metric_name not in self.models:\n            await self._train_metric_model(metric_name, historical_values)\n\n        # Prepare features\n        features = self._extract_time_series_features(\n            current_value,\n            historical_values,\n            window_size\n        )\n\n        # Scale features\n        if metric_name in self.scalers:\n            features = self.scalers[metric_name].transform([features])\n\n        # Predict anomaly\n        model = self.models[metric_name]\n        anomaly_score = model.decision_function([features])[0]\n        is_anomaly = model.predict([features])[0] == -1\n\n        return is_anomaly, float(anomaly_score)\n\n    async def detect_log_anomaly(\n        self,\n        log_entries: List[Dict[str, Any]]\n    ) -> List[Dict[str, Any]]:\n        \"\"\"Detect anomalies in log patterns\"\"\"\n        # Extract log features\n        features = []\n        for entry in log_entries:\n            features.append([\n                len(entry.get(\"message\", \"\")),\n                entry.get(\"level_numeric\", 0),\n                entry.get(\"hour_of_day\", 0),\n                entry.get(\"day_of_week\", 0)\n            ])\n\n        features = np.array(features)\n\n        # Train or use existing model\n        model_name = \"log_patterns\"\n        if model_name not in self.models:\n            self.models[model_name] = IsolationForest(\n                contamination=0.1,\n                random_state=42\n            )\n            self.models[model_name].fit(features)\n\n        # Predict anomalies\n        predictions = self.models[model_name].predict(features)\n        scores = self.models[model_name].decision_function(features)\n\n        # Return anomalous logs\n        anomalies = []\n        for i, (pred, score) in enumerate(zip(predictions, scores)):\n            if pred == -1:  # Anomaly\n                anomalies.append({\n                    \"log_index\": i,\n                    \"log_entry\": log_entries[i],\n                    \"anomaly_score\": float(score),\n                    \"reason\": \"Unusual log pattern detected\"\n                })\n\n        return anomalies\n\n    async def detect_performance_anomaly(\n        self,\n        metrics: Dict[str, List[float]],\n        threshold: float = 0.1\n    ) -> Dict[str, Dict[str, Any]]:\n        \"\"\"Detect performance anomalies across multiple metrics\"\"\"\n        results = {}\n\n        for metric_name, values in metrics.items():\n            if len(values) \u003C 10:\n                continue\n\n            # Calculate statistical anomalies\n            mean_val = np.mean(values)\n            std_val = np.std(values)\n            recent_values = values[-5:]\n\n            for i, value in enumerate(recent_values):\n                z_score = abs(value - mean_val) \u002F (std_val + 1e-8)\n\n                if z_score > 3:  # Statistical outlier\n                    if metric_name not in results:\n                        results[metric_name] = {\n                            \"anomalies\": [],\n                            \"severity\": \"medium\"\n                        }\n\n                    results[metric_name][\"anomalies\"].append({\n                        \"value\": value,\n                        \"z_score\": z_score,\n                        \"index\": i,\n                        \"type\": \"statistical\"\n                    })\n\n        return results\n\n    async def _train_metric_model(\n        self,\n        metric_name: str,\n        values: List[float]\n    ) -> None:\n        \"\"\"Train anomaly detection model for a metric\"\"\"\n        if len(values) \u003C 30:\n            # Not enough data for training\n            return\n\n        # Prepare training data\n        training_features = []\n        for i in range(10, len(values)):\n            # Create window of previous values\n            window = values[i-10:i]\n            features = [\n                np.mean(window),\n                np.std(window),\n                values[i],  # Current value\n                (values[i] - np.mean(window)) \u002F (np.std(window) + 1e-8),  # Z-score\n                max(window) - min(window),  # Range\n            ]\n            training_features.append(features)\n\n        training_features = np.array(training_features)\n\n        # Scale features\n        scaler = StandardScaler()\n        scaled_features = scaler.fit_transform(training_features)\n        self.scalers[metric_name] = scaler\n\n        # Train model\n        if self.model_type == \"isolation_forest\":\n            model = IsolationForest(\n                contamination=0.05,\n                random_state=42\n            )\n            model.fit(scaled_features)\n        else:\n            # Add other model types as needed\n            model = IsolationForest(contamination=0.05, random_state=42)\n            model.fit(scaled_features)\n\n        self.models[metric_name] = model\n        self.is_trained[metric_name] = True\n\n    def _extract_time_series_features(\n        self,\n        current_value: float,\n        historical_values: List[float],\n        window_size: int\n    ) -> List[float]:\n        \"\"\"Extract features from time series data\"\"\"\n        if len(historical_values) \u003C window_size:\n            window = historical_values\n        else:\n            window = historical_values[-window_size:]\n\n        features = [\n            np.mean(window),\n            np.std(window),\n            min(window),\n            max(window),\n            current_value,\n            (current_value - np.mean(window)) \u002F (np.std(window) + 1e-8),\n            len([v for v in window if v > np.mean(window)]) \u002F len(window),  # Percentage above mean\n        ]\n\n        return features\n\nclass PredictiveAnalyzer:\n    \"\"\"Predictive analytics for capacity planning and incident prediction\"\"\"\n\n    def __init__(self):\n        self.models: Dict[str, Any] = {}\n\n    async def predict_capacity_needs(\n        self,\n        metric_history: Dict[str, List[float]],\n        forecast_horizon: int = 7\n    ) -> Dict[str, Dict[str, Any]]:\n        \"\"\"Predict future resource needs\"\"\"\n        predictions = {}\n\n        for metric_name, values in metric_history.items():\n            if len(values) \u003C 30:\n                continue\n\n            # Simple linear trend prediction (use more sophisticated models in production)\n            x = np.arange(len(values))\n            coeffs = np.polyfit(x, values, 1)\n\n            # Predict future values\n            future_x = np.arange(len(values), len(values) + forecast_horizon * 24)\n            future_values = np.polyval(coeffs, future_x)\n\n            # Calculate growth rate\n            growth_rate = (future_values[-1] - values[-1]) \u002F values[-1] * 100\n\n            predictions[metric_name] = {\n                \"current\": values[-1],\n                \"predicted_peak\": float(max(future_values)),\n                \"predicted_average\": float(np.mean(future_values)),\n                \"growth_rate\": growth_rate,\n                \"recommendation\": self._get_capacity_recommendation(growth_rate),\n                \"confidence\": 0.75  # Should be calculated based on model accuracy\n            }\n\n        return predictions\n\n    async def predict_incidents(\n        self,\n        alert_history: List[Dict[str, Any]],\n        system_metrics: Dict[str, List[float]]\n    ) -> Dict[str, Any]:\n        \"\"\"Predict likelihood of future incidents\"\"\"\n        # Feature engineering\n        features = self._extract_incident_features(alert_history, system_metrics)\n\n        # Simplified risk score calculation\n        risk_factors = {\n            \"error_rate_trend\": self._calculate_trend(\n                system_metrics.get(\"error_rate\", [])\n            ),\n            \"cpu_utilization_avg\": np.mean(\n                system_metrics.get(\"cpu_utilization\", [0])\n            ),\n            \"memory_utilization_avg\": np.mean(\n                system_metrics.get(\"memory_utilization\", [0])\n            ),\n            \"recent_incident_count\": len([\n                a for a in alert_history[-24:]\n                if a.get(\"severity\") == \"critical\"\n            ]),\n        }\n\n        # Calculate risk score (0-100)\n        risk_score = min(100, max(0, (\n            risk_factors[\"error_rate_trend\"] * 30 +\n            risk_factors[\"cpu_utilization_avg\"] * 25 +\n            risk_factors[\"memory_utilization_avg\"] * 25 +\n            risk_factors[\"recent_incident_count\"] * 20\n        )))\n\n        return {\n            \"risk_score\": risk_score,\n            \"risk_level\": self._get_risk_level(risk_score),\n            \"contributing_factors\": risk_factors,\n            \"recommendations\": self._get_mitigation_recommendations(risk_factors)\n        }\n\n    def _extract_incident_features(\n        self,\n        alert_history: List[Dict[str, Any]],\n        system_metrics: Dict[str, List[float]]\n    ) -> List[float]:\n        \"\"\"Extract features for incident prediction\"\"\"\n        features = []\n\n        # Alert patterns\n        recent_alerts = alert_history[-24:] if len(alert_history) >= 24 else alert_history\n        features.append(len(recent_alerts))\n        features.append(len([a for a in recent_alerts if a.get(\"severity\") == \"critical\"]))\n\n        # System metrics\n        for metric_name in [\"cpu\", \"memory\", \"disk\", \"network\"]:\n            values = system_metrics.get(f\"{metric_name}_utilization\", [0])\n            features.append(np.mean(values[-12:]) if values else 0)\n            features.append(np.std(values[-12:]) if values else 0)\n\n        return features\n\n    def _calculate_trend(self, values: List[float]) -> float:\n        \"\"\"Calculate trend coefficient (positive = increasing)\"\"\"\n        if len(values) \u003C 2:\n            return 0.0\n\n        x = np.arange(len(values))\n        coeffs = np.polyfit(x, values, 1)\n        return coeffs[0]\n\n    def _get_capacity_recommendation(self, growth_rate: float) -> str:\n        \"\"\"Get capacity recommendation based on growth rate\"\"\"\n        if growth_rate > 50:\n            return \"Immediate scaling required - consider 2x current capacity\"\n        elif growth_rate > 20:\n            return \"Plan for scaling within next week - recommend 1.5x capacity\"\n        elif growth_rate > 0:\n            return \"Monitor growth - consider 25% buffer\"\n        else:\n            return \"Capacity adequate - no immediate action needed\"\n\n    def _get_risk_level(self, risk_score: float) -> str:\n        \"\"\"Get risk level from risk score\"\"\"\n        if risk_score >= 80:\n            return \"critical\"\n        elif risk_score >= 60:\n            return \"high\"\n        elif risk_score >= 40:\n            return \"medium\"\n        else:\n            return \"low\"\n\n    def _get_mitigation_recommendations(\n        self,\n        risk_factors: Dict[str, float]\n    ) -> List[str]:\n        \"\"\"Get mitigation recommendations based on risk factors\"\"\"\n        recommendations = []\n\n        if risk_factors[\"cpu_utilization_avg\"] > 80:\n            recommendations.append(\"Scale CPU resources or optimize CPU usage\")\n\n        if risk_factors[\"memory_utilization_avg\"] > 80:\n            recommendations.append(\"Increase memory allocation or check for memory leaks\")\n\n        if risk_factors[\"error_rate_trend\"] > 0:\n            recommendations.append(\"Investigate and fix increasing error rate\")\n\n        if risk_factors[\"recent_incident_count\"] > 3:\n            recommendations.append(\"Review recent incidents for root cause analysis\")\n\n        return recommendations\n\nclass AILogAnalyzer:\n    \"\"\"AI-powered log analysis and pattern detection\"\"\"\n\n    def __init__(self):\n        self.patterns: Dict[str, List[str]] = {}\n        self.embeddings: Dict[str, np.ndarray] = {}\n\n    async def analyze_logs(\n        self,\n        logs: List[LogEntry]\n    ) -> Dict[str, Any]:\n        \"\"\"Analyze logs for patterns and anomalies\"\"\"\n        analysis = {\n            \"total_logs\": len(logs),\n            \"error_rate\": 0.0,\n            \"patterns\": [],\n            \"anomalies\": [],\n            \"trending_topics\": [],\n            \"recommendations\": []\n        }\n\n        # Calculate error rate\n        error_count = len([l for l in logs if l.level.lower() in [\"error\", \"critical\"]])\n        analysis[\"error_rate\"] = error_count \u002F len(logs) * 100\n\n        # Extract patterns using regex and ML\n        analysis[\"patterns\"] = await self._extract_patterns(logs)\n\n        # Find anomalies\n        analysis[\"anomalies\"] = await self._detect_log_anomalies(logs)\n\n        # Identify trending topics\u002Ferrors\n        analysis[\"trending_topics\"] = await self._identify_trending_topics(logs)\n\n        # Generate recommendations\n        analysis[\"recommendations\"] = self._generate_log_recommendations(analysis)\n\n        return analysis\n\n    async def _extract_patterns(self, logs: List[LogEntry]) -> List[Dict[str, Any]]:\n        \"\"\"Extract common patterns from logs\"\"\"\n        # Simplified pattern extraction\n        # In production, use more sophisticated NLP techniques\n        patterns = []\n\n        # Group logs by service\n        service_logs = {}\n        for log in logs:\n            if log.service not in service_logs:\n                service_logs[log.service] = []\n            service_logs[log.service].append(log)\n\n        # Find error patterns\n        for service, service_log_list in service_logs.items():\n            error_logs = [l for l in service_log_list if l.level.lower() == \"error\"]\n            if len(error_logs) > 0:\n                # Find common substrings in error messages\n                common_errors = {}\n                for log in error_logs:\n                    # Simplified - use actual pattern matching in production\n                    for word in log.message.split():\n                        if len(word) > 5:  # Only consider longer words\n                            common_errors[word] = common_errors.get(word, 0) + 1\n\n                if common_errors:\n                    most_common = max(common_errors, key=common_errors.get)\n                    patterns.append({\n                        \"service\": service,\n                        \"type\": \"error_pattern\",\n                        \"pattern\": most_common,\n                        \"count\": common_errors[most_common],\n                        \"percentage\": common_errors[most_common] \u002F len(error_logs) * 100\n                    })\n\n        return patterns\n\n    async def _detect_log_anomalies(self, logs: List[LogEntry]) -> List[Dict[str, Any]]:\n        \"\"\"Detect anomalies in logs\"\"\"\n        anomalies = []\n\n        # Check for unusual error spikes\n        error_by_minute = {}\n        for log in logs:\n            if log.level.lower() in [\"error\", \"critical\"]:\n                minute = log.timestamp.replace(second=0, microsecond=0)\n                error_by_minute[minute] = error_by_minute.get(minute, 0) + 1\n\n        if error_by_minute:\n            avg_errors = np.mean(list(error_by_minute.values()))\n            for minute, count in error_by_minute.items():\n                if count > avg_errors * 3:  # 3x spike\n                    anomalies.append({\n                        \"type\": \"error_spike\",\n                        \"timestamp\": minute.isoformat(),\n                        \"value\": count,\n                        \"baseline\": avg_errors,\n                        \"severity\": \"high\"\n                    })\n\n        return anomalies\n\n    async def _identify_trending_topics(self, logs: List[LogEntry]) -> List[Dict[str, Any]]:\n        \"\"\"Identify trending topics in logs\"\"\"\n        # Simplified trending detection\n        word_count = {}\n        for log in logs[-100:]:  # Last 100 logs\n            for word in log.message.lower().split():\n                if len(word) > 5 and word.isalpha():\n                    word_count[word] = word_count.get(word, 0) + 1\n\n        # Get top trending words\n        trending = sorted(word_count.items(), key=lambda x: x[1], reverse=True)[:10]\n\n        return [\n            {\"topic\": word, \"count\": count}\n            for word, count in trending\n        ]\n\n    def _generate_log_recommendations(self, analysis: Dict[str, Any]) -> List[str]:\n        \"\"\"Generate recommendations based on log analysis\"\"\"\n        recommendations = []\n\n        if analysis[\"error_rate\"] > 5:\n            recommendations.append(\n                f\"High error rate ({analysis['error_rate']:.1f}%) detected - investigate immediately\"\n            )\n\n        if len(analysis[\"anomalies\"]) > 0:\n            recommendations.append(\n                f\"Found {len(analysis['anomalies'])} anomalies - review and address\"\n            )\n\n        for pattern in analysis[\"patterns\"][:3]:\n            if pattern[\"percentage\"] > 30:\n                recommendations.append(\n                    f\"Common error pattern in {pattern['service']}: {pattern['pattern']}\"\n                )\n\n        return recommendations\n```\n\n## 3. Automation and Self-Healing\n\n### Automation Engine\n\n```python\n# aiops\u002Fautomation\u002Fengine.py\nimport asyncio\nimport subprocess\nfrom typing import Dict, List, Any, Optional, Callable\nfrom dataclasses import dataclass\nfrom enum import Enum\nimport logging\nfrom datetime import datetime, timedelta\n\nlogger = logging.getLogger(__name__)\n\nclass ActionType(str, Enum):\n    \"\"\"Types of automation actions\"\"\"\n    SCALE = \"scale\"\n    RESTART = \"restart\"\n    PATCH = \"patch\"\n    NOTIFY = \"notify\"\n    SUPPRESS = \"suppress\"\n    ISOLATE = \"isolate\"\n    FAILBACK = \"failback\"\n\n@dataclass\nclass Action:\n    \"\"\"Automation action definition\"\"\"\n    type: ActionType\n    target: str\n    params: Dict[str, Any]\n    conditions: List[str] = None\n    timeout: int = 300\n    retries: int = 3\n\nclass AutomationEngine:\n    \"\"\"Automation engine for executing remediation actions\"\"\"\n\n    def __init__(self):\n        self.actions: Dict[str, Action] = {}\n        self.execution_history: List[Dict[str, Any]] = []\n        self.action_handlers: Dict[ActionType, Callable] = {\n            ActionType.SCALE: self._handle_scale,\n            ActionType.RESTART: self._handle_restart,\n            ActionType.PATCH: self._handle_patch,\n            ActionType.NOTIFY: self._handle_notify,\n            ActionType.SUPPRESS: self._handle_suppress,\n            ActionType.ISOLATE: self._handle_isolate,\n            ActionType.FAILBACK: self._handle_failback\n        }\n\n    def register_action(self, action: Action) -> None:\n        \"\"\"Register an automation action\"\"\"\n        self.actions[action.target] = action\n        logger.info(f\"Registered action: {action.type} for {action.target}\")\n\n    async def execute(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Execute an automation action\"\"\"\n        execution_id = f\"{action.type}_{action.target}_{int(datetime.utcnow().timestamp())}\"\n\n        logger.info(f\"Executing action {execution_id}: {action.type} on {action.target}\")\n\n        execution = {\n            \"id\": execution_id,\n            \"action\": action,\n            \"status\": \"running\",\n            \"started_at\": datetime.utcnow(),\n            \"result\": None,\n            \"error\": None\n        }\n\n        try:\n            # Check conditions\n            if action.conditions and not await self._check_conditions(action.conditions):\n                execution[\"status\"] = \"skipped\"\n                execution[\"error\"] = \"Conditions not met\"\n                return execution\n\n            # Execute action with retries\n            result = await self._execute_with_retries(action)\n\n            execution[\"status\"] = \"success\"\n            execution[\"result\"] = result\n            logger.info(f\"Action {execution_id} completed successfully\")\n\n        except Exception as e:\n            execution[\"status\"] = \"failed\"\n            execution[\"error\"] = str(e)\n            logger.error(f\"Action {execution_id} failed: {e}\")\n\n        finally:\n            execution[\"completed_at\"] = datetime.utcnow()\n            execution[\"duration\"] = (execution[\"completed_at\"] - execution[\"started_at\"]).total_seconds()\n            self.execution_history.append(execution)\n\n        return execution\n\n    async def _execute_with_retries(self, action: Action) -> Any:\n        \"\"\"Execute action with retry logic\"\"\"\n        last_exception = None\n\n        for attempt in range(action.retries + 1):\n            try:\n                handler = self.action_handlers.get(action.type)\n                if not handler:\n                    raise ValueError(f\"No handler for action type: {action.type}\")\n\n                # Execute with timeout\n                return await asyncio.wait_for(\n                    handler(action),\n                    timeout=action.timeout\n                )\n\n            except asyncio.TimeoutError:\n                last_exception = TimeoutError(f\"Action timed out after {action.timeout}s\")\n                if attempt \u003C action.retries:\n                    await asyncio.sleep(2 ** attempt)  # Exponential backoff\n                    continue\n                raise last_exception\n\n            except Exception as e:\n                last_exception = e\n                if attempt \u003C action.retries:\n                    await asyncio.sleep(2 ** attempt)  # Exponential backoff\n                    continue\n                raise last_exception\n\n    async def _check_conditions(self, conditions: List[str]) -> bool:\n        \"\"\"Check if action conditions are met\"\"\"\n        # Implement condition checking logic\n        # For example, check cluster health, time windows, etc.\n        return True\n\n    async def _handle_scale(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle scale action\"\"\"\n        params = action.params\n        command = [\n            \"kubectl\", \"scale\",\n            f\"--replicas={params.get('replicas', '+1')}\",\n            f\"deployment\u002F{action.target}\"\n        ]\n\n        if \"namespace\" in params:\n            command.insert(1, f\"-n={params['namespace']}\")\n\n        result = await self._run_command(command)\n        return {\"scaled\": True, \"result\": result}\n\n    async def _handle_restart(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle restart action\"\"\"\n        params = action.params\n        command = [\"kubectl\", \"rollout\", \"restart\", f\"deployment\u002F{action.target}\"]\n\n        if \"namespace\" in params:\n            command.insert(2, f\"-n={params['namespace']}\")\n\n        result = await self._run_command(command)\n        return {\"restarted\": True, \"result\": result}\n\n    async def _handle_patch(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle patch action\"\"\"\n        params = action.params\n\n        # Build patch command\n        command = [\"kubectl\", \"patch\"]\n\n        if \"namespace\" in params:\n            command.extend([\"-n\", params[\"namespace\"]])\n\n        resource_type = params.get(\"type\", \"deployment\")\n        command.extend([f\"{resource_type}\u002F{action.target}\"])\n\n        patch_type = params.get(\"patch_type\", \"strategic\")\n        command.extend([\"--type\", patch_type])\n\n        patch_data = params.get(\"patch\", {})\n        command.extend([\"-p\", json.dumps(patch_data)])\n\n        result = await self._run_command(command)\n        return {\"patched\": True, \"result\": result}\n\n    async def _handle_notify(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle notification action\"\"\"\n        params = action.params\n        channel = params.get(\"channel\", \"default\")\n        message = params.get(\"message\", f\"AAction executed: {action.type} on {action.target}\")\n\n        # Implement notification logic (Slack, Teams, Email, etc.)\n        # This is a placeholder for actual notification implementation\n        notification = {\n            \"channel\": channel,\n            \"message\": message,\n            \"timestamp\": datetime.utcnow().isoformat(),\n            \"sent\": True\n        }\n\n        # Example: Send to Slack\n        if channel == \"slack\":\n            webhook_url = params.get(\"webhook_url\")\n            if webhook_url:\n                slack_payload = {\n                    \"text\": message,\n                    \"attachments\": [{\n                        \"color\": \"warning\",\n                        \"fields\": [{\n                            \"title\": \"Action\",\n                            \"value\": f\"{action.type} on {action.target}\",\n                            \"short\": True\n                        }, {\n                            \"title\": \"Timestamp\",\n                            \"value\": datetime.utcnow().strftime(\"%Y-%m-%d %H:%M:%S\"),\n                            \"short\": True\n                        }]\n                    }]\n                }\n\n                async with aiohttp.ClientSession() as session:\n                    async with session.post(webhook_url, json=slack_payload) as response:\n                        if response.status != 200:\n                            raise Exception(f\"Slack notification failed: {response.status}\")\n\n        return notification\n\n    async def _handle_suppress(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle alert suppression\"\"\"\n        params = action.params\n        duration = params.get(\"duration\", \"1h\")\n        reason = params.get(\"reason\", \"Automated suppression\")\n\n        # Implement alert suppression logic\n        # This depends on your alerting system (Prometheus, AlertManager, etc.)\n        suppression = {\n            \"target\": action.target,\n            \"duration\": duration,\n            \"reason\": reason,\n            \"timestamp\": datetime.utcnow().isoformat()\n        }\n\n        return {\"suppressed\": True, \"suppression\": suppression}\n\n    async def _handle_isolate(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle service isolation\"\"\"\n        params = action.params\n        namespace = params.get(\"namespace\", \"default\")\n\n        # Implement isolation logic\n        # For example, update service to point to maintenance page\n        command = [\n            \"kubectl\", \"patch\", \"service\", action.target,\n            \"-n\", namespace,\n            \"-p\", '{\"spec\": {\"selector\": {\"app\": \"maintenance\"}}}'\n        ]\n\n        result = await self._run_command(command)\n        return {\"isolated\": True, \"result\": result}\n\n    async def _handle_failback(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle failback to primary\"\"\"\n        params = action.params\n        namespace = params.get(\"namespace\", \"default\")\n\n        # Implement failback logic\n        # Restore traffic to primary service\n        command = [\n            \"kubectl\", \"patch\", \"service\", action.target,\n            \"-n\", namespace,\n            \"-p\", '{\"spec\": {\"selector\": {\"app\": \"' + action.target + '\"}}}'\n        ]\n\n        result = await self._run_command(command)\n        return {\"failed_back\": True, \"result\": result}\n\n    async def _run_command(self, command: List[str]) -> str:\n        \"\"\"Run shell command\"\"\"\n        process = await asyncio.create_subprocess_exec(\n            *command,\n            stdout=asyncio.subprocess.PIPE,\n            stderr=asyncio.subprocess.PIPE\n        )\n\n        stdout, stderr = await process.communicate()\n\n        if process.returncode != 0:\n            raise Exception(f\"Command failed: {stderr.decode()}\")\n\n        return stdout.decode()\n\nclass SelfHealingManager:\n    \"\"\"Manages self-healing policies and execution\"\"\"\n\n    def __init__(self, automation_engine: AutomationEngine):\n        self.automation_engine = automation_engine\n        self.healing_policies: Dict[str, Dict[str, Any]] = {}\n        self.active_healings: Dict[str, Dict[str, Any]] = {}\n\n    def register_healing_policy(\n        self,\n        service_name: str,\n        policy: Dict[str, Any]\n    ) -> None:\n        \"\"\"Register a self-healing policy for a service\"\"\"\n        self.healing_policies[service_name] = policy\n        logger.info(f\"Registered healing policy for {service_name}\")\n\n    async def trigger_healing(\n        self,\n        service_name: str,\n        incident: Dict[str, Any]\n    ) -> Dict[str, Any]:\n        \"\"\"Trigger self-healing for a service\"\"\"\n        if service_name not in self.healing_policies:\n            return {\"error\": \"No healing policy registered\"}\n\n        policy = self.healing_policies[service_name]\n        healing_id = f\"healing_{service_name}_{int(datetime.utcnow().timestamp())}\"\n\n        logger.info(f\"Triggering self-healing {healing_id} for {service_name}\")\n\n        healing_record = {\n            \"id\": healing_id,\n            \"service\": service_name,\n            \"incident\": incident,\n            \"policy\": policy,\n            \"status\": \"running\",\n            \"actions\": [],\n            \"started_at\": datetime.utcnow()\n        }\n\n        self.active_healings[healing_id] = healing_record\n\n        try:\n            # Execute healing actions based on policy\n            for action_def in policy.get(\"actions\", []):\n                action = Action(\n                    type=ActionType(action_def[\"type\"]),\n                    target=action_def.get(\"target\", service_name),\n                    params=action_def.get(\"params\", {}),\n                    timeout=action_def.get(\"timeout\", 300)\n                )\n\n                result = await self.automation_engine.execute(action)\n                healing_record[\"actions\"].append({\n                    \"action\": action_def,\n                    \"result\": result\n                })\n\n                # Check if healing was successful\n                if result[\"status\"] != \"success\":\n                    healing_record[\"status\"] = \"failed\"\n                    break\n            else:\n                healing_record[\"status\"] = \"success\"\n\n        except Exception as e:\n            healing_record[\"status\"] = \"error\"\n            healing_record[\"error\"] = str(e)\n            logger.error(f\"Self-healing failed: {e}\")\n\n        finally:\n            healing_record[\"completed_at\"] = datetime.utcnow()\n            healing_record[\"duration\"] = (\n                healing_record[\"completed_at\"] - healing_record[\"started_at\"]\n            ).total_seconds()\n\n        return healing_record\n\n    async def check_healing_status(self, healing_id: str) -> Optional[Dict[str, Any]]:\n        \"\"\"Get status of an active healing process\"\"\"\n        return self.active_healings.get(healing_id)\n```\n\n## 4. Observability and Monitoring Best Practices\n\n### Unified Observability Platform\n\n```python\n# aiops\u002Fobservability\u002Fplatform.py\nimport asyncio\nfrom typing import Dict, List, Any, Optional\nfrom datetime import datetime, timedelta\nfrom dataclasses import dataclass, field\nimport json\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n@dataclass\nclass ServiceHealth:\n    \"\"\"Service health status\"\"\"\n    service_name: str\n    status: str  # healthy, degraded, unhealthy\n    latency_p99: float\n    error_rate: float\n    throughput: float\n    last_check: datetime\n    dependencies: List[str] = field(default_factory=list)\n    sla_status: str = \"within_sla\"\n\nclass ObservabilityPlatform:\n    \"\"\"Unified observability platform for AIOps\"\"\"\n\n    def __init__(self, data_sources: List[DataSource]):\n        self.data_sources = data_sources\n        self.service_health: Dict[str, ServiceHealth] = {}\n        self.slo_compliance: Dict[str, Dict[str, Any]] = {}\n        self.dashboard_configs: Dict[str, Dict[str, Any]] = {}\n\n    async def get_service_overview(self) -> Dict[str, Any]:\n        \"\"\"Get comprehensive service overview\"\"\"\n        overview = {\n            \"total_services\": len(self.service_health),\n            \"healthy_services\": 0,\n            \"degraded_services\": 0,\n            \"unhealthy_services\": 0,\n            \"services\": {}\n        }\n\n        for service_name, health in self.service_health.items():\n            overview[\"services\"][service_name] = {\n                \"status\": health.status,\n                \"latency_p99\": health.latency_p99,\n                \"error_rate\": health.error_rate,\n                \"throughput\": health.throughput,\n                \"sla_status\": health.sla_status\n            }\n\n            if health.status == \"healthy\":\n                overview[\"healthy_services\"] += 1\n            elif health.status == \"degraded\":\n                overview[\"degraded_services\"] += 1\n            else:\n                overview[\"unhealthy_services\"] += 1\n\n        return overview\n\n    async def check_slo_compliance(\n        self,\n        service_name: str,\n        slo_config: Dict[str, Any]\n    ) -> Dict[str, Any]:\n        \"\"\"Check SLO compliance for a service\"\"\"\n        now = datetime.utcnow()\n        window = timedelta(days=30)  # 30-day rolling window\n\n        # Collect metrics for SLO calculation\n        metrics = []\n        for source in self.data_sources:\n            try:\n                source_metrics = await source.collect_metrics(\n                    query=slo_config[\"metric_query\"],\n                    start_time=now - window,\n                    end_time=now\n                )\n                metrics.extend(source_metrics)\n            except Exception as e:\n                logger.error(f\"Failed to collect metrics from {type(source).__name__}: {e}\")\n\n        if not metrics:\n            return {\"error\": \"No metrics available\"}\n\n        # Calculate SLO compliance\n        total_requests = sum(m.value for m in metrics if m.name.endswith(\"_total\"))\n        good_requests = sum(m.value for m in metrics if m.name.endswith(\"_success\"))\n\n        if total_requests == 0:\n            return {\"error\": \"No requests in time window\"}\n\n        actual_availability = (good_requests \u002F total_requests) * 100\n        target_availability = slo_config[\"target\"]\n\n        compliance = {\n            \"service\": service_name,\n            \"slo_name\": slo_config[\"name\"],\n            \"target_availability\": target_availability,\n            \"actual_availability\": actual_availability,\n            \"compliance\": actual_availability >= target_availability,\n            \"error_budget\": target_availability - actual_availability,\n            \"time_window\": window.days,\n            \"total_requests\": total_requests,\n            \"good_requests\": good_requests,\n            \"calculated_at\": now.isoformat()\n        }\n\n        self.slo_compliance[f\"{service_name}_{slo_config['name']}\"] = compliance\n        return compliance\n\n    async def generate_incident_report(\n        self,\n        incident_id: str,\n        start_time: datetime,\n        end_time: Optional[datetime] = None\n    ) -> Dict[str, Any]:\n        \"\"\"Generate comprehensive incident report\"\"\"\n        if not end_time:\n            end_time = datetime.utcnow()\n\n        report = {\n            \"incident_id\": incident_id,\n            \"start_time\": start_time.isoformat(),\n            \"end_time\": end_time.isoformat(),\n            \"duration_hours\": (end_time - start_time).total_seconds() \u002F 3600,\n            \"affected_services\": [],\n            \"metrics_impact\": {},\n            \"error_rate_impact\": {},\n            \"user_impact\": {},\n            \"timeline\": [],\n            \"root_cause_analysis\": {},\n            \"action_items\": []\n        }\n\n        # Collect data for affected services\n        for service_name, health in self.service_health.items():\n            if health.last_check >= start_time and health.last_check \u003C= end_time:\n                report[\"affected_services\"].append(service_name)\n\n                # Collect metrics impact\n                for source in self.data_sources:\n                    try:\n                        metrics = await source.collect_metrics(\n                            query=f\"rate(http_requests_total[5m])\",\n                            start_time=start_time,\n                            end_time=end_time\n                        )\n                        if metrics:\n                            report[\"metrics_impact\"][service_name] = [\n                                {\"timestamp\": m.timestamp.isoformat(), \"value\": m.value}\n                                for m in metrics\n                            ]\n                    except Exception as e:\n                        logger.error(f\"Failed to collect metrics for {service_name}: {e}\")\n\n        return report\n\n    async def create_dashboard(\n        self,\n        dashboard_name: str,\n        config: Dict[str, Any]\n    ) -> Dict[str, Any]:\n        \"\"\"Create a new observability dashboard\"\"\"\n        dashboard = {\n            \"name\": dashboard_name,\n            \"description\": config.get(\"description\", \"\"),\n            \"panels\": config.get(\"panels\", []),\n            \"time_range\": config.get(\"time_range\", \"1h\"),\n            \"refresh_interval\": config.get(\"refresh_interval\", \"5m\"),\n            \"created_at\": datetime.utcnow().isoformat()\n        }\n\n        self.dashboard_configs[dashboard_name] = dashboard\n        return dashboard\n\n    async def get_dashboard_data(\n        self,\n        dashboard_name: str,\n        time_range: Optional[str] = None\n    ) -> Dict[str, Any]:\n        \"\"\"Get data for a specific dashboard\"\"\"\n        if dashboard_name not in self.dashboard_configs:\n            return {\"error\": \"Dashboard not found\"}\n\n        dashboard = self.dashboard_configs[dashboard_name]\n        now = datetime.utcnow()\n\n        # Parse time range\n        if time_range:\n            end_time = now\n            if time_range.endswith(\"h\"):\n                start_time = now - timedelta(hours=int(time_range[:-1]))\n            elif time_range.endswith(\"d\"):\n                start_time = now - timedelta(days=int(time_range[:-1]))\n            else:\n                start_time = now - timedelta(hours=1)\n        else:\n            start_time = now - timedelta(hours=1)\n            end_time = now\n\n        dashboard_data = {\n            \"name\": dashboard_name,\n            \"time_range\": {\"start\": start_time.isoformat(), \"end\": end_time.isoformat()},\n            \"panels\": []\n        }\n\n        # Collect data for each panel\n        for panel in dashboard[\"panels\"]:\n            panel_data = {\n                \"title\": panel[\"title\"],\n                \"type\": panel[\"type\"],\n                \"data\": []\n            }\n\n            try:\n                # Collect metrics based on panel query\n                for source in self.data_sources:\n                    metrics = await source.collect_metrics(\n                        query=panel[\"query\"],\n                        start_time=start_time,\n                        end_time=end_time\n                    )\n                    panel_data[\"data\"].extend([\n                        {\"timestamp\": m.timestamp.isoformat(), \"value\": m.value}\n                        for m in metrics\n                    ])\n            except Exception as e:\n                panel_data[\"error\"] = str(e)\n\n            dashboard_data[\"panels\"].append(panel_data)\n\n        return dashboard_data\n\n# SLO and SLA Management\nclass SLOManager:\n    \"\"\"Manages Service Level Objectives and Service Level Agreements\"\"\"\n\n    def __init__(self):\n        self.slos: Dict[str, Dict[str, Any]] = {}\n        self.slas: Dict[str, Dict[str, Any]] = {}\n\n    def define_slo(\n        self,\n        service_name: str,\n        slo_name: str,\n        target: float,\n        time_window: str,\n        alerting_burn_rate: float = 2.0\n    ) -> Dict[str, Any]:\n        \"\"\"Define a Service Level Objective\"\"\"\n        slo = {\n            \"service_name\": service_name,\n            \"slo_name\": slo_name,\n            \"target\": target,\n            \"time_window\": time_window,\n            \"alerting_burn_rate\": alerting_burn_rate,\n            \"created_at\": datetime.utcnow().isoformat(),\n            \"status\": \"active\"\n        }\n\n        self.slos[f\"{service_name}_{slo_name}\"] = slo\n        return slo\n\n    def define_sla(\n        self,\n        service_name: str,\n        sla_name: str,\n        availability_target: float,\n        performance_targets: Dict[str, float],\n        penalties: Dict[str, Any] = None\n    ) -> Dict[str, Any]:\n        \"\"\"Define a Service Level Agreement\"\"\"\n        sla = {\n            \"service_name\": service_name,\n            \"sla_name\": sla_name,\n            \"availability_target\": availability_target,\n            \"performance_targets\": performance_targets,\n            \"penalties\": penalties or {},\n            \"created_at\": datetime.utcnow().isoformat(),\n            \"status\": \"active\"\n        }\n\n        self.slas[f\"{service_name}_{sla_name}\"] = sla\n        return sla\n\n    def calculate_error_budget(\n        self,\n        service_name: str,\n        slo_name: str,\n        period_days: int = 30\n    ) -> Dict[str, Any]:\n        \"\"\"Calculate error budget for an SLO\"\"\"\n        slo_key = f\"{service_name}_{slo_name}\"\n        if slo_key not in self.slos:\n            return {\"error\": \"SLO not found\"}\n\n        slo = self.slos[slo_key]\n        target_availability = slo[\"target\"]\n\n        # Calculate error budget\n        period_hours = period_days * 24\n        error_budget = (100 - target_availability) \u002F 100 * period_hours\n\n        return {\n            \"service\": service_name,\n            \"slo\": slo_name,\n            \"period_days\": period_days,\n            \"target_availability\": target_availability,\n            \"error_budget_hours\": error_budget,\n            \"error_budget_percentage\": 100 - target_availability,\n            \"remaining_error_budget\": error_budget,  # Would be calculated from actual metrics\n            \"burn_rate\": 0.0  # Would be calculated from actual metrics\n        }\n```\n\nThis comprehensive AIOps skill provides production-ready patterns for implementing AI-powered operations in 2025, including intelligent monitoring, anomaly detection, predictive analytics, automation, and self-healing capabilities.\n\n`★ Insight ─────────────────────────────────────`\nThe AIOps patterns shown here emphasize several key 2025 best practices:\n1. **Data Source Abstraction**: Generic interfaces allow integration with any monitoring platform (Prometheus, Datadog, New Relic, etc.)\n2. **ML-Driven Operations**: Anomaly detection, predictive analytics, and pattern recognition using proven ML models\n3. **Automation with Safety**: Retry logic, timeout handling, and condition checking ensure safe automated remediation\n4. **Observability First**: Unified platform approach to metrics, logs, and traces for comprehensive visibility\n5. **SLO-Driven**: Focus on business outcomes through Service Level Objectives and error budget management\n\nThese patterns ensure your AIOps implementation is intelligent, automated, and aligned with business objectives.\n`─────────────────────────────────────────────────`","# AIOps - AI for IT Operations\n\nThis skill provides comprehensive patterns for implementing AIOps strategies in 2025, including intelligent monitoring, automated incident response, predictive analytics, and observability best practices. The patterns are designed to be framework-agnostic and applicable across different infrastructure platforms.\n\n## When to Use This Skill\n\nUse this skill when you need to:\n- Implement AIOps strategies for modern infrastructure\n- Build intelligent monitoring and alerting systems\n- Create automated incident response workflows\n- Deploy predictive maintenance solutions\n- Implement self-healing capabilities\n- Build observability platforms with AI\u002FML\n- Optimize multi-cloud operations\n- Create chaos engineering practices\n- Implement generative AI for operations\n- Build digital twins for infrastructure\n\n## 1. AIOps Architecture Patterns\n\n### Core AIOps Platform Architecture\n\n```python\n# aiops\u002Fcore\u002Farchitecture.py\nfrom abc import ABC, abstractmethod\nfrom typing import Dict, List, Optional, Any, Union\nfrom dataclasses import dataclass, field\nfrom datetime import datetime, timedelta\nfrom enum import Enum\nimport asyncio\nimport json\nimport logging\n\nlogger = logging.getLogger(__name__)\n\nclass AlertSeverity(str, Enum):\n    \"\"\"Alert severity levels\"\"\"\n    CRITICAL = \"critical\"\n    HIGH = \"high\"\n    MEDIUM = \"medium\"\n    LOW = \"low\"\n    INFO = \"info\"\n\nclass IncidentStatus(str, Enum):\n    \"\"\"Incident status\"\"\"\n    OPEN = \"open\"\n    INVESTIGATING = \"investigating\"\n    IDENTIFIED = \"identified\"\n    MONITORING = \"monitoring\"\n    RESOLVED = \"resolved\"\n    CLOSED = \"closed\"\n\n@dataclass\nclass Metric:\n    \"\"\"Metric data point\"\"\"\n    name: str\n    value: float\n    timestamp: datetime\n    labels: Dict[str, str] = field(default_factory=dict)\n    source: str = \"\"\n    unit: str = \"\"\n\n@dataclass\nclass LogEntry:\n    \"\"\"Structured log entry\"\"\"\n    timestamp: datetime\n    level: str\n    message: str\n    service: str\n    trace_id: Optional[str] = None\n    span_id: Optional[str] = None\n    labels: Dict[str, str] = field(default_factory=dict)\n    stack_trace: Optional[str] = None\n\n@dataclass\nclass Trace:\n    \"\"\"Distributed trace data\"\"\"\n    trace_id: str\n    spans: List[Dict[str, Any]]\n    duration: timedelta\n    service_map: Dict[str, List[str]]\n    error_count: int = 0\n\n@dataclass\nclass Alert:\n    \"\"\"Alert representation\"\"\"\n    id: str\n    name: str\n    severity: AlertSeverity\n    status: IncidentStatus\n    description: str\n    source: str\n    timestamp: datetime\n    labels: Dict[str, str] = field(default_factory=dict)\n    annotations: Dict[str, str] = field(default_factory=dict)\n    fingerprint: str = \"\"\n    related_entities: List[str] = field(default_factory=list)\n\nclass DataSource(ABC):\n    \"\"\"Abstract data source interface\"\"\"\n\n    @abstractmethod\n    async def collect_metrics(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime\n    ) -> List[Metric]:\n        \"\"\"Collect metrics from data source\"\"\"\n        pass\n\n    @abstractmethod\n    async def query_logs(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime,\n        limit: int = 100\n    ) -> List[LogEntry]:\n        \"\"\"Query logs from data source\"\"\"\n        pass\n\n    @abstractmethod\n    async def get_trace(self, trace_id: str) -> Optional[Trace]:\n        \"\"\"Get trace data\"\"\"\n        pass\n\nclass AIOpsEngine:\n    \"\"\"Core AIOps processing engine\"\"\"\n\n    def __init__(self):\n        self.data_sources: List[DataSource] = []\n        self.alert_processors: List[AlertProcessor] = []\n        self.ml_models: Dict[str, Any] = {}\n        self.knowledge_base: KnowledgeBase = KnowledgeBase()\n        self.automation_engine = AutomationEngine()\n\n    def register_data_source(self, data_source: DataSource) -> None:\n        \"\"\"Register a data source\"\"\"\n        self.data_sources.append(data_source)\n        logger.info(f\"Registered data source: {type(data_source).__name__}\")\n\n    def register_alert_processor(self, processor: 'AlertProcessor') -> None:\n        \"\"\"Register an alert processor\"\"\"\n        self.alert_processors.append(processor)\n        logger.info(f\"Registered alert processor: {type(processor).__name__}\")\n\n    async def process_alert(self, alert: Alert) -> Dict[str, Any]:\n        \"\"\"Process incoming alert through AIOps pipeline\"\"\"\n        logger.info(f\"Processing alert: {alert.id} - {alert.name}\")\n\n        # 1. Enrich alert with context\n        enriched_alert = await self._enrich_alert(alert)\n\n        # 2. Run through ML models for classification and prediction\n        analysis = await self._analyze_alert(enriched_alert)\n\n        # 3. Determine appropriate response\n        response_plan = await self._generate_response_plan(enriched_alert, analysis)\n\n        # 4. Execute automated actions if applicable\n        if response_plan.get(\"auto_execute\", False):\n            await self._execute_response(enriched_alert, response_plan)\n\n        # 5. Update knowledge base\n        await self.knowledge_base.update_from_alert(enriched_alert, analysis, response_plan)\n\n        return {\n            \"alert_id\": alert.id,\n            \"status\": \"processed\",\n            \"analysis\": analysis,\n            \"response_plan\": response_plan\n        }\n\n    async def _enrich_alert(self, alert: Alert) -> Alert:\n        \"\"\"Enrich alert with additional context\"\"\"\n        # Get related metrics\n        for source in self.data_sources:\n            try:\n                metrics = await source.collect_metrics(\n                    query=f\"{alert.name}[5m]\",\n                    start_time=alert.timestamp - timedelta(minutes=5),\n                    end_time=alert.timestamp\n                )\n\n                # Add metrics to alert annotations\n                alert.annotations[f\"metrics_{type(source).__name__}\"] = json.dumps([\n                    {\"name\": m.name, \"value\": m.value} for m in metrics\n                ])\n            except Exception as e:\n                logger.error(f\"Failed to enrich alert with metrics: {e}\")\n\n        # Get related logs\n        for source in self.data_sources:\n            try:\n                logs = await source.query_logs(\n                    query=f\"service:{alert.source} level:error\",\n                    start_time=alert.timestamp - timedelta(minutes=5),\n                    end_time=alert.timestamp,\n                    limit=10\n                )\n\n                # Add logs to alert context\n                alert.annotations[\"recent_errors\"] = json.dumps([\n                    {\"timestamp\": l.timestamp.isoformat(), \"message\": l.message}\n                    for l in logs\n                ])\n            except Exception as e:\n                logger.error(f\"Failed to enrich alert with logs: {e}\")\n\n        return alert\n\n    async def _analyze_alert(self, alert: Alert) -> Dict[str, Any]:\n        \"\"\"Analyze alert using ML models\"\"\"\n        analysis = {\n            \"classification\": None,\n            \"severity_score\": 0.0,\n            \"predicted_impact\": \"low\",\n            \"recommendations\": [],\n            \"related_incidents\": []\n        }\n\n        # Classification model\n        if \"classification\" in self.ml_models:\n            features = self._extract_features(alert)\n            analysis[\"classification\"] = self.ml_models[\"classification\"].predict([features])[0]\n\n        # Severity prediction\n        if \"severity\" in self.ml_models:\n            features = self._extract_features(alert)\n            analysis[\"severity_score\"] = self.ml_models[\"severity\"].predict_proba([features])[0][1]\n\n        # Impact prediction\n        if \"impact\" in self.ml_models:\n            features = self._extract_features(alert)\n            analysis[\"predicted_impact\"] = self.ml_models[\"impact\"].predict([features])[0]\n\n        # Find related incidents from knowledge base\n        analysis[\"related_incidents\"] = await self.knowledge_base.find_similar_incidents(alert)\n\n        return analysis\n\n    async def _generate_response_plan(\n        self,\n        alert: Alert,\n        analysis: Dict[str, Any]\n    ) -> Dict[str, Any]:\n        \"\"\"Generate automated response plan\"\"\"\n        plan = {\n            \"priority\": alert.severity,\n            \"actions\": [],\n            \"escalation_rules\": [],\n            \"auto_execute\": False,\n            \"estimated_resolution_time\": None\n        }\n\n        # Determine if auto-execution is safe\n        if (analysis[\"classification\"] in [\"known_issue\", \"false_positive\"] and\n            alert.severity in [AlertSeverity.LOW, AlertSeverity.MEDIUM]):\n            plan[\"auto_execute\"] = True\n\n        # Generate actions based on classification\n        classification = analysis.get(\"classification\")\n\n        if classification == \"resource_exhaustion\":\n            plan[\"actions\"].append({\n                \"type\": \"scale\",\n                \"target\": alert.source,\n                \"params\": {\"replicas\": \"+20%\"}\n            })\n            plan[\"actions\"].append({\n                \"type\": \"notify\",\n                \"channel\": \"devops\",\n                \"message\": f\"Resource exhaustion detected in {alert.source}\"\n            })\n\n        elif classification == \"known_issue\":\n            # Apply known fix\n            known_fix = await self.knowledge_base.get_known_fix(alert.name)\n            if known_fix:\n                plan[\"actions\"].append(known_fix)\n\n        elif classification == \"false_positive\":\n            plan[\"actions\"].append({\n                \"type\": \"suppress\",\n                \"duration\": \"1h\",\n                \"reason\": \"False positive detected\"\n            })\n\n        # Add escalation rules\n        if alert.severity == AlertSeverity.CRITICAL:\n            plan[\"escalation_rules\"].append({\n                \"condition\": \"no_response_5m\",\n                \"action\": \"escalate_to_oncall\",\n                \"timeout\": 300\n            })\n\n        return plan\n\n    def _extract_features(self, alert: Alert) -> List[float]:\n        \"\"\"Extract features for ML models\"\"\"\n        # Simple feature extraction - in production, use more sophisticated methods\n        features = [\n            float(alert.severity == AlertSeverity.CRITICAL),\n            float(alert.severity == AlertSeverity.HIGH),\n            float(alert.severity == AlertSeverity.MEDIUM),\n            len(alert.description),\n            len(alert.labels),\n            alert.timestamp.hour \u002F 24.0,\n            alert.timestamp.weekday() \u002F 7.0\n        ]\n        return features\n\n    async def _execute_response(\n        self,\n        alert: Alert,\n        response_plan: Dict[str, Any]\n    ) -> None:\n        \"\"\"Execute automated response actions\"\"\"\n        for action in response_plan.get(\"actions\", []):\n            try:\n                await self.automation_engine.execute(action)\n                logger.info(f\"Executed action for alert {alert.id}: {action['type']}\")\n            except Exception as e:\n                logger.error(f\"Failed to execute action: {e}\")\n```\n\n### Monitoring Platform Integrations\n\n```python\n# aiops\u002Fintegrations\u002Fmonitoring.py\nimport aiohttp\nimport asyncio\nfrom typing import List, Dict, Any, Optional\nfrom datetime import datetime\nfrom ..core.architecture import DataSource, Metric, LogEntry, Trace, Alert\n\nclass PrometheusDataSource(DataSource):\n    \"\"\"Prometheus metrics integration\"\"\"\n\n    def __init__(self, url: str):\n        self.url = url.rstrip(\"\u002F\")\n        self.session = None\n\n    async def _get_session(self) -> aiohttp.ClientSession:\n        if not self.session:\n            self.session = aiohttp.ClientSession()\n        return self.session\n\n    async def collect_metrics(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime\n    ) -> List[Metric]:\n        \"\"\"Query Prometheus for metrics\"\"\"\n        session = await self._get_session()\n\n        params = {\n            \"query\": query,\n            \"start\": start_time.timestamp(),\n            \"end\": end_time.timestamp(),\n            \"step\": \"15s\"\n        }\n\n        url = f\"{self.url}\u002Fapi\u002Fv1\u002Fquery_range\"\n\n        async with session.get(url, params=params) as response:\n            data = await response.json()\n            metrics = []\n\n            if data[\"status\"] == \"success\":\n                result = data[\"data\"]\n                if result[\"resultType\"] == \"matrix\":\n                    for series in result[\"result\"]:\n                        labels = series[\"metric\"]\n                        values = series[\"values\"]\n\n                        for timestamp, value in values:\n                            metrics.append(Metric(\n                                name=query,\n                                value=float(value),\n                                timestamp=datetime.fromtimestamp(float(timestamp)),\n                                labels=labels,\n                                source=\"prometheus\"\n                            ))\n\n            return metrics\n\n    async def query_logs(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime,\n        limit: int = 100\n    ) -> List[LogEntry]:\n        \"\"\"Prometheus doesn't store logs - use Loki integration\"\"\"\n        return []\n\n    async def get_trace(self, trace_id: str) -> Optional[Trace]:\n        \"\"\"Prometheus doesn't store traces - use Jaeger\u002FTempo integration\"\"\"\n        return None\n\nclass LokiDataSource(DataSource):\n    \"\"\"Loki log aggregation integration\"\"\"\n\n    def __init__(self, url: str):\n        self.url = url.rstrip(\"\u002F\")\n        self.session = None\n\n    async def _get_session(self) -> aiohttp.ClientSession:\n        if not self.session:\n            self.session = aiohttp.ClientSession()\n        return self.session\n\n    async def collect_metrics(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime\n    ) -> List[Metric]:\n        \"\"\"Loki doesn't store metrics\"\"\"\n        return []\n\n    async def query_logs(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime,\n        limit: int = 100\n    ) -> List[LogEntry]:\n        \"\"\"Query Loki for logs\"\"\"\n        session = await self._get_session()\n\n        params = {\n            \"query\": query,\n            \"start\": start_time.isoformat(),\n            \"end\": end_time.isoformat(),\n            \"limit\": str(limit)\n        }\n\n        url = f\"{self.url}\u002Floki\u002Fapi\u002Fv1\u002Fquery_range\"\n\n        async with session.get(url, params=params) as response:\n            data = await response.json()\n            logs = []\n\n            if data[\"status\"] == \"success\":\n                result = data[\"data\"]\n                for series in result[\"result\"]:\n                    stream = series[\"stream\"]\n                    for timestamp, line in series[\"values\"]:\n                        logs.append(LogEntry(\n                            timestamp=datetime.fromtimestamp(float(timestamp) \u002F 1e9),\n                            level=stream.get(\"level\", \"info\"),\n                            message=line,\n                            service=stream.get(\"service\", \"unknown\"),\n                            labels=stream\n                        ))\n\n            return logs\n\n    async def get_trace(self, trace_id: str) -> Optional[Trace]:\n        \"\"\"Loki doesn't store traces\"\"\"\n        return None\n\nclass JaegerDataSource(DataSource):\n    \"\"\"Jaeger distributed tracing integration\"\"\"\n\n    def __init__(self, url: str):\n        self.url = url.rstrip(\"\u002F\")\n        self.session = None\n\n    async def _get_session(self) -> aiohttp.ClientSession:\n        if not self.session:\n            self.session = aiohttp.ClientSession()\n        return self.session\n\n    async def collect_metrics(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime\n    ) -> List[Metric]:\n        \"\"\"Jaeger doesn't store metrics\"\"\"\n        return []\n\n    async def query_logs(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime,\n        limit: int = 100\n    ) -> List[LogEntry]:\n        \"\"\"Jaeger doesn't store logs\"\"\"\n        return []\n\n    async def get_trace(self, trace_id: str) -> Optional[Trace]:\n        \"\"\"Get trace from Jaeger\"\"\"\n        session = await self._get_session()\n\n        url = f\"{self.url}\u002Fapi\u002Ftraces\u002F{trace_id}\"\n\n        async with session.get(url) as response:\n            data = await response.json()\n\n            if data[\"data\"] and data[\"data\"][0][\"spans\"]:\n                spans = data[\"data\"][0][\"spans\"]\n                service_map = {}\n                error_count = 0\n\n                for span in spans:\n                    service = span[\"process\"][\"serviceName\"]\n                    if service not in service_map:\n                        service_map[service] = []\n                    service_map[service].append(span[\"spanID\"])\n\n                    if span.get(\"tags\"):\n                        for tag in span[\"tags\"]:\n                            if tag.get(\"key\") == \"error\" and tag.get(\"value\"):\n                                error_count += 1\n\n                return Trace(\n                    trace_id=trace_id,\n                    spans=spans,\n                    duration=timedelta(\n                        microseconds=data[\"data\"][0][\"duration\"]\n                    ),\n                    service_map=service_map,\n                    error_count=error_count\n                )\n\n            return None\n\nclass DatadogDataSource(DataSource):\n    \"\"\"Datadog APM integration\"\"\"\n\n    def __init__(self, api_key: str, app_key: str, site: str = \"datadoghq.com\"):\n        self.api_key = api_key\n        self.app_key = app_key\n        self.site = site\n        self.base_url = f\"https:\u002F\u002Fapi.{site}\"\n        self.session = None\n\n    async def _get_session(self) -> aiohttp.ClientSession:\n        if not self.session:\n            headers = {\n                \"DD-API-KEY\": self.api_key,\n                \"DD-APPLICATION-KEY\": self.app_key\n            }\n            self.session = aiohttp.ClientSession(headers=headers)\n        return self.session\n\n    async def collect_metrics(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime\n    ) -> List[Metric]:\n        \"\"\"Query Datadog metrics\"\"\"\n        session = await self._get_session()\n\n        params = {\n            \"query\": query,\n            \"from\": int(start_time.timestamp()),\n            \"to\": int(end_time.timestamp())\n        }\n\n        url = f\"{self.base_url}\u002Fapi\u002Fv1\u002Fquery\"\n\n        async with session.get(url, params=params) as response:\n            data = await response.json()\n            metrics = []\n\n            if \"series\" in data:\n                for series in data[\"series\"]:\n                    for point in series[\"pointlist\"]:\n                        metrics.append(Metric(\n                            name=series[\"metric\"],\n                            value=point[1],\n                            timestamp=datetime.fromtimestamp(point[0]),\n                            labels={k: str(v) for k, v in series[\"tag_set\"].items()},\n                            source=\"datadog\",\n                            unit=series.get(\"unit\", \"\")\n                        ))\n\n            return metrics\n\n    async def query_logs(\n        self,\n        query: str,\n        start_time: datetime,\n        end_time: datetime,\n        limit: int = 100\n    ) -> List[LogEntry]:\n        \"\"\"Query Datadog logs\"\"\"\n        session = await self._get_session()\n\n        payload = {\n            \"query\": query,\n            \"time\": {\n                \"from\": start_time.isoformat(),\n                \"to\": end_time.isoformat()\n            },\n            \"limit\": limit\n        }\n\n        url = f\"{self.base_url}\u002Fapi\u002Fv2\u002Flogs\u002Fevents\u002Fsearch\"\n\n        async with session.post(url, json=payload) as response:\n            data = await response.json()\n            logs = []\n\n            for log in data.get(\"data\", []):\n                attributes = log.get(\"attributes\", {})\n\n                logs.append(LogEntry(\n                    timestamp=datetime.fromisoformat(attributes[\"timestamp\"]),\n                    level=attributes.get(\"status\", \"info\"),\n                    message=attributes.get(\"message\", \"\"),\n                    service=attributes.get(\"service\", \"unknown\"),\n                    trace_id=attributes.get(\"trace_id\"),\n                    labels=attributes.get(\"tags\", [])\n                ))\n\n            return logs\n\n    async def get_trace(self, trace_id: str) -> Optional[Trace]:\n        \"\"\"Get trace from Datadog\"\"\"\n        session = await self._get_session()\n\n        url = f\"{self.base_url}\u002Fapi\u002Fv1\u002Ftrace\u002F{trace_id}\"\n\n        async with session.get(url) as response:\n            data = await response.json()\n\n            # Parse Datadog trace format\n            # Implementation depends on Datadog API response structure\n            return None\n```\n\n## 2. Machine Learning for Operations\n\n### Anomaly Detection Models\n\n```python\n# aiops\u002Fml\u002Fanomaly_detection.py\nimport numpy as np\nimport pandas as pd\nfrom typing import List, Dict, Tuple, Optional\nfrom datetime import datetime, timedelta\nfrom sklearn.ensemble import IsolationForest\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nimport joblib\nimport asyncio\nfrom collections import deque\n\nclass AnomalyDetector:\n    \"\"\"Generic anomaly detection for metrics and logs\"\"\"\n\n    def __init__(self, model_type: str = \"isolation_forest\"):\n        self.model_type = model_type\n        self.models: Dict[str, Any] = {}\n        self.scalers: Dict[str, StandardScaler] = {}\n        self.is_trained: Dict[str, bool] = {}\n        self.feature_history: Dict[str, deque] = {}\n\n    async def detect_metric_anomaly(\n        self,\n        metric_name: str,\n        current_value: float,\n        historical_values: List[float],\n        window_size: int = 100\n    ) -> Tuple[bool, float]:\n        \"\"\"Detect anomalies in time series metrics\"\"\"\n        if metric_name not in self.models:\n            await self._train_metric_model(metric_name, historical_values)\n\n        # Prepare features\n        features = self._extract_time_series_features(\n            current_value,\n            historical_values,\n            window_size\n        )\n\n        # Scale features\n        if metric_name in self.scalers:\n            features = self.scalers[metric_name].transform([features])\n\n        # Predict anomaly\n        model = self.models[metric_name]\n        anomaly_score = model.decision_function([features])[0]\n        is_anomaly = model.predict([features])[0] == -1\n\n        return is_anomaly, float(anomaly_score)\n\n    async def detect_log_anomaly(\n        self,\n        log_entries: List[Dict[str, Any]]\n    ) -> List[Dict[str, Any]]:\n        \"\"\"Detect anomalies in log patterns\"\"\"\n        # Extract log features\n        features = []\n        for entry in log_entries:\n            features.append([\n                len(entry.get(\"message\", \"\")),\n                entry.get(\"level_numeric\", 0),\n                entry.get(\"hour_of_day\", 0),\n                entry.get(\"day_of_week\", 0)\n            ])\n\n        features = np.array(features)\n\n        # Train or use existing model\n        model_name = \"log_patterns\"\n        if model_name not in self.models:\n            self.models[model_name] = IsolationForest(\n                contamination=0.1,\n                random_state=42\n            )\n            self.models[model_name].fit(features)\n\n        # Predict anomalies\n        predictions = self.models[model_name].predict(features)\n        scores = self.models[model_name].decision_function(features)\n\n        # Return anomalous logs\n        anomalies = []\n        for i, (pred, score) in enumerate(zip(predictions, scores)):\n            if pred == -1:  # Anomaly\n                anomalies.append({\n                    \"log_index\": i,\n                    \"log_entry\": log_entries[i],\n                    \"anomaly_score\": float(score),\n                    \"reason\": \"Unusual log pattern detected\"\n                })\n\n        return anomalies\n\n    async def detect_performance_anomaly(\n        self,\n        metrics: Dict[str, List[float]],\n        threshold: float = 0.1\n    ) -> Dict[str, Dict[str, Any]]:\n        \"\"\"Detect performance anomalies across multiple metrics\"\"\"\n        results = {}\n\n        for metric_name, values in metrics.items():\n            if len(values) \u003C 10:\n                continue\n\n            # Calculate statistical anomalies\n            mean_val = np.mean(values)\n            std_val = np.std(values)\n            recent_values = values[-5:]\n\n            for i, value in enumerate(recent_values):\n                z_score = abs(value - mean_val) \u002F (std_val + 1e-8)\n\n                if z_score > 3:  # Statistical outlier\n                    if metric_name not in results:\n                        results[metric_name] = {\n                            \"anomalies\": [],\n                            \"severity\": \"medium\"\n                        }\n\n                    results[metric_name][\"anomalies\"].append({\n                        \"value\": value,\n                        \"z_score\": z_score,\n                        \"index\": i,\n                        \"type\": \"statistical\"\n                    })\n\n        return results\n\n    async def _train_metric_model(\n        self,\n        metric_name: str,\n        values: List[float]\n    ) -> None:\n        \"\"\"Train anomaly detection model for a metric\"\"\"\n        if len(values) \u003C 30:\n            # Not enough data for training\n            return\n\n        # Prepare training data\n        training_features = []\n        for i in range(10, len(values)):\n            # Create window of previous values\n            window = values[i-10:i]\n            features = [\n                np.mean(window),\n                np.std(window),\n                values[i],  # Current value\n                (values[i] - np.mean(window)) \u002F (np.std(window) + 1e-8),  # Z-score\n                max(window) - min(window),  # Range\n            ]\n            training_features.append(features)\n\n        training_features = np.array(training_features)\n\n        # Scale features\n        scaler = StandardScaler()\n        scaled_features = scaler.fit_transform(training_features)\n        self.scalers[metric_name] = scaler\n\n        # Train model\n        if self.model_type == \"isolation_forest\":\n            model = IsolationForest(\n                contamination=0.05,\n                random_state=42\n            )\n            model.fit(scaled_features)\n        else:\n            # Add other model types as needed\n            model = IsolationForest(contamination=0.05, random_state=42)\n            model.fit(scaled_features)\n\n        self.models[metric_name] = model\n        self.is_trained[metric_name] = True\n\n    def _extract_time_series_features(\n        self,\n        current_value: float,\n        historical_values: List[float],\n        window_size: int\n    ) -> List[float]:\n        \"\"\"Extract features from time series data\"\"\"\n        if len(historical_values) \u003C window_size:\n            window = historical_values\n        else:\n            window = historical_values[-window_size:]\n\n        features = [\n            np.mean(window),\n            np.std(window),\n            min(window),\n            max(window),\n            current_value,\n            (current_value - np.mean(window)) \u002F (np.std(window) + 1e-8),\n            len([v for v in window if v > np.mean(window)]) \u002F len(window),  # Percentage above mean\n        ]\n\n        return features\n\nclass PredictiveAnalyzer:\n    \"\"\"Predictive analytics for capacity planning and incident prediction\"\"\"\n\n    def __init__(self):\n        self.models: Dict[str, Any] = {}\n\n    async def predict_capacity_needs(\n        self,\n        metric_history: Dict[str, List[float]],\n        forecast_horizon: int = 7\n    ) -> Dict[str, Dict[str, Any]]:\n        \"\"\"Predict future resource needs\"\"\"\n        predictions = {}\n\n        for metric_name, values in metric_history.items():\n            if len(values) \u003C 30:\n                continue\n\n            # Simple linear trend prediction (use more sophisticated models in production)\n            x = np.arange(len(values))\n            coeffs = np.polyfit(x, values, 1)\n\n            # Predict future values\n            future_x = np.arange(len(values), len(values) + forecast_horizon * 24)\n            future_values = np.polyval(coeffs, future_x)\n\n            # Calculate growth rate\n            growth_rate = (future_values[-1] - values[-1]) \u002F values[-1] * 100\n\n            predictions[metric_name] = {\n                \"current\": values[-1],\n                \"predicted_peak\": float(max(future_values)),\n                \"predicted_average\": float(np.mean(future_values)),\n                \"growth_rate\": growth_rate,\n                \"recommendation\": self._get_capacity_recommendation(growth_rate),\n                \"confidence\": 0.75  # Should be calculated based on model accuracy\n            }\n\n        return predictions\n\n    async def predict_incidents(\n        self,\n        alert_history: List[Dict[str, Any]],\n        system_metrics: Dict[str, List[float]]\n    ) -> Dict[str, Any]:\n        \"\"\"Predict likelihood of future incidents\"\"\"\n        # Feature engineering\n        features = self._extract_incident_features(alert_history, system_metrics)\n\n        # Simplified risk score calculation\n        risk_factors = {\n            \"error_rate_trend\": self._calculate_trend(\n                system_metrics.get(\"error_rate\", [])\n            ),\n            \"cpu_utilization_avg\": np.mean(\n                system_metrics.get(\"cpu_utilization\", [0])\n            ),\n            \"memory_utilization_avg\": np.mean(\n                system_metrics.get(\"memory_utilization\", [0])\n            ),\n            \"recent_incident_count\": len([\n                a for a in alert_history[-24:]\n                if a.get(\"severity\") == \"critical\"\n            ]),\n        }\n\n        # Calculate risk score (0-100)\n        risk_score = min(100, max(0, (\n            risk_factors[\"error_rate_trend\"] * 30 +\n            risk_factors[\"cpu_utilization_avg\"] * 25 +\n            risk_factors[\"memory_utilization_avg\"] * 25 +\n            risk_factors[\"recent_incident_count\"] * 20\n        )))\n\n        return {\n            \"risk_score\": risk_score,\n            \"risk_level\": self._get_risk_level(risk_score),\n            \"contributing_factors\": risk_factors,\n            \"recommendations\": self._get_mitigation_recommendations(risk_factors)\n        }\n\n    def _extract_incident_features(\n        self,\n        alert_history: List[Dict[str, Any]],\n        system_metrics: Dict[str, List[float]]\n    ) -> List[float]:\n        \"\"\"Extract features for incident prediction\"\"\"\n        features = []\n\n        # Alert patterns\n        recent_alerts = alert_history[-24:] if len(alert_history) >= 24 else alert_history\n        features.append(len(recent_alerts))\n        features.append(len([a for a in recent_alerts if a.get(\"severity\") == \"critical\"]))\n\n        # System metrics\n        for metric_name in [\"cpu\", \"memory\", \"disk\", \"network\"]:\n            values = system_metrics.get(f\"{metric_name}_utilization\", [0])\n            features.append(np.mean(values[-12:]) if values else 0)\n            features.append(np.std(values[-12:]) if values else 0)\n\n        return features\n\n    def _calculate_trend(self, values: List[float]) -> float:\n        \"\"\"Calculate trend coefficient (positive = increasing)\"\"\"\n        if len(values) \u003C 2:\n            return 0.0\n\n        x = np.arange(len(values))\n        coeffs = np.polyfit(x, values, 1)\n        return coeffs[0]\n\n    def _get_capacity_recommendation(self, growth_rate: float) -> str:\n        \"\"\"Get capacity recommendation based on growth rate\"\"\"\n        if growth_rate > 50:\n            return \"Immediate scaling required - consider 2x current capacity\"\n        elif growth_rate > 20:\n            return \"Plan for scaling within next week - recommend 1.5x capacity\"\n        elif growth_rate > 0:\n            return \"Monitor growth - consider 25% buffer\"\n        else:\n            return \"Capacity adequate - no immediate action needed\"\n\n    def _get_risk_level(self, risk_score: float) -> str:\n        \"\"\"Get risk level from risk score\"\"\"\n        if risk_score >= 80:\n            return \"critical\"\n        elif risk_score >= 60:\n            return \"high\"\n        elif risk_score >= 40:\n            return \"medium\"\n        else:\n            return \"low\"\n\n    def _get_mitigation_recommendations(\n        self,\n        risk_factors: Dict[str, float]\n    ) -> List[str]:\n        \"\"\"Get mitigation recommendations based on risk factors\"\"\"\n        recommendations = []\n\n        if risk_factors[\"cpu_utilization_avg\"] > 80:\n            recommendations.append(\"Scale CPU resources or optimize CPU usage\")\n\n        if risk_factors[\"memory_utilization_avg\"] > 80:\n            recommendations.append(\"Increase memory allocation or check for memory leaks\")\n\n        if risk_factors[\"error_rate_trend\"] > 0:\n            recommendations.append(\"Investigate and fix increasing error rate\")\n\n        if risk_factors[\"recent_incident_count\"] > 3:\n            recommendations.append(\"Review recent incidents for root cause analysis\")\n\n        return recommendations\n\nclass AILogAnalyzer:\n    \"\"\"AI-powered log analysis and pattern detection\"\"\"\n\n    def __init__(self):\n        self.patterns: Dict[str, List[str]] = {}\n        self.embeddings: Dict[str, np.ndarray] = {}\n\n    async def analyze_logs(\n        self,\n        logs: List[LogEntry]\n    ) -> Dict[str, Any]:\n        \"\"\"Analyze logs for patterns and anomalies\"\"\"\n        analysis = {\n            \"total_logs\": len(logs),\n            \"error_rate\": 0.0,\n            \"patterns\": [],\n            \"anomalies\": [],\n            \"trending_topics\": [],\n            \"recommendations\": []\n        }\n\n        # Calculate error rate\n        error_count = len([l for l in logs if l.level.lower() in [\"error\", \"critical\"]])\n        analysis[\"error_rate\"] = error_count \u002F len(logs) * 100\n\n        # Extract patterns using regex and ML\n        analysis[\"patterns\"] = await self._extract_patterns(logs)\n\n        # Find anomalies\n        analysis[\"anomalies\"] = await self._detect_log_anomalies(logs)\n\n        # Identify trending topics\u002Ferrors\n        analysis[\"trending_topics\"] = await self._identify_trending_topics(logs)\n\n        # Generate recommendations\n        analysis[\"recommendations\"] = self._generate_log_recommendations(analysis)\n\n        return analysis\n\n    async def _extract_patterns(self, logs: List[LogEntry]) -> List[Dict[str, Any]]:\n        \"\"\"Extract common patterns from logs\"\"\"\n        # Simplified pattern extraction\n        # In production, use more sophisticated NLP techniques\n        patterns = []\n\n        # Group logs by service\n        service_logs = {}\n        for log in logs:\n            if log.service not in service_logs:\n                service_logs[log.service] = []\n            service_logs[log.service].append(log)\n\n        # Find error patterns\n        for service, service_log_list in service_logs.items():\n            error_logs = [l for l in service_log_list if l.level.lower() == \"error\"]\n            if len(error_logs) > 0:\n                # Find common substrings in error messages\n                common_errors = {}\n                for log in error_logs:\n                    # Simplified - use actual pattern matching in production\n                    for word in log.message.split():\n                        if len(word) > 5:  # Only consider longer words\n                            common_errors[word] = common_errors.get(word, 0) + 1\n\n                if common_errors:\n                    most_common = max(common_errors, key=common_errors.get)\n                    patterns.append({\n                        \"service\": service,\n                        \"type\": \"error_pattern\",\n                        \"pattern\": most_common,\n                        \"count\": common_errors[most_common],\n                        \"percentage\": common_errors[most_common] \u002F len(error_logs) * 100\n                    })\n\n        return patterns\n\n    async def _detect_log_anomalies(self, logs: List[LogEntry]) -> List[Dict[str, Any]]:\n        \"\"\"Detect anomalies in logs\"\"\"\n        anomalies = []\n\n        # Check for unusual error spikes\n        error_by_minute = {}\n        for log in logs:\n            if log.level.lower() in [\"error\", \"critical\"]:\n                minute = log.timestamp.replace(second=0, microsecond=0)\n                error_by_minute[minute] = error_by_minute.get(minute, 0) + 1\n\n        if error_by_minute:\n            avg_errors = np.mean(list(error_by_minute.values()))\n            for minute, count in error_by_minute.items():\n                if count > avg_errors * 3:  # 3x spike\n                    anomalies.append({\n                        \"type\": \"error_spike\",\n                        \"timestamp\": minute.isoformat(),\n                        \"value\": count,\n                        \"baseline\": avg_errors,\n                        \"severity\": \"high\"\n                    })\n\n        return anomalies\n\n    async def _identify_trending_topics(self, logs: List[LogEntry]) -> List[Dict[str, Any]]:\n        \"\"\"Identify trending topics in logs\"\"\"\n        # Simplified trending detection\n        word_count = {}\n        for log in logs[-100:]:  # Last 100 logs\n            for word in log.message.lower().split():\n                if len(word) > 5 and word.isalpha():\n                    word_count[word] = word_count.get(word, 0) + 1\n\n        # Get top trending words\n        trending = sorted(word_count.items(), key=lambda x: x[1], reverse=True)[:10]\n\n        return [\n            {\"topic\": word, \"count\": count}\n            for word, count in trending\n        ]\n\n    def _generate_log_recommendations(self, analysis: Dict[str, Any]) -> List[str]:\n        \"\"\"Generate recommendations based on log analysis\"\"\"\n        recommendations = []\n\n        if analysis[\"error_rate\"] > 5:\n            recommendations.append(\n                f\"High error rate ({analysis['error_rate']:.1f}%) detected - investigate immediately\"\n            )\n\n        if len(analysis[\"anomalies\"]) > 0:\n            recommendations.append(\n                f\"Found {len(analysis['anomalies'])} anomalies - review and address\"\n            )\n\n        for pattern in analysis[\"patterns\"][:3]:\n            if pattern[\"percentage\"] > 30:\n                recommendations.append(\n                    f\"Common error pattern in {pattern['service']}: {pattern['pattern']}\"\n                )\n\n        return recommendations\n```\n\n## 3. Automation and Self-Healing\n\n### Automation Engine\n\n```python\n# aiops\u002Fautomation\u002Fengine.py\nimport asyncio\nimport subprocess\nfrom typing import Dict, List, Any, Optional, Callable\nfrom dataclasses import dataclass\nfrom enum import Enum\nimport logging\nfrom datetime import datetime, timedelta\n\nlogger = logging.getLogger(__name__)\n\nclass ActionType(str, Enum):\n    \"\"\"Types of automation actions\"\"\"\n    SCALE = \"scale\"\n    RESTART = \"restart\"\n    PATCH = \"patch\"\n    NOTIFY = \"notify\"\n    SUPPRESS = \"suppress\"\n    ISOLATE = \"isolate\"\n    FAILBACK = \"failback\"\n\n@dataclass\nclass Action:\n    \"\"\"Automation action definition\"\"\"\n    type: ActionType\n    target: str\n    params: Dict[str, Any]\n    conditions: List[str] = None\n    timeout: int = 300\n    retries: int = 3\n\nclass AutomationEngine:\n    \"\"\"Automation engine for executing remediation actions\"\"\"\n\n    def __init__(self):\n        self.actions: Dict[str, Action] = {}\n        self.execution_history: List[Dict[str, Any]] = []\n        self.action_handlers: Dict[ActionType, Callable] = {\n            ActionType.SCALE: self._handle_scale,\n            ActionType.RESTART: self._handle_restart,\n            ActionType.PATCH: self._handle_patch,\n            ActionType.NOTIFY: self._handle_notify,\n            ActionType.SUPPRESS: self._handle_suppress,\n            ActionType.ISOLATE: self._handle_isolate,\n            ActionType.FAILBACK: self._handle_failback\n        }\n\n    def register_action(self, action: Action) -> None:\n        \"\"\"Register an automation action\"\"\"\n        self.actions[action.target] = action\n        logger.info(f\"Registered action: {action.type} for {action.target}\")\n\n    async def execute(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Execute an automation action\"\"\"\n        execution_id = f\"{action.type}_{action.target}_{int(datetime.utcnow().timestamp())}\"\n\n        logger.info(f\"Executing action {execution_id}: {action.type} on {action.target}\")\n\n        execution = {\n            \"id\": execution_id,\n            \"action\": action,\n            \"status\": \"running\",\n            \"started_at\": datetime.utcnow(),\n            \"result\": None,\n            \"error\": None\n        }\n\n        try:\n            # Check conditions\n            if action.conditions and not await self._check_conditions(action.conditions):\n                execution[\"status\"] = \"skipped\"\n                execution[\"error\"] = \"Conditions not met\"\n                return execution\n\n            # Execute action with retries\n            result = await self._execute_with_retries(action)\n\n            execution[\"status\"] = \"success\"\n            execution[\"result\"] = result\n            logger.info(f\"Action {execution_id} completed successfully\")\n\n        except Exception as e:\n            execution[\"status\"] = \"failed\"\n            execution[\"error\"] = str(e)\n            logger.error(f\"Action {execution_id} failed: {e}\")\n\n        finally:\n            execution[\"completed_at\"] = datetime.utcnow()\n            execution[\"duration\"] = (execution[\"completed_at\"] - execution[\"started_at\"]).total_seconds()\n            self.execution_history.append(execution)\n\n        return execution\n\n    async def _execute_with_retries(self, action: Action) -> Any:\n        \"\"\"Execute action with retry logic\"\"\"\n        last_exception = None\n\n        for attempt in range(action.retries + 1):\n            try:\n                handler = self.action_handlers.get(action.type)\n                if not handler:\n                    raise ValueError(f\"No handler for action type: {action.type}\")\n\n                # Execute with timeout\n                return await asyncio.wait_for(\n                    handler(action),\n                    timeout=action.timeout\n                )\n\n            except asyncio.TimeoutError:\n                last_exception = TimeoutError(f\"Action timed out after {action.timeout}s\")\n                if attempt \u003C action.retries:\n                    await asyncio.sleep(2 ** attempt)  # Exponential backoff\n                    continue\n                raise last_exception\n\n            except Exception as e:\n                last_exception = e\n                if attempt \u003C action.retries:\n                    await asyncio.sleep(2 ** attempt)  # Exponential backoff\n                    continue\n                raise last_exception\n\n    async def _check_conditions(self, conditions: List[str]) -> bool:\n        \"\"\"Check if action conditions are met\"\"\"\n        # Implement condition checking logic\n        # For example, check cluster health, time windows, etc.\n        return True\n\n    async def _handle_scale(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle scale action\"\"\"\n        params = action.params\n        command = [\n            \"kubectl\", \"scale\",\n            f\"--replicas={params.get('replicas', '+1')}\",\n            f\"deployment\u002F{action.target}\"\n        ]\n\n        if \"namespace\" in params:\n            command.insert(1, f\"-n={params['namespace']}\")\n\n        result = await self._run_command(command)\n        return {\"scaled\": True, \"result\": result}\n\n    async def _handle_restart(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle restart action\"\"\"\n        params = action.params\n        command = [\"kubectl\", \"rollout\", \"restart\", f\"deployment\u002F{action.target}\"]\n\n        if \"namespace\" in params:\n            command.insert(2, f\"-n={params['namespace']}\")\n\n        result = await self._run_command(command)\n        return {\"restarted\": True, \"result\": result}\n\n    async def _handle_patch(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle patch action\"\"\"\n        params = action.params\n\n        # Build patch command\n        command = [\"kubectl\", \"patch\"]\n\n        if \"namespace\" in params:\n            command.extend([\"-n\", params[\"namespace\"]])\n\n        resource_type = params.get(\"type\", \"deployment\")\n        command.extend([f\"{resource_type}\u002F{action.target}\"])\n\n        patch_type = params.get(\"patch_type\", \"strategic\")\n        command.extend([\"--type\", patch_type])\n\n        patch_data = params.get(\"patch\", {})\n        command.extend([\"-p\", json.dumps(patch_data)])\n\n        result = await self._run_command(command)\n        return {\"patched\": True, \"result\": result}\n\n    async def _handle_notify(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle notification action\"\"\"\n        params = action.params\n        channel = params.get(\"channel\", \"default\")\n        message = params.get(\"message\", f\"AAction executed: {action.type} on {action.target}\")\n\n        # Implement notification logic (Slack, Teams, Email, etc.)\n        # This is a placeholder for actual notification implementation\n        notification = {\n            \"channel\": channel,\n            \"message\": message,\n            \"timestamp\": datetime.utcnow().isoformat(),\n            \"sent\": True\n        }\n\n        # Example: Send to Slack\n        if channel == \"slack\":\n            webhook_url = params.get(\"webhook_url\")\n            if webhook_url:\n                slack_payload = {\n                    \"text\": message,\n                    \"attachments\": [{\n                        \"color\": \"warning\",\n                        \"fields\": [{\n                            \"title\": \"Action\",\n                            \"value\": f\"{action.type} on {action.target}\",\n                            \"short\": True\n                        }, {\n                            \"title\": \"Timestamp\",\n                            \"value\": datetime.utcnow().strftime(\"%Y-%m-%d %H:%M:%S\"),\n                            \"short\": True\n                        }]\n                    }]\n                }\n\n                async with aiohttp.ClientSession() as session:\n                    async with session.post(webhook_url, json=slack_payload) as response:\n                        if response.status != 200:\n                            raise Exception(f\"Slack notification failed: {response.status}\")\n\n        return notification\n\n    async def _handle_suppress(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle alert suppression\"\"\"\n        params = action.params\n        duration = params.get(\"duration\", \"1h\")\n        reason = params.get(\"reason\", \"Automated suppression\")\n\n        # Implement alert suppression logic\n        # This depends on your alerting system (Prometheus, AlertManager, etc.)\n        suppression = {\n            \"target\": action.target,\n            \"duration\": duration,\n            \"reason\": reason,\n            \"timestamp\": datetime.utcnow().isoformat()\n        }\n\n        return {\"suppressed\": True, \"suppression\": suppression}\n\n    async def _handle_isolate(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle service isolation\"\"\"\n        params = action.params\n        namespace = params.get(\"namespace\", \"default\")\n\n        # Implement isolation logic\n        # For example, update service to point to maintenance page\n        command = [\n            \"kubectl\", \"patch\", \"service\", action.target,\n            \"-n\", namespace,\n            \"-p\", '{\"spec\": {\"selector\": {\"app\": \"maintenance\"}}}'\n        ]\n\n        result = await self._run_command(command)\n        return {\"isolated\": True, \"result\": result}\n\n    async def _handle_failback(self, action: Action) -> Dict[str, Any]:\n        \"\"\"Handle failback to primary\"\"\"\n        params = action.params\n        namespace = params.get(\"namespace\", \"default\")\n\n        # Implement failback logic\n        # Restore traffic to primary service\n        command = [\n            \"kubectl\", \"patch\", \"service\", action.target,\n            \"-n\", namespace,\n            \"-p\", '{\"spec\": {\"selector\": {\"app\": \"' + action.target + '\"}}}'\n        ]\n\n        result = await self._run_command(command)\n        return {\"failed_back\": True, \"result\": result}\n\n    async def _run_command(self, command: List[str]) -> str:\n        \"\"\"Run shell command\"\"\"\n        process = await asyncio.create_subprocess_exec(\n            *command,\n            stdout=asyncio.subprocess.PIPE,\n            stderr=asyncio.subprocess.PIPE\n        )\n\n        stdout, stderr = await process.communicate()\n\n        if process.returncode != 0:\n            raise Exception(f\"Command failed: {stderr.decode()}\")\n\n        return stdout.decode()\n\nclass SelfHealingManager:\n    \"\"\"Manages self-healing policies and execution\"\"\"\n\n    def __init__(self, automation_engine: AutomationEngine):\n        self.automation_engine = automation_engine\n        self.healing_policies: Dict[str, Dict[str, Any]] = {}\n        self.active_healings: Dict[str, Dict[str, Any]] = {}\n\n    def register_healing_policy(\n        self,\n        service_name: str,\n        policy: Dict[str, Any]\n    ) -> None:\n        \"\"\"Register a self-healing policy for a service\"\"\"\n        self.healing_policies[service_name] = policy\n        logger.info(f\"Registered healing policy for {service_name}\")\n\n    async def trigger_healing(\n        self,\n        service_name: str,\n        incident: Dict[str, Any]\n    ) -> Dict[str, Any]:\n        \"\"\"Trigger self-healing for a service\"\"\"\n        if service_name not in self.healing_policies:\n            return {\"error\": \"No healing policy registered\"}\n\n        policy = self.healing_policies[service_name]\n        healing_id = f\"healing_{service_name}_{int(datetime.utcnow().timestamp())}\"\n\n        logger.info(f\"Triggering self-healing {healing_id} for {service_name}\")\n\n        healing_record = {\n            \"id\": healing_id,\n            \"service\": service_name,\n            \"incident\": incident,\n            \"policy\": policy,\n            \"status\": \"running\",\n            \"actions\": [],\n            \"started_at\": datetime.utcnow()\n        }\n\n        self.active_healings[healing_id] = healing_record\n\n        try:\n            # Execute healing actions based on policy\n            for action_def in policy.get(\"actions\", []):\n                action = Action(\n                    type=ActionType(action_def[\"type\"]),\n                    target=action_def.get(\"target\", service_name),\n                    params=action_def.get(\"params\", {}),\n                    timeout=action_def.get(\"timeout\", 300)\n                )\n\n                result = await self.automation_engine.execute(action)\n                healing_record[\"actions\"].append({\n                    \"action\": action_def,\n                    \"result\": result\n                })\n\n                # Check if healing was successful\n                if result[\"status\"] != \"success\":\n                    healing_record[\"status\"] = \"failed\"\n                    break\n            else:\n                healing_record[\"status\"] = \"success\"\n\n        except Exception as e:\n            healing_record[\"status\"] = \"error\"\n            healing_record[\"error\"] = str(e)\n            logger.error(f\"Self-healing failed: {e}\")\n\n        finally:\n            healing_record[\"completed_at\"] = datetime.utcnow()\n            healing_record[\"duration\"] = (\n                healing_record[\"completed_at\"] - healing_record[\"started_at\"]\n            ).total_seconds()\n\n        return healing_record\n\n    async def check_healing_status(self, healing_id: str) -> Optional[Dict[str, Any]]:\n        \"\"\"Get status of an active healing process\"\"\"\n        return self.active_healings.get(healing_id)\n```\n\n## 4. Observability and Monitoring Best Practices\n\n### Unified Observability Platform\n\n```python\n# aiops\u002Fobservability\u002Fplatform.py\nimport asyncio\nfrom typing import Dict, List, Any, Optional\nfrom datetime import datetime, timedelta\nfrom dataclasses import dataclass, field\nimport json\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n@dataclass\nclass ServiceHealth:\n    \"\"\"Service health status\"\"\"\n    service_name: str\n    status: str  # healthy, degraded, unhealthy\n    latency_p99: float\n    error_rate: float\n    throughput: float\n    last_check: datetime\n    dependencies: List[str] = field(default_factory=list)\n    sla_status: str = \"within_sla\"\n\nclass ObservabilityPlatform:\n    \"\"\"Unified observability platform for AIOps\"\"\"\n\n    def __init__(self, data_sources: List[DataSource]):\n        self.data_sources = data_sources\n        self.service_health: Dict[str, ServiceHealth] = {}\n        self.slo_compliance: Dict[str, Dict[str, Any]] = {}\n        self.dashboard_configs: Dict[str, Dict[str, Any]] = {}\n\n    async def get_service_overview(self) -> Dict[str, Any]:\n        \"\"\"Get comprehensive service overview\"\"\"\n        overview = {\n            \"total_services\": len(self.service_health),\n            \"healthy_services\": 0,\n            \"degraded_services\": 0,\n            \"unhealthy_services\": 0,\n            \"services\": {}\n        }\n\n        for service_name, health in self.service_health.items():\n            overview[\"services\"][service_name] = {\n                \"status\": health.status,\n                \"latency_p99\": health.latency_p99,\n                \"error_rate\": health.error_rate,\n                \"throughput\": health.throughput,\n                \"sla_status\": health.sla_status\n            }\n\n            if health.status == \"healthy\":\n                overview[\"healthy_services\"] += 1\n            elif health.status == \"degraded\":\n                overview[\"degraded_services\"] += 1\n            else:\n                overview[\"unhealthy_services\"] += 1\n\n        return overview\n\n    async def check_slo_compliance(\n        self,\n        service_name: str,\n        slo_config: Dict[str, Any]\n    ) -> Dict[str, Any]:\n        \"\"\"Check SLO compliance for a service\"\"\"\n        now = datetime.utcnow()\n        window = timedelta(days=30)  # 30-day rolling window\n\n        # Collect metrics for SLO calculation\n        metrics = []\n        for source in self.data_sources:\n            try:\n                source_metrics = await source.collect_metrics(\n                    query=slo_config[\"metric_query\"],\n                    start_time=now - window,\n                    end_time=now\n                )\n                metrics.extend(source_metrics)\n            except Exception as e:\n                logger.error(f\"Failed to collect metrics from {type(source).__name__}: {e}\")\n\n        if not metrics:\n            return {\"error\": \"No metrics available\"}\n\n        # Calculate SLO compliance\n        total_requests = sum(m.value for m in metrics if m.name.endswith(\"_total\"))\n        good_requests = sum(m.value for m in metrics if m.name.endswith(\"_success\"))\n\n        if total_requests == 0:\n            return {\"error\": \"No requests in time window\"}\n\n        actual_availability = (good_requests \u002F total_requests) * 100\n        target_availability = slo_config[\"target\"]\n\n        compliance = {\n            \"service\": service_name,\n            \"slo_name\": slo_config[\"name\"],\n            \"target_availability\": target_availability,\n            \"actual_availability\": actual_availability,\n            \"compliance\": actual_availability >= target_availability,\n            \"error_budget\": target_availability - actual_availability,\n            \"time_window\": window.days,\n            \"total_requests\": total_requests,\n            \"good_requests\": good_requests,\n            \"calculated_at\": now.isoformat()\n        }\n\n        self.slo_compliance[f\"{service_name}_{slo_config['name']}\"] = compliance\n        return compliance\n\n    async def generate_incident_report(\n        self,\n        incident_id: str,\n        start_time: datetime,\n        end_time: Optional[datetime] = None\n    ) -> Dict[str, Any]:\n        \"\"\"Generate comprehensive incident report\"\"\"\n        if not end_time:\n            end_time = datetime.utcnow()\n\n        report = {\n            \"incident_id\": incident_id,\n            \"start_time\": start_time.isoformat(),\n            \"end_time\": end_time.isoformat(),\n            \"duration_hours\": (end_time - start_time).total_seconds() \u002F 3600,\n            \"affected_services\": [],\n            \"metrics_impact\": {},\n            \"error_rate_impact\": {},\n            \"user_impact\": {},\n            \"timeline\": [],\n            \"root_cause_analysis\": {},\n            \"action_items\": []\n        }\n\n        # Collect data for affected services\n        for service_name, health in self.service_health.items():\n            if health.last_check >= start_time and health.last_check \u003C= end_time:\n                report[\"affected_services\"].append(service_name)\n\n                # Collect metrics impact\n                for source in self.data_sources:\n                    try:\n                        metrics = await source.collect_metrics(\n                            query=f\"rate(http_requests_total[5m])\",\n                            start_time=start_time,\n                            end_time=end_time\n                        )\n                        if metrics:\n                            report[\"metrics_impact\"][service_name] = [\n                                {\"timestamp\": m.timestamp.isoformat(), \"value\": m.value}\n                                for m in metrics\n                            ]\n                    except Exception as e:\n                        logger.error(f\"Failed to collect metrics for {service_name}: {e}\")\n\n        return report\n\n    async def create_dashboard(\n        self,\n        dashboard_name: str,\n        config: Dict[str, Any]\n    ) -> Dict[str, Any]:\n        \"\"\"Create a new observability dashboard\"\"\"\n        dashboard = {\n            \"name\": dashboard_name,\n            \"description\": config.get(\"description\", \"\"),\n            \"panels\": config.get(\"panels\", []),\n            \"time_range\": config.get(\"time_range\", \"1h\"),\n            \"refresh_interval\": config.get(\"refresh_interval\", \"5m\"),\n            \"created_at\": datetime.utcnow().isoformat()\n        }\n\n        self.dashboard_configs[dashboard_name] = dashboard\n        return dashboard\n\n    async def get_dashboard_data(\n        self,\n        dashboard_name: str,\n        time_range: Optional[str] = None\n    ) -> Dict[str, Any]:\n        \"\"\"Get data for a specific dashboard\"\"\"\n        if dashboard_name not in self.dashboard_configs:\n            return {\"error\": \"Dashboard not found\"}\n\n        dashboard = self.dashboard_configs[dashboard_name]\n        now = datetime.utcnow()\n\n        # Parse time range\n        if time_range:\n            end_time = now\n            if time_range.endswith(\"h\"):\n                start_time = now - timedelta(hours=int(time_range[:-1]))\n            elif time_range.endswith(\"d\"):\n                start_time = now - timedelta(days=int(time_range[:-1]))\n            else:\n                start_time = now - timedelta(hours=1)\n        else:\n            start_time = now - timedelta(hours=1)\n            end_time = now\n\n        dashboard_data = {\n            \"name\": dashboard_name,\n            \"time_range\": {\"start\": start_time.isoformat(), \"end\": end_time.isoformat()},\n            \"panels\": []\n        }\n\n        # Collect data for each panel\n        for panel in dashboard[\"panels\"]:\n            panel_data = {\n                \"title\": panel[\"title\"],\n                \"type\": panel[\"type\"],\n                \"data\": []\n            }\n\n            try:\n                # Collect metrics based on panel query\n                for source in self.data_sources:\n                    metrics = await source.collect_metrics(\n                        query=panel[\"query\"],\n                        start_time=start_time,\n                        end_time=end_time\n                    )\n                    panel_data[\"data\"].extend([\n                        {\"timestamp\": m.timestamp.isoformat(), \"value\": m.value}\n                        for m in metrics\n                    ])\n            except Exception as e:\n                panel_data[\"error\"] = str(e)\n\n            dashboard_data[\"panels\"].append(panel_data)\n\n        return dashboard_data\n\n# SLO and SLA Management\nclass SLOManager:\n    \"\"\"Manages Service Level Objectives and Service Level Agreements\"\"\"\n\n    def __init__(self):\n        self.slos: Dict[str, Dict[str, Any]] = {}\n        self.slas: Dict[str, Dict[str, Any]] = {}\n\n    def define_slo(\n        self,\n        service_name: str,\n        slo_name: str,\n        target: float,\n        time_window: str,\n        alerting_burn_rate: float = 2.0\n    ) -> Dict[str, Any]:\n        \"\"\"Define a Service Level Objective\"\"\"\n        slo = {\n            \"service_name\": service_name,\n            \"slo_name\": slo_name,\n            \"target\": target,\n            \"time_window\": time_window,\n            \"alerting_burn_rate\": alerting_burn_rate,\n            \"created_at\": datetime.utcnow().isoformat(),\n            \"status\": \"active\"\n        }\n\n        self.slos[f\"{service_name}_{slo_name}\"] = slo\n        return slo\n\n    def define_sla(\n        self,\n        service_name: str,\n        sla_name: str,\n        availability_target: float,\n        performance_targets: Dict[str, float],\n        penalties: Dict[str, Any] = None\n    ) -> Dict[str, Any]:\n        \"\"\"Define a Service Level Agreement\"\"\"\n        sla = {\n            \"service_name\": service_name,\n            \"sla_name\": sla_name,\n            \"availability_target\": availability_target,\n            \"performance_targets\": performance_targets,\n            \"penalties\": penalties or {},\n            \"created_at\": datetime.utcnow().isoformat(),\n            \"status\": \"active\"\n        }\n\n        self.slas[f\"{service_name}_{sla_name}\"] = sla\n        return sla\n\n    def calculate_error_budget(\n        self,\n        service_name: str,\n        slo_name: str,\n        period_days: int = 30\n    ) -> Dict[str, Any]:\n        \"\"\"Calculate error budget for an SLO\"\"\"\n        slo_key = f\"{service_name}_{slo_name}\"\n        if slo_key not in self.slos:\n            return {\"error\": \"SLO not found\"}\n\n        slo = self.slos[slo_key]\n        target_availability = slo[\"target\"]\n\n        # Calculate error budget\n        period_hours = period_days * 24\n        error_budget = (100 - target_availability) \u002F 100 * period_hours\n\n        return {\n            \"service\": service_name,\n            \"slo\": slo_name,\n            \"period_days\": period_days,\n            \"target_availability\": target_availability,\n            \"error_budget_hours\": error_budget,\n            \"error_budget_percentage\": 100 - target_availability,\n            \"remaining_error_budget\": error_budget,  # Would be calculated from actual metrics\n            \"burn_rate\": 0.0  # Would be calculated from actual metrics\n        }\n```\n\nThis comprehensive AIOps skill provides production-ready patterns for implementing AI-powered operations in 2025, including intelligent monitoring, anomaly detection, predictive analytics, automation, and self-healing capabilities.\n\n`★ Insight ─────────────────────────────────────`\nThe AIOps patterns shown here emphasize several key 2025 best practices:\n1. **Data Source Abstraction**: Generic interfaces allow integration with any monitoring platform (Prometheus, Datadog, New Relic, etc.)\n2. **ML-Driven Operations**: Anomaly detection, predictive analytics, and pattern recognition using proven ML models\n3. **Automation with Safety**: Retry logic, timeout handling, and condition checking ensure safe automated remediation\n4. **Observability First**: Unified platform approach to metrics, logs, and traces for comprehensive visibility\n5. **SLO-Driven**: Focus on business outcomes through Service Level Objectives and error budget management\n\nThese patterns ensure your AIOps implementation is intelligent, automated, and aligned with business objectives.\n`─────────────────────────────────────────────────`",{"name":5,"license":14,"description":7},"master","08cc2532077058aa50e6e18f54695bc256c88152","skills\u002Fazeem-2\u002Faiops","https:\u002F\u002Fgithub.com\u002Fsuperhealth\u002Fskills-hub","https:\u002F\u002Fgithub.com\u002Fsuperhealth","java engineer","2026-04-06 22:07:23",{"source":8,"skillName":5,"commands":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add superhealth\u002Fskills-hub --skill aiops",{"tool":60,"command":61},"bunx","bunx skills add superhealth\u002Fskills-hub --skill aiops",{"tool":63,"command":64},"pnpm","pnpm dlx skills add superhealth\u002Fskills-hub --skill aiops","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fsuperhealth\u002Fskills-hub\u002Fblob\u002Fmaster\u002Fskills\u002Fazeem-2\u002Faiops\u002FSKILL.md",true,[68],{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"skill-report.json","REFERENCE","参考文档",9859,"https:\u002F\u002Fgithub.com\u002Fsuperhealth\u002Fskills-hub\u002Fblob\u002Fmaster\u002Fskills\u002Fazeem-2\u002Faiops\u002Fskill-report.json",[75,103,130,143,159,183],{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":84,"publishedAt":42},"01M6MC9F3WHV8S8FZ2W4TRB0FE","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. 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