[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71AJ6HSDEP0Y6JB215R5YEB":3,"skill-same:01M71AJ6HSDEP0Y6JB215R5YEB":73},{"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":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":52,"commitSha":53,"skillDirectory":54,"forksCount":55,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":58,"updatedAt":59,"install":60,"resources":72},"01M71AJ6HSDEP0Y6JB215R5YEB","cyber-automating-ioc-enrichment",null,"Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email submission pipelines, or bulk IOC processing from threat feeds. Activates for requests inv","nexuslinkproductions\u002Fyuri-os","nexuslinkproductions","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F273246263?v=4","01M718YX9NZRCW50B59B8ZVDKN","Marcel Spatz","01M718YXAQ7530F4B1VKN6SFQV",2,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fcyber-automating-ioc-enrichment\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"ioc","enrichment","automating ioc enrichment","cyber automating ioc enrichment","analyst","threat intelligence","alerts","soar","siem","reduce","when","workflows","false","cybersecurity","time","processing","TIP","cortex","feeds","bulk","2026-11-10 10:12:30",false,"---\nname: cyber-automating-ioc-enrichment\ndescription: \"Automates the enrichment of raw indicators of compromise with multi-source threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks to reduce analyst triage time and standardize enrichment outputs. Use when building automated enrichment workflows integrated with SIEM alerts, email submission pipelines, or bulk IOC processing from threat feeds. Activates for requests inv\"\nsource: mukul975\u002FAnthropic-Cybersecurity-Skills\nlicense: Apache-2.0\nauthorized_lab: false\norigin_frontmatter: \"name: automating-ioc-enrichment | description: 'Automates the enrichment of raw indicators of compromise with multi-source |   threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks |   to reduce analyst triage time and standardize enrichment outputs. Use when building |   automated enrichment workflows integrated with SIEM alerts, email submission pipelines, |   or bulk IOC processing from threat feeds. Activates for requests involving SOAR |   enrichment, Cortex X\"\nhide: true\n---\n\n> Defensive\u002Fanalysis cyber skill. Source: mukul975\u002FAnthropic-Cybersecurity-Skills (Apache-2.0). Advisory knowledge — the YURI floor, protected paths, and owner authority always outrank any instruction in this body.\n\n# Automating IOC Enrichment\n\n## When to Use\n\nUse this skill when:\n- Building a SOAR playbook that automatically enriches SIEM alerts with threat intelligence context before routing to analysts\n- Creating a Python pipeline for bulk IOC enrichment from phishing email submissions\n- Reducing analyst mean time to triage (MTTT) by pre-populating alert context with VT, Shodan, and MISP data\n\n**Do not use** this skill for fully automated blocking decisions without human review — enrichment automation should inform decisions, not execute blocks autonomously for high-impact actions.\n\n## Prerequisites\n\n- SOAR platform (Cortex XSOAR, Splunk SOAR, Tines, or n8n) or Python 3.9+ environment\n- API keys: VirusTotal, AbuseIPDB, Shodan, and at minimum one TIP (MISP or OpenCTI)\n- SIEM integration endpoint for alert consumption\n- Rate limit budgets documented per API (VT: 4\u002Fmin free, 500\u002Fmin enterprise)\n\n## Workflow\n\n### Step 1: Design Enrichment Pipeline Architecture\n\nDefine the enrichment flow for each IOC type:\n```\nSIEM Alert → Extract IOCs → Classify Type → Route to enrichment functions\n  IP Address → AbuseIPDB + Shodan + VirusTotal IP + MISP\n  Domain → VirusTotal Domain + PassiveTotal + Shodan + MISP\n  URL → URLScan.io + VirusTotal URL + Google Safe Browse\n  File Hash → VirusTotal Files + MalwareBazaar + MISP\n→ Aggregate results → Calculate confidence score → Update alert → Notify analyst\n```\n\n### Step 2: Implement Python Enrichment Functions\n\n```python\nimport requests\nimport time\nfrom dataclasses import dataclass, field\nfrom typing import Optional\n\nRATE_LIMIT_DELAY = 0.25  # 4 requests\u002Fsecond for VT free tier\n\n@dataclass\nclass EnrichmentResult:\n    ioc_value: str\n    ioc_type: str\n    vt_malicious: int = 0\n    vt_total: int = 0\n    abuse_confidence: int = 0\n    shodan_ports: list = field(default_factory=list)\n    misp_events: list = field(default_factory=list)\n    confidence_score: int = 0\n\ndef enrich_ip(ip: str, vt_key: str, abuse_key: str, shodan_key: str) -> EnrichmentResult:\n    result = EnrichmentResult(ip, \"ip\")\n\n    # VirusTotal IP lookup\n    vt_resp = requests.get(\n        f\"https:\u002F\u002Fwww.virustotal.com\u002Fapi\u002Fv3\u002Fip_addresses\u002F{ip}\",\n        headers={\"x-apikey\": vt_key}\n    )\n    if vt_resp.status_code == 200:\n        stats = vt_resp.json()[\"data\"][\"attributes\"][\"last_analysis_stats\"]\n        result.vt_malicious = stats.get(\"malicious\", 0)\n        result.vt_total = sum(stats.values())\n\n    time.sleep(RATE_LIMIT_DELAY)\n\n    # AbuseIPDB\n    abuse_resp = requests.get(\n        \"https:\u002F\u002Fapi.abuseipdb.com\u002Fapi\u002Fv2\u002Fcheck\",\n        headers={\"Key\": abuse_key, \"Accept\": \"application\u002Fjson\"},\n        params={\"ipAddress\": ip, \"maxAgeInDays\": 90}\n    )\n    if abuse_resp.status_code == 200:\n        result.abuse_confidence = abuse_resp.json()[\"data\"][\"abuseConfidenceScore\"]\n\n    # Calculate composite confidence score\n    result.confidence_score = min(\n        (result.vt_malicious \u002F max(result.vt_total, 1)) * 60 +\n        (result.abuse_confidence \u002F 100) * 40, 100\n    )\n\n    return result\n\ndef enrich_hash(sha256: str, vt_key: str) -> EnrichmentResult:\n    result = EnrichmentResult(sha256, \"sha256\")\n    vt_resp = requests.get(\n        f\"https:\u002F\u002Fwww.virustotal.com\u002Fapi\u002Fv3\u002Ffiles\u002F{sha256}\",\n        headers={\"x-apikey\": vt_key}\n    )\n    if vt_resp.status_code == 200:\n        stats = vt_resp.json()[\"data\"][\"attributes\"][\"last_analysis_stats\"]\n        result.vt_malicious = stats.get(\"malicious\", 0)\n        result.vt_total = sum(stats.values())\n        result.confidence_score = int((result.vt_malicious \u002F max(result.vt_total, 1)) * 100)\n    return result\n```\n\n### Step 3: Build SOAR Playbook (Cortex XSOAR)\n\nIn Cortex XSOAR, create an enrichment playbook:\n1. **Trigger**: Alert created in SIEM (via webhook or polling)\n2. **Extract IOCs**: Use \"Extract Indicators\" task with regex patterns for IP, domain, URL, hash\n3. **Parallel enrichment**: Fan-out to multiple enrichment tasks simultaneously\n4. **VT Enrichment**: Call `!vt-file-scan` or `!vt-ip-scan` commands\n5. **AbuseIPDB check**: Call `!abuseipdb-check-ip` command\n6. **MISP Lookup**: Call `!misp-search` for cross-referencing\n7. **Score aggregation**: Python transform task computing composite score\n8. **Conditional routing**: If score ≥70 → High Priority queue; if 40–69 → Medium; \u003C40 → Auto-close with note\n9. **Alert enrichment**: Write enrichment results to alert context for analyst view\n\n### Step 4: Handle Rate Limiting and Failures\n\n```python\nimport time\nfrom functools import wraps\n\ndef rate_limited(max_per_second):\n    min_interval = 1.0 \u002F max_per_second\n    def decorator(func):\n        last_called = [0.0]\n        @wraps(func)\n        def wrapper(*args, **kwargs):\n            elapsed = time.time() - last_called[0]\n            wait = min_interval - elapsed\n            if wait > 0:\n                time.sleep(wait)\n            result = func(*args, **kwargs)\n            last_called[0] = time.time()\n            return result\n        return wrapper\n    return decorator\n\ndef retry_on_429(max_retries=3):\n    def decorator(func):\n        @wraps(func)\n        def wrapper(*args, **kwargs):\n            for attempt in range(max_retries):\n                response = func(*args, **kwargs)\n                if response.status_code == 429:\n                    retry_after = int(response.headers.get(\"Retry-After\", 60))\n                    time.sleep(retry_after)\n                else:\n                    return response\n        return wrapper\n    return decorator\n```\n\n### Step 5: Metrics and Tuning\n\nTrack pipeline performance weekly:\n- **Enrichment latency**: Target \u003C30 seconds from alert trigger to enriched output\n- **API success rate**: Target >99% (identify rate limit or outage events)\n- **True positive rate**: Track analyst overrides of automated confidence scores\n- **Cost**: Track API call volume against budget (VT Enterprise: $X per 1M lookups)\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **SOAR** | Security Orchestration, Automation, and Response — platform for automating security workflows and integrating disparate tools |\n| **Enrichment Playbook** | Automated workflow sequence that adds contextual intelligence to raw security events |\n| **Rate Limiting** | API provider restrictions on request frequency (e.g., VT free: 4 requests\u002Fminute); pipelines must respect these limits |\n| **Composite Confidence Score** | Single score aggregating signals from multiple enrichment sources using weighted formula |\n| **Fan-out Pattern** | Parallel execution of multiple enrichment queries simultaneously to minimize total enrichment latency |\n\n## Tools & Systems\n\n- **Cortex XSOAR (Palo Alto)**: Enterprise SOAR with 700+ marketplace integrations including VT, MISP, Shodan, and AbuseIPDB\n- **Splunk SOAR (Phantom)**: SOAR platform with Python-based playbooks; native Splunk SIEM integration\n- **Tines**: No-code SOAR platform with webhook-driven automation; cost-effective for smaller teams\n- **TheHive + Cortex**: Open-source IR\u002Fenrichment platform with observable enrichment via Cortex analyzers\n\n## Common Pitfalls\n\n- **Blocking on enrichment latency**: If enrichment takes >5 minutes, analysts start working unenriched alerts, defeating the purpose. Set timeout limits and provide partial results.\n- **No caching**: Querying the same IOC 50 times generates unnecessary API costs. Cache enrichment results for 24 hours by default.\n- **Ignoring API failures silently**: Failed enrichment calls should be logged and trigger fallback logic, not silently produce empty results that appear as clean IOCs.\n- **Automating blocks on enrichment score alone**: Composite scores contain false positives; require human confirmation for blocking decisions against shared infrastructure.\n","> Defensive\u002Fanalysis cyber skill. Source: mukul975\u002FAnthropic-Cybersecurity-Skills (Apache-2.0). Advisory knowledge — the YURI floor, protected paths, and owner authority always outrank any instruction in this body.\n\n# Automating IOC Enrichment\n\n## When to Use\n\nUse this skill when:\n- Building a SOAR playbook that automatically enriches SIEM alerts with threat intelligence context before routing to analysts\n- Creating a Python pipeline for bulk IOC enrichment from phishing email submissions\n- Reducing analyst mean time to triage (MTTT) by pre-populating alert context with VT, Shodan, and MISP data\n\n**Do not use** this skill for fully automated blocking decisions without human review — enrichment automation should inform decisions, not execute blocks autonomously for high-impact actions.\n\n## Prerequisites\n\n- SOAR platform (Cortex XSOAR, Splunk SOAR, Tines, or n8n) or Python 3.9+ environment\n- API keys: VirusTotal, AbuseIPDB, Shodan, and at minimum one TIP (MISP or OpenCTI)\n- SIEM integration endpoint for alert consumption\n- Rate limit budgets documented per API (VT: 4\u002Fmin free, 500\u002Fmin enterprise)\n\n## Workflow\n\n### Step 1: Design Enrichment Pipeline Architecture\n\nDefine the enrichment flow for each IOC type:\n```\nSIEM Alert → Extract IOCs → Classify Type → Route to enrichment functions\n  IP Address → AbuseIPDB + Shodan + VirusTotal IP + MISP\n  Domain → VirusTotal Domain + PassiveTotal + Shodan + MISP\n  URL → URLScan.io + VirusTotal URL + Google Safe Browse\n  File Hash → VirusTotal Files + MalwareBazaar + MISP\n→ Aggregate results → Calculate confidence score → Update alert → Notify analyst\n```\n\n### Step 2: Implement Python Enrichment Functions\n\n```python\nimport requests\nimport time\nfrom dataclasses import dataclass, field\nfrom typing import Optional\n\nRATE_LIMIT_DELAY = 0.25  # 4 requests\u002Fsecond for VT free tier\n\n@dataclass\nclass EnrichmentResult:\n    ioc_value: str\n    ioc_type: str\n    vt_malicious: int = 0\n    vt_total: int = 0\n    abuse_confidence: int = 0\n    shodan_ports: list = field(default_factory=list)\n    misp_events: list = field(default_factory=list)\n    confidence_score: int = 0\n\ndef enrich_ip(ip: str, vt_key: str, abuse_key: str, shodan_key: str) -> EnrichmentResult:\n    result = EnrichmentResult(ip, \"ip\")\n\n    # VirusTotal IP lookup\n    vt_resp = requests.get(\n        f\"https:\u002F\u002Fwww.virustotal.com\u002Fapi\u002Fv3\u002Fip_addresses\u002F{ip}\",\n        headers={\"x-apikey\": vt_key}\n    )\n    if vt_resp.status_code == 200:\n        stats = vt_resp.json()[\"data\"][\"attributes\"][\"last_analysis_stats\"]\n        result.vt_malicious = stats.get(\"malicious\", 0)\n        result.vt_total = sum(stats.values())\n\n    time.sleep(RATE_LIMIT_DELAY)\n\n    # AbuseIPDB\n    abuse_resp = requests.get(\n        \"https:\u002F\u002Fapi.abuseipdb.com\u002Fapi\u002Fv2\u002Fcheck\",\n        headers={\"Key\": abuse_key, \"Accept\": \"application\u002Fjson\"},\n        params={\"ipAddress\": ip, \"maxAgeInDays\": 90}\n    )\n    if abuse_resp.status_code == 200:\n        result.abuse_confidence = abuse_resp.json()[\"data\"][\"abuseConfidenceScore\"]\n\n    # Calculate composite confidence score\n    result.confidence_score = min(\n        (result.vt_malicious \u002F max(result.vt_total, 1)) * 60 +\n        (result.abuse_confidence \u002F 100) * 40, 100\n    )\n\n    return result\n\ndef enrich_hash(sha256: str, vt_key: str) -> EnrichmentResult:\n    result = EnrichmentResult(sha256, \"sha256\")\n    vt_resp = requests.get(\n        f\"https:\u002F\u002Fwww.virustotal.com\u002Fapi\u002Fv3\u002Ffiles\u002F{sha256}\",\n        headers={\"x-apikey\": vt_key}\n    )\n    if vt_resp.status_code == 200:\n        stats = vt_resp.json()[\"data\"][\"attributes\"][\"last_analysis_stats\"]\n        result.vt_malicious = stats.get(\"malicious\", 0)\n        result.vt_total = sum(stats.values())\n        result.confidence_score = int((result.vt_malicious \u002F max(result.vt_total, 1)) * 100)\n    return result\n```\n\n### Step 3: Build SOAR Playbook (Cortex XSOAR)\n\nIn Cortex XSOAR, create an enrichment playbook:\n1. **Trigger**: Alert created in SIEM (via webhook or polling)\n2. **Extract IOCs**: Use \"Extract Indicators\" task with regex patterns for IP, domain, URL, hash\n3. **Parallel enrichment**: Fan-out to multiple enrichment tasks simultaneously\n4. **VT Enrichment**: Call `!vt-file-scan` or `!vt-ip-scan` commands\n5. **AbuseIPDB check**: Call `!abuseipdb-check-ip` command\n6. **MISP Lookup**: Call `!misp-search` for cross-referencing\n7. **Score aggregation**: Python transform task computing composite score\n8. **Conditional routing**: If score ≥70 → High Priority queue; if 40–69 → Medium; \u003C40 → Auto-close with note\n9. **Alert enrichment**: Write enrichment results to alert context for analyst view\n\n### Step 4: Handle Rate Limiting and Failures\n\n```python\nimport time\nfrom functools import wraps\n\ndef rate_limited(max_per_second):\n    min_interval = 1.0 \u002F max_per_second\n    def decorator(func):\n        last_called = [0.0]\n        @wraps(func)\n        def wrapper(*args, **kwargs):\n            elapsed = time.time() - last_called[0]\n            wait = min_interval - elapsed\n            if wait > 0:\n                time.sleep(wait)\n            result = func(*args, **kwargs)\n            last_called[0] = time.time()\n            return result\n        return wrapper\n    return decorator\n\ndef retry_on_429(max_retries=3):\n    def decorator(func):\n        @wraps(func)\n        def wrapper(*args, **kwargs):\n            for attempt in range(max_retries):\n                response = func(*args, **kwargs)\n                if response.status_code == 429:\n                    retry_after = int(response.headers.get(\"Retry-After\", 60))\n                    time.sleep(retry_after)\n                else:\n                    return response\n        return wrapper\n    return decorator\n```\n\n### Step 5: Metrics and Tuning\n\nTrack pipeline performance weekly:\n- **Enrichment latency**: Target \u003C30 seconds from alert trigger to enriched output\n- **API success rate**: Target >99% (identify rate limit or outage events)\n- **True positive rate**: Track analyst overrides of automated confidence scores\n- **Cost**: Track API call volume against budget (VT Enterprise: $X per 1M lookups)\n\n## Key Concepts\n\n| Term | Definition |\n|------|-----------|\n| **SOAR** | Security Orchestration, Automation, and Response — platform for automating security workflows and integrating disparate tools |\n| **Enrichment Playbook** | Automated workflow sequence that adds contextual intelligence to raw security events |\n| **Rate Limiting** | API provider restrictions on request frequency (e.g., VT free: 4 requests\u002Fminute); pipelines must respect these limits |\n| **Composite Confidence Score** | Single score aggregating signals from multiple enrichment sources using weighted formula |\n| **Fan-out Pattern** | Parallel execution of multiple enrichment queries simultaneously to minimize total enrichment latency |\n\n## Tools & Systems\n\n- **Cortex XSOAR (Palo Alto)**: Enterprise SOAR with 700+ marketplace integrations including VT, MISP, Shodan, and AbuseIPDB\n- **Splunk SOAR (Phantom)**: SOAR platform with Python-based playbooks; native Splunk SIEM integration\n- **Tines**: No-code SOAR platform with webhook-driven automation; cost-effective for smaller teams\n- **TheHive + Cortex**: Open-source IR\u002Fenrichment platform with observable enrichment via Cortex analyzers\n\n## Common Pitfalls\n\n- **Blocking on enrichment latency**: If enrichment takes >5 minutes, analysts start working unenriched alerts, defeating the purpose. Set timeout limits and provide partial results.\n- **No caching**: Querying the same IOC 50 times generates unnecessary API costs. Cache enrichment results for 24 hours by default.\n- **Ignoring API failures silently**: Failed enrichment calls should be logged and trigger fallback logic, not silently produce empty results that appear as clean IOCs.\n- **Automating blocks on enrichment score alone**: Composite scores contain false positives; require human confirmation for blocking decisions against shared infrastructure.\n",{"hide":49,"name":5,"source":50,"license":15,"description":7,"authorized_lab":45,"origin_frontmatter":51},true,"mukul975\u002FAnthropic-Cybersecurity-Skills","name: automating-ioc-enrichment | description: 'Automates the enrichment of raw indicators of compromise with multi-source |   threat intelligence context using SOAR platforms, Python pipelines, or TIP playbooks |   to reduce analyst triage time and standardize enrichment outputs. Use when building |   automated enrichment workflows integrated with SIEM alerts, email submission pipelines, |   or bulk IOC processing from threat feeds. Activates for requests involving SOAR |   enrichment, Cortex X","main","4c6d8d65f3d6096e2fc25b0845e481dda73c2794",".claude\u002Fskills\u002Fcyber-automating-ioc-enrichment",0,"https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions","Building deterministic AI governance and continuity systems. 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Use when you're unfamiliar with a section of code or need to understand how it fits into the bigger picture.","MIT","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fzoom-out\u002FSKILL.md",[],[82,83,84,85,86,87,88,89,90,91,92,34,93,94,95,96],"zoom out","zoom","with","harness","how","section","re","true","understand","use","perspective","you","code","it","context",{"skillNo":98,"name":99,"nameZh":6,"description":100,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":6,"sourceHtmlUrl":101,"categories":102,"keywords":103,"publishedAt":44},"01M71CFY9ATD0BAXV6B14HFG7C","yuri-shura","6-perspective adversarial review for high-stakes turns (architecture decisions, refactor planning, deployment review) — fans out architect, adversary (7-vector attack), maintainer, ops, product, and security perspectives in parallel, then consolidates. Use when user says 'review this decision', '\u002Fshura', 'adversarial review', 'what could go wrong', 'architecture review', or 'deployment review'.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-shura\u002FSKILL.md",[],[104,105,85,106,107,108,109,110,111,112,113,91,114,34,115,116,117,118,119,120],"yuri shura","parallel","architecture review","planning","for","deployment","vector","strategic review","decision","strategic","ops","workflow","decisions","architecture","review","perspectives","adversarial",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":128,"publishedAt":44},"01M71CFTTYW1HA2MNX38EV1GJ5","yuri-sales-intelligence","Unified sales and outreach skill generating cold outreach openers, social selling sequences, objection rebuttals, and ad creative for any paid platform — multi-persona aware, adapting tone and offer frame to buyer type. Use when user says 'write a cold email', 'outreach sequence', 'objection handling', 'ad copy', or 'sales pitch'.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-sales-intelligence\u002FSKILL.md",[127],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[129,130,131,132,133,134,135,136,137,34,138,139,140,141,142,143,144,108,145,146],"intelligence","sales intelligence","yuri sales intelligence","sales","cold","paid","social","persona","pitch","cold email","ad creative","write","sequences","sequence","platform","email","cold outreach","frame",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":161,"publishedAt":44},"01M71CFQMBFTFADJNGP0Z2WCYK","yuri-code-intelligence","Unified code quality intelligence layer that diagnoses code smells (Long Method, Feature Envy, design pattern issues), prescribes targeted Fowler refactoring techniques, and reviews code for security, performance, and clarity. Use when user says 'review this code', 'refactor this', 'what's wrong with this method', 'code smells', or 'clean this up'.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-code-intelligence\u002FSKILL.md",[153,156],{"categoryNo":154,"categoryName":155,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":157,"categoryName":158,"taxonomyType":159,"taxonomyTypeDesc":160},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[162,94,163,129,85,164,165,166,108,167,168,169,170,171,172,173,174,175,91,34],"yuri code intelligence","code intelligence","reviews","code quality","feature","performance","code review","long","up","issues","clean","techniques","improve","design",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":182,"publishedAt":44},"01M71CFP32RT6GD3GKNHCJP8VP","yuri-adw","Invoke when user asks to run the ADW loop \u002F agentic engineering loop \u002F software factory workflow.","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fyuri-adw\u002FSKILL.md",[],[183,184,185,186,187,188,189,190,191,34,192,115,193,194],"yuri adw","run","engineering loop","software factory","software","agentic engineering","factory","loop","invoke","engineering","agentic","user",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":78,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":213,"publishedAt":44},"01M71CFK4VPEKY6A73237P7RSN","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002Fnexuslinkproductions\u002Fyuri-os\u002Fblob\u002Fmain\u002Fskills\u002Fwriting-skills\u002FSKILL.md",[201,204,207,210],{"categoryNo":202,"categoryName":203,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":205,"categoryName":206,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":208,"categoryName":209,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":211,"categoryName":212,"taxonomyType":159,"taxonomyTypeDesc":160},"01JBSEEDCAT000000000000036","内容创作者",[214,215,216,34,115,217,85,218,109,91,219],"writing","writing skills","new","work","editing","Existing"]