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This skill covers collecting and weighting attr","luokai0\u002Fai-agent-skills-by-luo-kai","luokai0","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F266954973?v=4","01M6E16VNT42JB8B3X3254QWJ7","luo kai","01M6E16VTFVP6S5XJF1V1MPV90",14,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F21-external-registries\u002F08-pinkpixel-collection\u002Fanalyzing-campaign-attribution-evidence\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000019","营销推广","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"campaign attribution","campaign","evidence","attribution","analyzing campaign attribution evidence","analyzing","operation","cybersecurity","intelligence","threat","analysis","campaign-analysis","stix","ioc","cti","threat-intelligence","mitre-attack","2026-11-10 10:12:30",false,"---\nname: analyzing-campaign-attribution-evidence\ndescription: Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr\ndomain: cybersecurity\nsubdomain: threat-intelligence\ntags: [threat-intelligence, cti, ioc, mitre-attack, stix, attribution, campaign-analysis]\nversion: \"1.0\"\nauthor: mahipal\nlicense: Apache-2.0\n---\n# Analyzing Campaign Attribution Evidence\n\n## Overview\n\nCampaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attribution indicators using the Diamond Model and ACH (Analysis of Competing Hypotheses), analyzing infrastructure overlaps, TTP consistency, malware code similarities, operational timing patterns, and language artifacts to build confidence-weighted attribution assessments.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing campaign attribution evidence\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Python 3.9+ with `attackcti`, `stix2`, `networkx` libraries\n- Access to threat intelligence platforms (MISP, OpenCTI)\n- Understanding of Diamond Model of Intrusion Analysis\n- Familiarity with MITRE ATT&CK threat group profiles\n- Knowledge of malware analysis and infrastructure tracking techniques\n\n## Key Concepts\n\n### Attribution Evidence Categories\n1. **Infrastructure Overlap**: Shared C2 servers, domains, IP ranges, hosting providers\n2. **TTP Consistency**: Matching ATT&CK techniques and sub-techniques across campaigns\n3. **Malware Code Similarity**: Shared code bases, compilers, PDB paths, encryption routines\n4. **Operational Patterns**: Timing (working hours, time zones), targeting patterns, operational tempo\n5. **Language Artifacts**: Embedded strings, variable names, error messages in specific languages\n6. **Victimology**: Target sector, geography, and organizational profile consistency\n\n### Confidence Levels\n- **High Confidence**: Multiple independent evidence categories converge on same actor\n- **Moderate Confidence**: Several evidence categories match, some ambiguity remains\n- **Low Confidence**: Limited evidence, possible false flags or shared tooling\n\n### Analysis of Competing Hypotheses (ACH)\nStructured analytical method that evaluates evidence against multiple competing hypotheses. Each piece of evidence is scored as consistent, inconsistent, or neutral with respect to each hypothesis. The hypothesis with the least inconsistent evidence is favored.\n\n## Workflow\n\n### Step 1: Collect Attribution Evidence\n\n```python\nfrom stix2 import MemoryStore, Filter\nfrom collections import defaultdict\n\nclass AttributionAnalyzer:\n    def __init__(self):\n        self.evidence = []\n        self.hypotheses = {}\n\n    def add_evidence(self, category, description, value, confidence):\n        self.evidence.append({\n            \"category\": category,\n            \"description\": description,\n            \"value\": value,\n            \"confidence\": confidence,\n            \"timestamp\": None,\n        })\n\n    def add_hypothesis(self, actor_name, actor_id=\"\"):\n        self.hypotheses[actor_name] = {\n            \"actor_id\": actor_id,\n            \"consistent_evidence\": [],\n            \"inconsistent_evidence\": [],\n            \"neutral_evidence\": [],\n            \"score\": 0,\n        }\n\n    def evaluate_evidence(self, evidence_idx, actor_name, assessment):\n        \"\"\"Assess evidence against a hypothesis: consistent\u002Finconsistent\u002Fneutral.\"\"\"\n        if assessment == \"consistent\":\n            self.hypotheses[actor_name][\"consistent_evidence\"].append(evidence_idx)\n            self.hypotheses[actor_name][\"score\"] += self.evidence[evidence_idx][\"confidence\"]\n        elif assessment == \"inconsistent\":\n            self.hypotheses[actor_name][\"inconsistent_evidence\"].append(evidence_idx)\n            self.hypotheses[actor_name][\"score\"] -= self.evidence[evidence_idx][\"confidence\"] * 2\n        else:\n            self.hypotheses[actor_name][\"neutral_evidence\"].append(evidence_idx)\n\n    def rank_hypotheses(self):\n        \"\"\"Rank hypotheses by attribution score.\"\"\"\n        ranked = sorted(\n            self.hypotheses.items(),\n            key=lambda x: x[1][\"score\"],\n            reverse=True,\n        )\n        return [\n            {\n                \"actor\": name,\n                \"score\": data[\"score\"],\n                \"consistent\": len(data[\"consistent_evidence\"]),\n                \"inconsistent\": len(data[\"inconsistent_evidence\"]),\n                \"confidence\": self._score_to_confidence(data[\"score\"]),\n            }\n            for name, data in ranked\n        ]\n\n    def _score_to_confidence(self, score):\n        if score >= 80:\n            return \"HIGH\"\n        elif score >= 40:\n            return \"MODERATE\"\n        else:\n            return \"LOW\"\n```\n\n### Step 2: Infrastructure Overlap Analysis\n\n```python\ndef analyze_infrastructure_overlap(campaign_a_infra, campaign_b_infra):\n    \"\"\"Compare infrastructure between two campaigns for attribution.\"\"\"\n    overlap = {\n        \"shared_ips\": set(campaign_a_infra.get(\"ips\", [])).intersection(\n            campaign_b_infra.get(\"ips\", [])\n        ),\n        \"shared_domains\": set(campaign_a_infra.get(\"domains\", [])).intersection(\n            campaign_b_infra.get(\"domains\", [])\n        ),\n        \"shared_asns\": set(campaign_a_infra.get(\"asns\", [])).intersection(\n            campaign_b_infra.get(\"asns\", [])\n        ),\n        \"shared_registrars\": set(campaign_a_infra.get(\"registrars\", [])).intersection(\n            campaign_b_infra.get(\"registrars\", [])\n        ),\n    }\n\n    overlap_score = 0\n    if overlap[\"shared_ips\"]:\n        overlap_score += 30\n    if overlap[\"shared_domains\"]:\n        overlap_score += 25\n    if overlap[\"shared_asns\"]:\n        overlap_score += 15\n    if overlap[\"shared_registrars\"]:\n        overlap_score += 10\n\n    return {\n        \"overlap\": {k: list(v) for k, v in overlap.items()},\n        \"overlap_score\": overlap_score,\n        \"assessment\": \"STRONG\" if overlap_score >= 40 else \"MODERATE\" if overlap_score >= 20 else \"WEAK\",\n    }\n```\n\n### Step 3: TTP Comparison Across Campaigns\n\n```python\nfrom attackcti import attack_client\n\ndef compare_campaign_ttps(campaign_techniques, known_actor_techniques):\n    \"\"\"Compare campaign TTPs against known threat actor profiles.\"\"\"\n    campaign_set = set(campaign_techniques)\n    actor_set = set(known_actor_techniques)\n\n    common = campaign_set.intersection(actor_set)\n    unique_campaign = campaign_set - actor_set\n    unique_actor = actor_set - campaign_set\n\n    jaccard = len(common) \u002F len(campaign_set.union(actor_set)) if campaign_set.union(actor_set) else 0\n\n    return {\n        \"common_techniques\": sorted(common),\n        \"common_count\": len(common),\n        \"unique_to_campaign\": sorted(unique_campaign),\n        \"unique_to_actor\": sorted(unique_actor),\n        \"jaccard_similarity\": round(jaccard, 3),\n        \"overlap_percentage\": round(len(common) \u002F len(campaign_set) * 100, 1) if campaign_set else 0,\n    }\n```\n\n### Step 4: Generate Attribution Report\n\n```python\ndef generate_attribution_report(analyzer):\n    \"\"\"Generate structured attribution assessment report.\"\"\"\n    rankings = analyzer.rank_hypotheses()\n\n    report = {\n        \"assessment_date\": \"2026-02-23\",\n        \"total_evidence_items\": len(analyzer.evidence),\n        \"hypotheses_evaluated\": len(analyzer.hypotheses),\n        \"rankings\": rankings,\n        \"primary_attribution\": rankings[0] if rankings else None,\n        \"evidence_summary\": [\n            {\n                \"index\": i,\n                \"category\": e[\"category\"],\n                \"description\": e[\"description\"],\n                \"confidence\": e[\"confidence\"],\n            }\n            for i, e in enumerate(analyzer.evidence)\n        ],\n    }\n\n    return report\n```\n\n## Validation Criteria\n\n- Evidence collection covers all six attribution categories\n- ACH matrix properly evaluates evidence against competing hypotheses\n- Infrastructure overlap analysis identifies shared indicators\n- TTP comparison uses ATT&CK technique IDs for precision\n- Attribution confidence levels are properly justified\n- Report includes alternative hypotheses and false flag considerations\n\n## References\n\n- [Diamond Model of Intrusion Analysis](https:\u002F\u002Fwww.activeresponse.org\u002Fwp-content\u002Fuploads\u002F2013\u002F07\u002Fdiamond.pdf)\n- [MITRE ATT&CK Groups](https:\u002F\u002Fattack.mitre.org\u002Fgroups\u002F)\n- [Analysis of Competing Hypotheses](https:\u002F\u002Fwww.cia.gov\u002Fstatic\u002F9a5f1162fd0932c29e985f0159f56c07\u002FTradecraft-Primer-apr09.pdf)\n- [Threat Attribution Framework](https:\u002F\u002Fwww.mandiant.com\u002Fresources\u002Freports)\n","# Analyzing Campaign Attribution Evidence\n\n## Overview\n\nCampaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attribution indicators using the Diamond Model and ACH (Analysis of Competing Hypotheses), analyzing infrastructure overlaps, TTP consistency, malware code similarities, operational timing patterns, and language artifacts to build confidence-weighted attribution assessments.\n\n\n## When to Use\n\n- When investigating security incidents that require analyzing campaign attribution evidence\n- When building detection rules or threat hunting queries for this domain\n- When SOC analysts need structured procedures for this analysis type\n- When validating security monitoring coverage for related attack techniques\n\n## Prerequisites\n\n- Python 3.9+ with `attackcti`, `stix2`, `networkx` libraries\n- Access to threat intelligence platforms (MISP, OpenCTI)\n- Understanding of Diamond Model of Intrusion Analysis\n- Familiarity with MITRE ATT&CK threat group profiles\n- Knowledge of malware analysis and infrastructure tracking techniques\n\n## Key Concepts\n\n### Attribution Evidence Categories\n1. **Infrastructure Overlap**: Shared C2 servers, domains, IP ranges, hosting providers\n2. **TTP Consistency**: Matching ATT&CK techniques and sub-techniques across campaigns\n3. **Malware Code Similarity**: Shared code bases, compilers, PDB paths, encryption routines\n4. **Operational Patterns**: Timing (working hours, time zones), targeting patterns, operational tempo\n5. **Language Artifacts**: Embedded strings, variable names, error messages in specific languages\n6. **Victimology**: Target sector, geography, and organizational profile consistency\n\n### Confidence Levels\n- **High Confidence**: Multiple independent evidence categories converge on same actor\n- **Moderate Confidence**: Several evidence categories match, some ambiguity remains\n- **Low Confidence**: Limited evidence, possible false flags or shared tooling\n\n### Analysis of Competing Hypotheses (ACH)\nStructured analytical method that evaluates evidence against multiple competing hypotheses. Each piece of evidence is scored as consistent, inconsistent, or neutral with respect to each hypothesis. The hypothesis with the least inconsistent evidence is favored.\n\n## Workflow\n\n### Step 1: Collect Attribution Evidence\n\n```python\nfrom stix2 import MemoryStore, Filter\nfrom collections import defaultdict\n\nclass AttributionAnalyzer:\n    def __init__(self):\n        self.evidence = []\n        self.hypotheses = {}\n\n    def add_evidence(self, category, description, value, confidence):\n        self.evidence.append({\n            \"category\": category,\n            \"description\": description,\n            \"value\": value,\n            \"confidence\": confidence,\n            \"timestamp\": None,\n        })\n\n    def add_hypothesis(self, actor_name, actor_id=\"\"):\n        self.hypotheses[actor_name] = {\n            \"actor_id\": actor_id,\n            \"consistent_evidence\": [],\n            \"inconsistent_evidence\": [],\n            \"neutral_evidence\": [],\n            \"score\": 0,\n        }\n\n    def evaluate_evidence(self, evidence_idx, actor_name, assessment):\n        \"\"\"Assess evidence against a hypothesis: consistent\u002Finconsistent\u002Fneutral.\"\"\"\n        if assessment == \"consistent\":\n            self.hypotheses[actor_name][\"consistent_evidence\"].append(evidence_idx)\n            self.hypotheses[actor_name][\"score\"] += self.evidence[evidence_idx][\"confidence\"]\n        elif assessment == \"inconsistent\":\n            self.hypotheses[actor_name][\"inconsistent_evidence\"].append(evidence_idx)\n            self.hypotheses[actor_name][\"score\"] -= self.evidence[evidence_idx][\"confidence\"] * 2\n        else:\n            self.hypotheses[actor_name][\"neutral_evidence\"].append(evidence_idx)\n\n    def rank_hypotheses(self):\n        \"\"\"Rank hypotheses by attribution score.\"\"\"\n        ranked = sorted(\n            self.hypotheses.items(),\n            key=lambda x: x[1][\"score\"],\n            reverse=True,\n        )\n        return [\n            {\n                \"actor\": name,\n                \"score\": data[\"score\"],\n                \"consistent\": len(data[\"consistent_evidence\"]),\n                \"inconsistent\": len(data[\"inconsistent_evidence\"]),\n                \"confidence\": self._score_to_confidence(data[\"score\"]),\n            }\n            for name, data in ranked\n        ]\n\n    def _score_to_confidence(self, score):\n        if score >= 80:\n            return \"HIGH\"\n        elif score >= 40:\n            return \"MODERATE\"\n        else:\n            return \"LOW\"\n```\n\n### Step 2: Infrastructure Overlap Analysis\n\n```python\ndef analyze_infrastructure_overlap(campaign_a_infra, campaign_b_infra):\n    \"\"\"Compare infrastructure between two campaigns for attribution.\"\"\"\n    overlap = {\n        \"shared_ips\": set(campaign_a_infra.get(\"ips\", [])).intersection(\n            campaign_b_infra.get(\"ips\", [])\n        ),\n        \"shared_domains\": set(campaign_a_infra.get(\"domains\", [])).intersection(\n            campaign_b_infra.get(\"domains\", [])\n        ),\n        \"shared_asns\": set(campaign_a_infra.get(\"asns\", [])).intersection(\n            campaign_b_infra.get(\"asns\", [])\n        ),\n        \"shared_registrars\": set(campaign_a_infra.get(\"registrars\", [])).intersection(\n            campaign_b_infra.get(\"registrars\", [])\n        ),\n    }\n\n    overlap_score = 0\n    if overlap[\"shared_ips\"]:\n        overlap_score += 30\n    if overlap[\"shared_domains\"]:\n        overlap_score += 25\n    if overlap[\"shared_asns\"]:\n        overlap_score += 15\n    if overlap[\"shared_registrars\"]:\n        overlap_score += 10\n\n    return {\n        \"overlap\": {k: list(v) for k, v in overlap.items()},\n        \"overlap_score\": overlap_score,\n        \"assessment\": \"STRONG\" if overlap_score >= 40 else \"MODERATE\" if overlap_score >= 20 else \"WEAK\",\n    }\n```\n\n### Step 3: TTP Comparison Across Campaigns\n\n```python\nfrom attackcti import attack_client\n\ndef compare_campaign_ttps(campaign_techniques, known_actor_techniques):\n    \"\"\"Compare campaign TTPs against known threat actor profiles.\"\"\"\n    campaign_set = set(campaign_techniques)\n    actor_set = set(known_actor_techniques)\n\n    common = campaign_set.intersection(actor_set)\n    unique_campaign = campaign_set - actor_set\n    unique_actor = actor_set - campaign_set\n\n    jaccard = len(common) \u002F len(campaign_set.union(actor_set)) if campaign_set.union(actor_set) else 0\n\n    return {\n        \"common_techniques\": sorted(common),\n        \"common_count\": len(common),\n        \"unique_to_campaign\": sorted(unique_campaign),\n        \"unique_to_actor\": sorted(unique_actor),\n        \"jaccard_similarity\": round(jaccard, 3),\n        \"overlap_percentage\": round(len(common) \u002F len(campaign_set) * 100, 1) if campaign_set else 0,\n    }\n```\n\n### Step 4: Generate Attribution Report\n\n```python\ndef generate_attribution_report(analyzer):\n    \"\"\"Generate structured attribution assessment report.\"\"\"\n    rankings = analyzer.rank_hypotheses()\n\n    report = {\n        \"assessment_date\": \"2026-02-23\",\n        \"total_evidence_items\": len(analyzer.evidence),\n        \"hypotheses_evaluated\": len(analyzer.hypotheses),\n        \"rankings\": rankings,\n        \"primary_attribution\": rankings[0] if rankings else None,\n        \"evidence_summary\": [\n            {\n                \"index\": i,\n                \"category\": e[\"category\"],\n                \"description\": e[\"description\"],\n                \"confidence\": e[\"confidence\"],\n            }\n            for i, e in enumerate(analyzer.evidence)\n        ],\n    }\n\n    return report\n```\n\n## Validation Criteria\n\n- Evidence collection covers all six attribution categories\n- ACH matrix properly evaluates evidence against competing hypotheses\n- Infrastructure overlap analysis identifies shared indicators\n- TTP comparison uses ATT&CK technique IDs for precision\n- Attribution confidence levels are properly justified\n- Report includes alternative hypotheses and false flag considerations\n\n## References\n\n- [Diamond Model of Intrusion Analysis](https:\u002F\u002Fwww.activeresponse.org\u002Fwp-content\u002Fuploads\u002F2013\u002F07\u002Fdiamond.pdf)\n- [MITRE ATT&CK Groups](https:\u002F\u002Fattack.mitre.org\u002Fgroups\u002F)\n- [Analysis of Competing Hypotheses](https:\u002F\u002Fwww.cia.gov\u002Fstatic\u002F9a5f1162fd0932c29e985f0159f56c07\u002FTradecraft-Primer-apr09.pdf)\n- [Threat Attribution 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secure Aptos Move V2 smart contracts with Object model, Digital Asset integration, security patterns, and storage type guidance. Includes comprehensive storage decision framework for optimal data structure selection. Triggers on: 'write contract', 'create NFT collection', 'build marketplace', 'implement minting', 'generate Move module', 'create token contract', 'build DAO', 'implement staking'. Ask storage questions when: 'store', 'track', 'registry', 'mapping', 'list', 'collection'.","MIT","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fwrite-contracts\u002FSKILL.md",[],[120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139],"write","contracts","write contracts","storage","secure","create","generate","collection","model","decision","ask","smart-contracts","selection","objects","structure","nft","token","aptos","critical","move",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":116,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":146,"publishedAt":46},"01M6F2RFXJ6T9MKW0A7N7GM45B","web-search","通用网络搜索技能，支持多引擎搜索（百度、必应、DuckDuckGo），无需API密钥即可获取实时信息","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fweb-search-ex-skill\u002FSKILL.md",[],[147,148,149,150,151,152,153,154,155,156],"web","search","web search","搜索","baidu","bing","duckduckgo","百度","多引擎","internet",{"skillNo":158,"name":159,"nameZh":6,"description":160,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":116,"licenseFileUrl":6,"sourceHtmlUrl":161,"categories":162,"keywords":166,"publishedAt":46},"01M6F2K968G1FZEZFPW2E6B222","solo-build","Execute implementation plan tasks with TDD workflow, auto-commit, and phase gates. Use when user says \"build it\", \"start building\", \"execute plan\", \"implement tasks\", \"ship it\", or references a track ID. Do NOT use for planning (use \u002Fplan) or scaffolding (use \u002Fscaffold).","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fsolo-build\u002FSKILL.md",[163],{"categoryNo":164,"categoryName":165,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[167,168,169,170,171,172,173,174,148,175,176,177,120,178,179,180,181,182,183,184],"build","solo","solo build","MCP","workflow","commit","tasks","code","Bash","plan","read","auto","references","context7","query","scaffolding","ship it","project",{"skillNo":186,"name":187,"nameZh":6,"description":188,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":116,"licenseFileUrl":6,"sourceHtmlUrl":189,"categories":190,"keywords":197,"publishedAt":46},"01M6F2GKN39SGHSPWYB3662YC8","react-email","Use when building HTML email templates with React components, adding a visual email editor to an application using the React Email visual editor, rendering emails to HTML, or sending emails with Resend. Covers welcome emails, password resets, notifications, order confirmations, newsletters, transactional emails, and the embeddable email editor component.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Freact-email-skills\u002FSKILL.md",[191,194],{"categoryNo":192,"categoryName":193,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000005","邮件与沟通",{"categoryNo":195,"categoryName":196,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217],"react","email","react email","github","editor","components","react components","component","welcome","https","notifications","password","node","use","html","resend","visual","emails","rendering","with",{"skillNo":219,"name":220,"nameZh":6,"description":221,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":116,"licenseFileUrl":6,"sourceHtmlUrl":222,"categories":223,"keywords":234,"publishedAt":46},"01M6F2FBVGVDFSG8SSFJGXA40C","APITester Agent-Driven API Testing","Test API endpoints and document responses. Define tests in plain English, run them, get formatted results. Agent-driven Postman alternative.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fopenclaw-api-tester\u002FSKILL.md",[224,227,230,231],{"categoryNo":225,"categoryName":226,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":228,"categoryName":229,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":164,"categoryName":165,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":232,"categoryName":233,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师",[235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252],"testing","api","api testing","driven","APITester Agent Driven API Testing","test","run","postman","results","plain english","tester","endpoints","document","get","define","tests","english","api-tester",{"skillNo":254,"name":255,"nameZh":6,"description":256,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":116,"licenseFileUrl":6,"sourceHtmlUrl":257,"categories":258,"keywords":259,"publishedAt":46},"01M6F273Y3NGAPVJZ608BC0G9G","hubspot-implementation-plan","Generate a phased implementation plan from a HubSpot audit report. Creates prioritized, sequenced cleanup processes with effort estimates, dependencies, and automation feasibility. Use after running \u002Fhubspot-audit.","https:\u002F\u002Fgithub.com\u002Fluokai0\u002Fai-agent-skills-by-luo-kai\u002Fblob\u002Fmain\u002Fai-agent-skills\u002F22-clawhub-skills\u002Fhubspot-implementation-plan\u002FSKILL.md",[],[176,260,261,262,263,264,265,126,266,267,268,269,270,211,271,272,217,273],"implementation","hubspot","hubspot implementation plan","automation","feasibility","cleanup","planning","report","audit","audit report","phased implementation","dependencies","processes","hubspot audit"]