[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73FTA9KDPEQEAASVDQXWAZK":3,"skill-same:01M73FTA9KDPEQEAASVDQXWAZK":50},{"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":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":26,"commitSha":27,"skillDirectory":28,"forksCount":29,"repositoryHtmlUrl":30,"repositoryHomepageUrl":31,"ownerHtmlUrl":32,"ownerBlogUrl":33,"ownerDescription":34,"updatedAt":35,"install":36,"resources":49},"01M73FTA9KDPEQEAASVDQXWAZK","skill-comply",null,"Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines","Horonomos-laps\u002FYana-AI","Horonomos-laps","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F355299122?v=4","01M76B3QPXE88ET2SQPDPVATZT"," Horonomos-laps","01M72ZQRJPCYPSSWT4D00VJR2A",13,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fskill-comply\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: skill-comply\ndescription: Visualize whether skills, rules, and agent definitions are actually followed — auto-generates scenarios at 3 prompt strictness levels, runs agents, classifies behavioral sequences, and reports compliance rates with full tool call timelines\norigin: ECC\ntools: Read, Bash\n---\n\n# skill-comply: Automated Compliance Measurement\n\nMeasures whether coding agents actually follow skills, rules, or agent definitions by:\n1. Auto-generating expected behavioral sequences (specs) from any .md file\n2. Auto-generating scenarios with decreasing prompt strictness (supportive → neutral → competing)\n3. Running `claude -p` and capturing tool call traces via stream-json\n4. Classifying tool calls against spec steps using LLM (not regex)\n5. Checking temporal ordering deterministically\n6. Generating self-contained reports with spec, prompts, and timelines\n\n## Supported Targets\n\n- **Skills** (`skills\u002F*\u002FSKILL.md`): Workflow skills like search-first, TDD guides\n- **Rules** (`rules\u002Fcommon\u002F*.md`): Mandatory rules like testing.md, security.md, git-workflow.md\n- **Agent definitions** (`agents\u002F*.md`): Whether an agent gets invoked when expected (internal workflow verification not yet supported)\n\n## When to Activate\n\n- User runs `\u002Fskill-comply \u003Cpath>`\n- User asks \"is this rule actually being followed?\"\n- After adding new rules\u002Fskills, to verify agent compliance\n- Periodically as part of quality maintenance\n\n## Usage\n\n```bash\n# Full run\nuv run python -m scripts.run ~\u002F.claude\u002Frules\u002Fcommon\u002Ftesting.md\n\n# Dry run (no cost, spec + scenarios only)\nuv run python -m scripts.run --dry-run ~\u002F.claude\u002Fskills\u002Fsearch-first\u002FSKILL.md\n\n# Custom models\nuv run python -m scripts.run --gen-model haiku --model sonnet \u003Cpath>\n```\n\n## Key Concept: Prompt Independence\n\nMeasures whether a skill\u002Frule is followed even when the prompt doesn't explicitly support it.\n\n## Report Contents\n\nReports are self-contained and include:\n1. Expected behavioral sequence (auto-generated spec)\n2. Scenario prompts (what was asked at each strictness level)\n3. Compliance scores per scenario\n4. Tool call timelines with LLM classification labels\n\n### Advanced (optional)\n\nFor users familiar with hooks, reports also include hook promotion recommendations for steps with low compliance. This is informational — the main value is the compliance visibility itself.\n","# skill-comply: Automated Compliance Measurement\n\nMeasures whether coding agents actually follow skills, rules, or agent definitions by:\n1. Auto-generating expected behavioral sequences (specs) from any .md file\n2. Auto-generating scenarios with decreasing prompt strictness (supportive → neutral → competing)\n3. Running `claude -p` and capturing tool call traces via stream-json\n4. Classifying tool calls against spec steps using LLM (not regex)\n5. Checking temporal ordering deterministically\n6. Generating self-contained reports with spec, prompts, and timelines\n\n## Supported Targets\n\n- **Skills** (`skills\u002F*\u002FSKILL.md`): Workflow skills like search-first, TDD guides\n- **Rules** (`rules\u002Fcommon\u002F*.md`): Mandatory rules like testing.md, security.md, git-workflow.md\n- **Agent definitions** (`agents\u002F*.md`): Whether an agent gets invoked when expected (internal workflow verification not yet supported)\n\n## When to Activate\n\n- User runs `\u002Fskill-comply \u003Cpath>`\n- User asks \"is this rule actually being followed?\"\n- After adding new rules\u002Fskills, to verify agent compliance\n- Periodically as part of quality maintenance\n\n## Usage\n\n```bash\n# Full run\nuv run python -m scripts.run ~\u002F.claude\u002Frules\u002Fcommon\u002Ftesting.md\n\n# Dry run (no cost, spec + scenarios only)\nuv run python -m scripts.run --dry-run ~\u002F.claude\u002Fskills\u002Fsearch-first\u002FSKILL.md\n\n# Custom models\nuv run python -m scripts.run --gen-model haiku --model sonnet \u003Cpath>\n```\n\n## Key Concept: Prompt Independence\n\nMeasures whether a skill\u002Frule is followed even when the prompt doesn't explicitly support it.\n\n## Report Contents\n\nReports are self-contained and include:\n1. Expected behavioral sequence (auto-generated spec)\n2. Scenario prompts (what was asked at each strictness level)\n3. Compliance scores per scenario\n4. Tool call timelines with LLM classification labels\n\n### Advanced (optional)\n\nFor users familiar with hooks, reports also include hook promotion recommendations for steps with low compliance. This is informational — the main value is the compliance visibility itself.\n",{"name":5,"tools":24,"origin":25,"description":7},"Read, Bash","ECC","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fskill-comply",2,"https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI","https:\u002F\u002Fyana.vutam.link\u002F","https:\u002F\u002Fgithub.com\u002FHoronomos-laps","https:\u002F\u002Fyana.vutam.link","An open-source governed runtime & execution environment for multi-agent AI systems.","2026-11-14 13:24:09",{"source":8,"skillName":5,"commands":37,"prompt":47,"zipAvailable":48},[38,41,44],{"tool":39,"command":40},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill skill-comply",{"tool":42,"command":43},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill skill-comply",{"tool":45,"command":46},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill skill-comply","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fskill-comply\u002FSKILL.md",true,[],[51,59,66,74,82,90],{"skillNo":52,"name":53,"nameZh":6,"description":54,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":55,"licenseFileUrl":6,"sourceHtmlUrl":56,"categories":57,"keywords":58,"publishedAt":19},"01M735GKET5YVYKAY62W2P5YXB","openai--sentry--sentry","Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.","MIT","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fopenai--sentry--sentry\u002FSKILL.md",[],[],{"skillNo":60,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":63,"categories":64,"keywords":65,"publishedAt":19},"01M73DCAP83T17HY7SMGK7MGFE","ito-basket-compare","Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fito-basket-compare\u002FSKILL.md",[],[],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":70,"sourceHtmlUrl":71,"categories":72,"keywords":73,"publishedAt":19},"01M734CYEMZFDR3KH6SW0JP8TD","implementing-network-traffic-baselining","Build network traffic baselines from NetFlow\u002FIPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly\u002Fdaily traffic pattern profiling","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-network-traffic-baselining\u002FSKILL.md",[],[],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":78,"sourceHtmlUrl":79,"categories":80,"keywords":81,"publishedAt":19},"01M73ASZ6NJRJM44Z2P84NGQ3Q","detecting-evasion-techniques-in-endpoint-logs","Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fdetecting-evasion-techniques-in-endpoint-logs\u002FSKILL.md",[],[],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":86,"sourceHtmlUrl":87,"categories":88,"keywords":89,"publishedAt":19},"01M734AFNXD06Z78QRK0VKGT7Q","implementing-microsegmentation-with-guardicore","Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.","https:\u002F\u002Fgithub.com\u002Fyanacuti1121\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FLICENSE","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fimplementing-microsegmentation-with-guardicore\u002FSKILL.md",[],[],{"skillNo":91,"name":92,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":93,"categories":94,"keywords":95,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]