[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6HAHXMWR6RX07R5VZD7GKPR":3,"skill-same:01M6HAHXMWR6RX07R5VZD7GKPR":55},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":26,"linkOnly":27,"markdownContent":28,"markdownBody":29,"frontMatter":30,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":14,"repositoryHtmlUrl":36,"repositoryHomepageUrl":6,"ownerHtmlUrl":37,"ownerBlogUrl":38,"ownerDescription":39,"updatedAt":40,"install":41,"resources":54},"01M6HAHXMWR6RX07R5VZD7GKPR","risk-assessment",null,"Security risk identification and analysis","NeuralBlitz\u002FMito","NeuralBlitz","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F79354388?v=4","01M6H8T2TFH477HRS0KTSTKR2E","David Riggs","01M6H8T2YQQDXPYM092P670Z21",0,"MIT","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito\u002Fblob\u002Fmain\u002F.opencode\u002Fskills\u002Frisk-assessment\u002FSKILL.md",[],[19,20,21,22,23,24,25],"risk assessment","assessment","risk","security","analysis","opencode","identification","2026-11-10 10:12:30",false,"---\nname: risk-assessment\ndescription: Security risk identification and analysis\nlicense: MIT\ncompatibility: opencode\nmetadata:\n  audience: developers\n  category: security\n---\n\n## What I do\n- Identify and categorize security risks\n- Assess likelihood and impact of threats\n- Prioritize risks for remediation\n- Recommend mitigation strategies\n- Track risk over time\n- Quantify risk in business terms\n\n## When to use me\nWhen evaluating security posture, planning security improvements, or conducting risk assessments for compliance.\n\n## Risk Assessment Framework\n\n### Risk Model\n```python\nfrom enum import Enum\nfrom datetime import datetime\n\nclass Likelihood(Enum):\n    RARE = 1\n    UNLIKELY = 2\n    POSSIBLE = 3\n    LIKELY = 4\n    CERTAIN = 5\n\nclass Impact(Enum):\n    NEGLIGIBLE = 1\n    MINOR = 2\n    MODERATE = 3\n    MAJOR = 4\n    CATASTROPHIC = 5\n\nclass RiskLevel(Enum):\n    LOW = \"low\"\n    MEDIUM = \"medium\"\n    HIGH = \"high\"\n    CRITICAL = \"critical\"\n\nclass Risk:\n    def __init__(self, id: str, name: str, description: str):\n        self.id = id\n        self.name = name\n        self.description = description\n        self.likelihood = None\n        self.impact = None\n        self.current_controls = []\n        self.residual_risk = None\n        self.treated_risk = None\n    \n    def calculate_risk_level(self) -> RiskLevel:\n        score = self.likelihood.value * self.impact.value\n        \n        if score >= 16:\n            return RiskLevel.CRITICAL\n        elif score >= 10:\n            return RiskLevel.HIGH\n        elif score >= 5:\n            return RiskLevel.MEDIUM\n        else:\n            return RiskLevel.LOW\n    \n    def calculate_residual_risk(self) -> int:\n        \"\"\"Risk after current controls\"\"\"\n        base_score = self.likelihood.value * self.impact.value\n        control_effectiveness = sum(c.effectiveness for c in self.current_controls)\n        reduction = min(control_effectiveness * 2, 80)  # Max 80% reduction\n        return int(base_score * (100 - reduction) \u002F 100)\n```\n\n### Risk Assessment Process\n```python\nclass RiskAssessment:\n    def __init__(self, name: str):\n        self.name = name\n        self.created_at = datetime.now()\n        self.risks = []\n        self.assets = []\n    \n    def add_asset(self, asset_id: str, name: str, value: str, \n                  classification: str):\n        \"\"\"Register an asset to protect\"\"\"\n        self.assets.append({\n            \"id\": asset_id,\n            \"name\": name,\n            \"value\": value,\n            \"classification\": classification,\n            \"dependencies\": []\n        })\n    \n    def identify_risks(self) -> list:\n        \"\"\"Systematic risk identification\"\"\"\n        risk_categories = [\n            \"Physical Security\",\n            \"Network Security\",\n            \"Application Security\",\n            \"Data Security\",\n            \"Operational Security\",\n            \"Business Continuity\"\n        ]\n        \n        for category in risk_categories:\n            risks = self._identify_category_risks(category)\n            self.risks.extend(risks)\n        \n        return self.risks\n    \n    def _identify_category_risks(self, category: str) -> list:\n        \"\"\"Category-specific risk identification\"\"\"\n        templates = {\n            \"Application Security\": [\n                (\"R01\", \"SQL Injection\", \"Unsanitized database queries\"),\n                (\"R02\", \"XSS Attack\", \"Unescaped user output\"),\n                (\"R03\", \"CSRF\", \"Missing token validation\"),\n                (\"R04\", \"Authentication Bypass\", \"Weak auth mechanisms\"),\n            ],\n            \"Data Security\": [\n                (\"R05\", \"Data Breach\", \"Unauthorized data access\"),\n                (\"R06\", \"Data Loss\", \"Accidental or malicious deletion\"),\n                (\"R07\", \"Data Exfiltration\", \"Unauthorized data transfer\"),\n            ]\n        }\n        \n        risks = []\n        for risk_id, name, desc in templates.get(category, []):\n            risk = Risk(risk_id, name, desc)\n            risks.append(risk)\n        \n        return risks\n    \n    def assess_risk(self, risk_id: str, likelihood: Likelihood, \n                    impact: Impact):\n        \"\"\"Assess individual risk\"\"\"\n        for risk in self.risks:\n            if risk.id == risk_id:\n                risk.likelihood = likelihood\n                risk.impact = impact\n                risk.residual_risk = risk.calculate_residual_risk()\n                return risk\n        \n        raise ValueError(f\"Risk {risk_id} not found\")\n    \n    def prioritize_risks(self) -> list:\n        \"\"\"Sort risks by severity\"\"\"\n        return sorted(self.risks, \n                     key=lambda r: r.calculate_risk_level().value,\n                     reverse=True)\n```\n\n### Risk Treatment\n```python\nclass RiskTreatment:\n    def __init__(self):\n        self.treatments = []\n    \n    def recommend_treatment(self, risk: Risk) -> dict:\n        \"\"\"Recommend mitigation strategy\"\"\"\n        risk_level = risk.calculate_risk_level()\n        \n        if risk_level == RiskLevel.CRITICAL:\n            treatment = \"mitigate\"\n            timeline = \"immediate\"\n            options = [\n                \"Implement preventive controls\",\n                \"Deploy detection mechanisms\",\n                \"Transfer risk through insurance\"\n            ]\n        elif risk_level == RiskLevel.HIGH:\n            treatment = \"mitigate\"\n            timeline = \"within 30 days\"\n            options = [\n                \"Enhance existing controls\",\n                \"Add monitoring\",\n                \"Implement compensating controls\"\n            ]\n        elif risk_level == RiskLevel.MEDIUM:\n            treatment = \"accept\"  # with monitoring\n            timeline = \"within 90 days\"\n            options = [\n                \"Accept with monitoring\",\n                \"Implement partial mitigation\"\n            ]\n        else:\n            treatment = \"accept\"\n            timeline = \"as scheduled\"\n            options = [\"Accept risk\"]\n        \n        return {\n            \"risk_id\": risk.id,\n            \"recommended_treatment\": treatment,\n            \"timeline\": timeline,\n            \"options\": options,\n            \"cost_benefit\": self._calculate_cost_benefit(risk, options)\n        }\n    \n    def _calculate_cost_benefit(self, risk: Risk, \n                                options: list) -> dict:\n        \"\"\"Analyze cost vs benefit of treatment\"\"\"\n        annual_loss_expectancy = self._calculate_ALE(risk)\n        \n        return {\n            \"annual_loss_expectancy\": annual_loss_expectancy,\n            \"mitigation_cost\": 0,  # Calculate actual cost\n            \"roi\": 0  # Return on investment\n        }\n    \n    def _calculate_ALE(self, risk: Risk) -> int:\n        \"\"\"Annual Loss Expectancy = Likelihood * Impact\"\"\"\n        return risk.likelihood.value * risk.impact.value * 10000\n```\n\n### Continuous Risk Monitoring\n```python\nclass RiskMonitor:\n    def __init__(self):\n        self.risk_register = {}\n    \n    def track_risk(self, risk: Risk):\n        \"\"\"Track risk over time\"\"\"\n        if risk.id not in self.risk_register:\n            self.risk_register[risk.id] = []\n        \n        self.risk_register[risk.id].append({\n            \"timestamp\": datetime.now(),\n            \"likelihood\": risk.likelihood,\n            \"impact\": risk.impact,\n            \"residual_risk\": risk.residual_risk,\n            \"controls\": risk.current_controls\n        })\n    \n    def detect_risk_change(self, risk_id: str) -> dict:\n        \"\"\"Detect significant risk changes\"\"\"\n        history = self.risk_register.get(risk_id, [])\n        if len(history) \u003C 2:\n            return {\"changed\": False}\n        \n        current = history[-1]\n        previous = history[-2]\n        \n        if current[\"residual_risk\"] > previous[\"residual_risk\"] * 1.2:\n            return {\n                \"changed\": True,\n                \"direction\": \"increasing\",\n                \"delta\": current[\"residual_risk\"] - previous[\"residual_risk\"]\n            }\n        \n        return {\"changed\": False}\n    \n    def generate_risk_report(self) -> dict:\n        \"\"\"Generate risk status report\"\"\"\n        return {\n            \"total_risks\": len(self.risk_register),\n            \"critical_risks\": 0,\n            \"high_risks\": 0,\n            \"risk_trends\": \"stable\",\n            \"recommendations\": []\n        }\n```\n","## What I do\n- Identify and categorize security risks\n- Assess likelihood and impact of threats\n- Prioritize risks for remediation\n- Recommend mitigation strategies\n- Track risk over time\n- Quantify risk in business terms\n\n## When to use me\nWhen evaluating security posture, planning security improvements, or conducting risk assessments for compliance.\n\n## Risk Assessment Framework\n\n### Risk Model\n```python\nfrom enum import Enum\nfrom datetime import datetime\n\nclass Likelihood(Enum):\n    RARE = 1\n    UNLIKELY = 2\n    POSSIBLE = 3\n    LIKELY = 4\n    CERTAIN = 5\n\nclass Impact(Enum):\n    NEGLIGIBLE = 1\n    MINOR = 2\n    MODERATE = 3\n    MAJOR = 4\n    CATASTROPHIC = 5\n\nclass RiskLevel(Enum):\n    LOW = \"low\"\n    MEDIUM = \"medium\"\n    HIGH = \"high\"\n    CRITICAL = \"critical\"\n\nclass Risk:\n    def __init__(self, id: str, name: str, description: str):\n        self.id = id\n        self.name = name\n        self.description = description\n        self.likelihood = None\n        self.impact = None\n        self.current_controls = []\n        self.residual_risk = None\n        self.treated_risk = None\n    \n    def calculate_risk_level(self) -> RiskLevel:\n        score = self.likelihood.value * self.impact.value\n        \n        if score >= 16:\n            return RiskLevel.CRITICAL\n        elif score >= 10:\n            return RiskLevel.HIGH\n        elif score >= 5:\n            return RiskLevel.MEDIUM\n        else:\n            return RiskLevel.LOW\n    \n    def calculate_residual_risk(self) -> int:\n        \"\"\"Risk after current controls\"\"\"\n        base_score = self.likelihood.value * self.impact.value\n        control_effectiveness = sum(c.effectiveness for c in self.current_controls)\n        reduction = min(control_effectiveness * 2, 80)  # Max 80% reduction\n        return int(base_score * (100 - reduction) \u002F 100)\n```\n\n### Risk Assessment Process\n```python\nclass RiskAssessment:\n    def __init__(self, name: str):\n        self.name = name\n        self.created_at = datetime.now()\n        self.risks = []\n        self.assets = []\n    \n    def add_asset(self, asset_id: str, name: str, value: str, \n                  classification: str):\n        \"\"\"Register an asset to protect\"\"\"\n        self.assets.append({\n            \"id\": asset_id,\n            \"name\": name,\n            \"value\": value,\n            \"classification\": classification,\n            \"dependencies\": []\n        })\n    \n    def identify_risks(self) -> list:\n        \"\"\"Systematic risk identification\"\"\"\n        risk_categories = [\n            \"Physical Security\",\n            \"Network Security\",\n            \"Application Security\",\n            \"Data Security\",\n            \"Operational Security\",\n            \"Business Continuity\"\n        ]\n        \n        for category in risk_categories:\n            risks = self._identify_category_risks(category)\n            self.risks.extend(risks)\n        \n        return self.risks\n    \n    def _identify_category_risks(self, category: str) -> list:\n        \"\"\"Category-specific risk identification\"\"\"\n        templates = {\n            \"Application Security\": [\n                (\"R01\", \"SQL Injection\", \"Unsanitized database queries\"),\n                (\"R02\", \"XSS Attack\", \"Unescaped user output\"),\n                (\"R03\", \"CSRF\", \"Missing token validation\"),\n                (\"R04\", \"Authentication Bypass\", \"Weak auth mechanisms\"),\n            ],\n            \"Data Security\": [\n                (\"R05\", \"Data Breach\", \"Unauthorized data access\"),\n                (\"R06\", \"Data Loss\", \"Accidental or malicious deletion\"),\n                (\"R07\", \"Data Exfiltration\", \"Unauthorized data transfer\"),\n            ]\n        }\n        \n        risks = []\n        for risk_id, name, desc in templates.get(category, []):\n            risk = Risk(risk_id, name, desc)\n            risks.append(risk)\n        \n        return risks\n    \n    def assess_risk(self, risk_id: str, likelihood: Likelihood, \n                    impact: Impact):\n        \"\"\"Assess individual risk\"\"\"\n        for risk in self.risks:\n            if risk.id == risk_id:\n                risk.likelihood = likelihood\n                risk.impact = impact\n                risk.residual_risk = risk.calculate_residual_risk()\n                return risk\n        \n        raise ValueError(f\"Risk {risk_id} not found\")\n    \n    def prioritize_risks(self) -> list:\n        \"\"\"Sort risks by severity\"\"\"\n        return sorted(self.risks, \n                     key=lambda r: r.calculate_risk_level().value,\n                     reverse=True)\n```\n\n### Risk Treatment\n```python\nclass RiskTreatment:\n    def __init__(self):\n        self.treatments = []\n    \n    def recommend_treatment(self, risk: Risk) -> dict:\n        \"\"\"Recommend mitigation strategy\"\"\"\n        risk_level = risk.calculate_risk_level()\n        \n        if risk_level == RiskLevel.CRITICAL:\n            treatment = \"mitigate\"\n            timeline = \"immediate\"\n            options = [\n                \"Implement preventive controls\",\n                \"Deploy detection mechanisms\",\n                \"Transfer risk through insurance\"\n            ]\n        elif risk_level == RiskLevel.HIGH:\n            treatment = \"mitigate\"\n            timeline = \"within 30 days\"\n            options = [\n                \"Enhance existing controls\",\n                \"Add monitoring\",\n                \"Implement compensating controls\"\n            ]\n        elif risk_level == RiskLevel.MEDIUM:\n            treatment = \"accept\"  # with monitoring\n            timeline = \"within 90 days\"\n            options = [\n                \"Accept with monitoring\",\n                \"Implement partial mitigation\"\n            ]\n        else:\n            treatment = \"accept\"\n            timeline = \"as scheduled\"\n            options = [\"Accept risk\"]\n        \n        return {\n            \"risk_id\": risk.id,\n            \"recommended_treatment\": treatment,\n            \"timeline\": timeline,\n            \"options\": options,\n            \"cost_benefit\": self._calculate_cost_benefit(risk, options)\n        }\n    \n    def _calculate_cost_benefit(self, risk: Risk, \n                                options: list) -> dict:\n        \"\"\"Analyze cost vs benefit of treatment\"\"\"\n        annual_loss_expectancy = self._calculate_ALE(risk)\n        \n        return {\n            \"annual_loss_expectancy\": annual_loss_expectancy,\n            \"mitigation_cost\": 0,  # Calculate actual cost\n            \"roi\": 0  # Return on investment\n        }\n    \n    def _calculate_ALE(self, risk: Risk) -> int:\n        \"\"\"Annual Loss Expectancy = Likelihood * Impact\"\"\"\n        return risk.likelihood.value * risk.impact.value * 10000\n```\n\n### Continuous Risk Monitoring\n```python\nclass RiskMonitor:\n    def __init__(self):\n        self.risk_register = {}\n    \n    def track_risk(self, risk: Risk):\n        \"\"\"Track risk over time\"\"\"\n        if risk.id not in self.risk_register:\n            self.risk_register[risk.id] = []\n        \n        self.risk_register[risk.id].append({\n            \"timestamp\": datetime.now(),\n            \"likelihood\": risk.likelihood,\n            \"impact\": risk.impact,\n            \"residual_risk\": risk.residual_risk,\n            \"controls\": risk.current_controls\n        })\n    \n    def detect_risk_change(self, risk_id: str) -> dict:\n        \"\"\"Detect significant risk changes\"\"\"\n        history = self.risk_register.get(risk_id, [])\n        if len(history) \u003C 2:\n            return {\"changed\": False}\n        \n        current = history[-1]\n        previous = history[-2]\n        \n        if current[\"residual_risk\"] > previous[\"residual_risk\"] * 1.2:\n            return {\n                \"changed\": True,\n                \"direction\": \"increasing\",\n                \"delta\": current[\"residual_risk\"] - previous[\"residual_risk\"]\n            }\n        \n        return {\"changed\": False}\n    \n    def generate_risk_report(self) -> dict:\n        \"\"\"Generate risk status report\"\"\"\n        return {\n            \"total_risks\": len(self.risk_register),\n            \"critical_risks\": 0,\n            \"high_risks\": 0,\n            \"risk_trends\": \"stable\",\n            \"recommendations\": []\n        }\n```\n",{"name":5,"license":15,"metadata":31,"description":7,"compatibility":24},{"audience":32,"category":22},"developers","main","2ea764e153842704d24a2e9aa4170441a7f03081",".opencode\u002Fskills\u002Frisk-assessment","https:\u002F\u002Fgithub.com\u002FNeuralBlitz\u002FMito","https:\u002F\u002Fgithub.com\u002FNeuralBlitz","nuralnexus.org","Independent Multidisciplinary Researcher, Creative Producer, and AI Systems Architect\r\nAn independent creator and research-driven systems designer.","2026-11-12 03:13:05",{"source":8,"skillName":5,"commands":42,"prompt":52,"zipAvailable":53},[43,46,49],{"tool":44,"command":45},"npx","npx skills add NeuralBlitz\u002FMito --skill risk-assessment",{"tool":47,"command":48},"bunx","bunx skills add NeuralBlitz\u002FMito --skill risk-assessment",{"tool":50,"command":51},"pnpm","pnpm dlx skills add NeuralBlitz\u002FMito --skill 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