[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73FV0TMSVZ4S7MFMV8JAMQQ":3,"skill-same:01M73FV0TMSVZ4S7MFMV8JAMQQ":49},{"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":25,"commitSha":26,"skillDirectory":27,"forksCount":28,"repositoryHtmlUrl":29,"repositoryHomepageUrl":30,"ownerHtmlUrl":31,"ownerBlogUrl":32,"ownerDescription":33,"updatedAt":34,"install":35,"resources":48},"01M73FV0TMSVZ4S7MFMV8JAMQQ","skill-stocktake",null,"Use when auditing Claude skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation.","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-stocktake\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: skill-stocktake\ndescription: \"Use when auditing Claude skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation.\"\norigin: ECC\n---\n\n# skill-stocktake\n\nSlash command (`\u002Fskill-stocktake`) that audits all Claude skills and commands using a quality checklist + AI holistic judgment. Supports two modes: Quick Scan for recently changed skills, and Full Stocktake for a complete review.\n\n## Scope\n\nThe command targets the following paths **relative to the directory where it is invoked**:\n\n| Path | Description |\n|------|-------------|\n| `~\u002F.claude\u002Fskills\u002F` | Global skills (all projects) |\n| `{cwd}\u002F.claude\u002Fskills\u002F` | Project-level skills (if the directory exists) |\n\n**At the start of Phase 1, the command explicitly lists which paths were found and scanned.**\n\n### Targeting a specific project\n\nTo include project-level skills, run from that project's root directory:\n\n```bash\ncd ~\u002Fpath\u002Fto\u002Fmy-project\n\u002Fskill-stocktake\n```\n\nIf the project has no `.claude\u002Fskills\u002F` directory, only global skills and commands are evaluated.\n\n## Modes\n\n| Mode | Trigger | Duration |\n|------|---------|---------|\n| Quick Scan | `results.json` exists (default) | 5–10 min |\n| Full Stocktake | `results.json` absent, or `\u002Fskill-stocktake full` | 20–30 min |\n\n**Results cache:** `~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fresults.json`\n\n## Quick Scan Flow\n\nRe-evaluate only skills that have changed since the last run (5–10 min).\n\n1. Read `~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fresults.json`\n2. Run: `bash ~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fscripts\u002Fquick-diff.sh \\\n         ~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fresults.json`\n   (Project dir is auto-detected from `$PWD\u002F.claude\u002Fskills`; pass it explicitly only if needed)\n3. If output is `[]`: report \"No changes since last run.\" and stop\n4. Re-evaluate only those changed files using the same Phase 2 criteria\n5. Carry forward unchanged skills from previous results\n6. Output only the diff\n7. Run: `bash ~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fscripts\u002Fsave-results.sh \\\n         ~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fresults.json \u003C\u003C\u003C \"$EVAL_RESULTS\"`\n\n## Full Stocktake Flow\n\n### Phase 1 — Inventory\n\nRun: `bash ~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fscripts\u002Fscan.sh`\n\nThe script enumerates skill files, extracts frontmatter, and collects UTC mtimes.\nProject dir is auto-detected from `$PWD\u002F.claude\u002Fskills`; pass it explicitly only if needed.\nPresent the scan summary and inventory table from the script output:\n\n```\nScanning:\n  ✓ ~\u002F.claude\u002Fskills\u002F         (17 files)\n  ✗ {cwd}\u002F.claude\u002Fskills\u002F    (not found — global skills only)\n```\n\n| Skill | 7d use | 30d use | Description |\n|-------|--------|---------|-------------|\n\n### Phase 2 — Quality Evaluation\n\nLaunch an Agent tool subagent (**general-purpose agent**) with the full inventory and checklist:\n\n```text\nAgent(\n  subagent_type=\"general-purpose\",\n  prompt=\"\nEvaluate the following skill inventory against the checklist.\n\n[INVENTORY]\n\n[CHECKLIST]\n\nReturn JSON for each skill:\n{ \\\"verdict\\\": \\\"Keep\\\"|\\\"Improve\\\"|\\\"Update\\\"|\\\"Retire\\\"|\\\"Merge into [X]\\\", \\\"reason\\\": \\\"...\\\" }\n\"\n)\n```\n\nThe subagent reads each skill, applies the checklist, and returns per-skill JSON:\n\n`{ \"verdict\": \"Keep\"|\"Improve\"|\"Update\"|\"Retire\"|\"Merge into [X]\", \"reason\": \"...\" }`\n\n**Chunk guidance:** Process ~20 skills per subagent invocation to keep context manageable. Save intermediate results to `results.json` (`status: \"in_progress\"`) after each chunk.\n\nAfter all skills are evaluated: set `status: \"completed\"`, proceed to Phase 3.\n\n**Resume detection:** If `status: \"in_progress\"` is found on startup, resume from the first unevaluated skill.\n\nEach skill is evaluated against this checklist:\n\n```\n- [ ] Content overlap with other skills checked\n- [ ] Overlap with MEMORY.md \u002F CLAUDE.md checked\n- [ ] Freshness of technical references verified (use WebSearch if tool names \u002F CLI flags \u002F APIs are present)\n- [ ] Usage frequency considered\n```\n\nVerdict criteria:\n\n| Verdict | Meaning |\n|---------|---------|\n| Keep | Useful and current |\n| Improve | Worth keeping, but specific improvements needed |\n| Update | Referenced technology is outdated (verify with WebSearch) |\n| Retire | Low quality, stale, or cost-asymmetric |\n| Merge into [X] | Substantial overlap with another skill; name the merge target |\n\nEvaluation is **holistic AI judgment** — not a numeric rubric. Guiding dimensions:\n- **Actionability**: code examples, commands, or steps that let you act immediately\n- **Scope fit**: name, trigger, and content are aligned; not too broad or narrow\n- **Uniqueness**: value not replaceable by MEMORY.md \u002F CLAUDE.md \u002F another skill\n- **Currency**: technical references work in the current environment\n\n**Reason quality requirements** — the `reason` field must be self-contained and decision-enabling:\n- Do NOT write \"unchanged\" alone — always restate the core evidence\n- For **Retire**: state (1) what specific defect was found, (2) what covers the same need instead\n  - Bad: `\"Superseded\"`\n  - Good: `\"disable-model-invocation: true already set; superseded by continuous-learning-v2 which covers all the same patterns plus confidence scoring. No unique content remains.\"`\n- For **Merge**: name the target and describe what content to integrate\n  - Bad: `\"Overlaps with X\"`\n  - Good: `\"42-line thin content; Step 4 of chatlog-to-article already covers the same workflow. Integrate the 'article angle' tip as a note in that skill.\"`\n- For **Improve**: describe the specific change needed (what section, what action, target size if relevant)\n  - Bad: `\"Too long\"`\n  - Good: `\"276 lines; Section 'Framework Comparison' (L80–140) duplicates ai-era-architecture-principles; delete it to reach ~150 lines.\"`\n- For **Keep** (mtime-only change in Quick Scan): restate the original verdict rationale, do not write \"unchanged\"\n  - Bad: `\"Unchanged\"`\n  - Good: `\"mtime updated but content unchanged. Unique Python reference explicitly imported by rules\u002Fpython\u002F; no overlap found.\"`\n\n### Phase 3 — Summary Table\n\n| Skill | 7d use | Verdict | Reason |\n|-------|--------|---------|--------|\n\n### Phase 4 — Consolidation\n\n1. **Retire \u002F Merge**: present detailed justification per file before confirming with user:\n   - What specific problem was found (overlap, staleness, broken references, etc.)\n   - What alternative covers the same functionality (for Retire: which existing skill\u002Frule; for Merge: the target file and what content to integrate)\n   - Impact of removal (any dependent skills, MEMORY.md references, or workflows affected)\n2. **Improve**: present specific improvement suggestions with rationale:\n   - What to change and why (e.g., \"trim 430→200 lines because sections X\u002FY duplicate python-patterns\")\n   - User decides whether to act\n3. **Update**: present updated content with sources checked\n4. Check MEMORY.md line count; propose compression if >100 lines\n\n## Results File Schema\n\n`~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fresults.json`:\n\n**`evaluated_at`**: Must be set to the actual UTC time of evaluation completion.\nObtain via Bash: `date -u +%Y-%m-%dT%H:%M:%SZ`. Never use a date-only approximation like `T00:00:00Z`.\n\n```json\n{\n  \"evaluated_at\": \"2026-02-21T10:00:00Z\",\n  \"mode\": \"full\",\n  \"batch_progress\": {\n    \"total\": 80,\n    \"evaluated\": 80,\n    \"status\": \"completed\"\n  },\n  \"skills\": {\n    \"skill-name\": {\n      \"path\": \"~\u002F.claude\u002Fskills\u002Fskill-name\u002FSKILL.md\",\n      \"verdict\": \"Keep\",\n      \"reason\": \"Concrete, actionable, unique value for X workflow\",\n      \"mtime\": \"2026-01-15T08:30:00Z\"\n    }\n  }\n}\n```\n\n## Notes\n\n- Evaluation is blind: the same checklist applies to all skills regardless of origin (ECC, self-authored, auto-extracted)\n- Archive \u002F delete operations always require explicit user confirmation\n- No verdict branching by skill origin\n","# skill-stocktake\n\nSlash command (`\u002Fskill-stocktake`) that audits all Claude skills and commands using a quality checklist + AI holistic judgment. Supports two modes: Quick Scan for recently changed skills, and Full Stocktake for a complete review.\n\n## Scope\n\nThe command targets the following paths **relative to the directory where it is invoked**:\n\n| Path | Description |\n|------|-------------|\n| `~\u002F.claude\u002Fskills\u002F` | Global skills (all projects) |\n| `{cwd}\u002F.claude\u002Fskills\u002F` | Project-level skills (if the directory exists) |\n\n**At the start of Phase 1, the command explicitly lists which paths were found and scanned.**\n\n### Targeting a specific project\n\nTo include project-level skills, run from that project's root directory:\n\n```bash\ncd ~\u002Fpath\u002Fto\u002Fmy-project\n\u002Fskill-stocktake\n```\n\nIf the project has no `.claude\u002Fskills\u002F` directory, only global skills and commands are evaluated.\n\n## Modes\n\n| Mode | Trigger | Duration |\n|------|---------|---------|\n| Quick Scan | `results.json` exists (default) | 5–10 min |\n| Full Stocktake | `results.json` absent, or `\u002Fskill-stocktake full` | 20–30 min |\n\n**Results cache:** `~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fresults.json`\n\n## Quick Scan Flow\n\nRe-evaluate only skills that have changed since the last run (5–10 min).\n\n1. Read `~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fresults.json`\n2. Run: `bash ~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fscripts\u002Fquick-diff.sh \\\n         ~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fresults.json`\n   (Project dir is auto-detected from `$PWD\u002F.claude\u002Fskills`; pass it explicitly only if needed)\n3. If output is `[]`: report \"No changes since last run.\" and stop\n4. Re-evaluate only those changed files using the same Phase 2 criteria\n5. Carry forward unchanged skills from previous results\n6. Output only the diff\n7. Run: `bash ~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fscripts\u002Fsave-results.sh \\\n         ~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fresults.json \u003C\u003C\u003C \"$EVAL_RESULTS\"`\n\n## Full Stocktake Flow\n\n### Phase 1 — Inventory\n\nRun: `bash ~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fscripts\u002Fscan.sh`\n\nThe script enumerates skill files, extracts frontmatter, and collects UTC mtimes.\nProject dir is auto-detected from `$PWD\u002F.claude\u002Fskills`; pass it explicitly only if needed.\nPresent the scan summary and inventory table from the script output:\n\n```\nScanning:\n  ✓ ~\u002F.claude\u002Fskills\u002F         (17 files)\n  ✗ {cwd}\u002F.claude\u002Fskills\u002F    (not found — global skills only)\n```\n\n| Skill | 7d use | 30d use | Description |\n|-------|--------|---------|-------------|\n\n### Phase 2 — Quality Evaluation\n\nLaunch an Agent tool subagent (**general-purpose agent**) with the full inventory and checklist:\n\n```text\nAgent(\n  subagent_type=\"general-purpose\",\n  prompt=\"\nEvaluate the following skill inventory against the checklist.\n\n[INVENTORY]\n\n[CHECKLIST]\n\nReturn JSON for each skill:\n{ \\\"verdict\\\": \\\"Keep\\\"|\\\"Improve\\\"|\\\"Update\\\"|\\\"Retire\\\"|\\\"Merge into [X]\\\", \\\"reason\\\": \\\"...\\\" }\n\"\n)\n```\n\nThe subagent reads each skill, applies the checklist, and returns per-skill JSON:\n\n`{ \"verdict\": \"Keep\"|\"Improve\"|\"Update\"|\"Retire\"|\"Merge into [X]\", \"reason\": \"...\" }`\n\n**Chunk guidance:** Process ~20 skills per subagent invocation to keep context manageable. Save intermediate results to `results.json` (`status: \"in_progress\"`) after each chunk.\n\nAfter all skills are evaluated: set `status: \"completed\"`, proceed to Phase 3.\n\n**Resume detection:** If `status: \"in_progress\"` is found on startup, resume from the first unevaluated skill.\n\nEach skill is evaluated against this checklist:\n\n```\n- [ ] Content overlap with other skills checked\n- [ ] Overlap with MEMORY.md \u002F CLAUDE.md checked\n- [ ] Freshness of technical references verified (use WebSearch if tool names \u002F CLI flags \u002F APIs are present)\n- [ ] Usage frequency considered\n```\n\nVerdict criteria:\n\n| Verdict | Meaning |\n|---------|---------|\n| Keep | Useful and current |\n| Improve | Worth keeping, but specific improvements needed |\n| Update | Referenced technology is outdated (verify with WebSearch) |\n| Retire | Low quality, stale, or cost-asymmetric |\n| Merge into [X] | Substantial overlap with another skill; name the merge target |\n\nEvaluation is **holistic AI judgment** — not a numeric rubric. Guiding dimensions:\n- **Actionability**: code examples, commands, or steps that let you act immediately\n- **Scope fit**: name, trigger, and content are aligned; not too broad or narrow\n- **Uniqueness**: value not replaceable by MEMORY.md \u002F CLAUDE.md \u002F another skill\n- **Currency**: technical references work in the current environment\n\n**Reason quality requirements** — the `reason` field must be self-contained and decision-enabling:\n- Do NOT write \"unchanged\" alone — always restate the core evidence\n- For **Retire**: state (1) what specific defect was found, (2) what covers the same need instead\n  - Bad: `\"Superseded\"`\n  - Good: `\"disable-model-invocation: true already set; superseded by continuous-learning-v2 which covers all the same patterns plus confidence scoring. No unique content remains.\"`\n- For **Merge**: name the target and describe what content to integrate\n  - Bad: `\"Overlaps with X\"`\n  - Good: `\"42-line thin content; Step 4 of chatlog-to-article already covers the same workflow. Integrate the 'article angle' tip as a note in that skill.\"`\n- For **Improve**: describe the specific change needed (what section, what action, target size if relevant)\n  - Bad: `\"Too long\"`\n  - Good: `\"276 lines; Section 'Framework Comparison' (L80–140) duplicates ai-era-architecture-principles; delete it to reach ~150 lines.\"`\n- For **Keep** (mtime-only change in Quick Scan): restate the original verdict rationale, do not write \"unchanged\"\n  - Bad: `\"Unchanged\"`\n  - Good: `\"mtime updated but content unchanged. Unique Python reference explicitly imported by rules\u002Fpython\u002F; no overlap found.\"`\n\n### Phase 3 — Summary Table\n\n| Skill | 7d use | Verdict | Reason |\n|-------|--------|---------|--------|\n\n### Phase 4 — Consolidation\n\n1. **Retire \u002F Merge**: present detailed justification per file before confirming with user:\n   - What specific problem was found (overlap, staleness, broken references, etc.)\n   - What alternative covers the same functionality (for Retire: which existing skill\u002Frule; for Merge: the target file and what content to integrate)\n   - Impact of removal (any dependent skills, MEMORY.md references, or workflows affected)\n2. **Improve**: present specific improvement suggestions with rationale:\n   - What to change and why (e.g., \"trim 430→200 lines because sections X\u002FY duplicate python-patterns\")\n   - User decides whether to act\n3. **Update**: present updated content with sources checked\n4. Check MEMORY.md line count; propose compression if >100 lines\n\n## Results File Schema\n\n`~\u002F.claude\u002Fskills\u002Fskill-stocktake\u002Fresults.json`:\n\n**`evaluated_at`**: Must be set to the actual UTC time of evaluation completion.\nObtain via Bash: `date -u +%Y-%m-%dT%H:%M:%SZ`. Never use a date-only approximation like `T00:00:00Z`.\n\n```json\n{\n  \"evaluated_at\": \"2026-02-21T10:00:00Z\",\n  \"mode\": \"full\",\n  \"batch_progress\": {\n    \"total\": 80,\n    \"evaluated\": 80,\n    \"status\": \"completed\"\n  },\n  \"skills\": {\n    \"skill-name\": {\n      \"path\": \"~\u002F.claude\u002Fskills\u002Fskill-name\u002FSKILL.md\",\n      \"verdict\": \"Keep\",\n      \"reason\": \"Concrete, actionable, unique value for X workflow\",\n      \"mtime\": \"2026-01-15T08:30:00Z\"\n    }\n  }\n}\n```\n\n## Notes\n\n- Evaluation is blind: the same checklist applies to all skills regardless of origin (ECC, self-authored, auto-extracted)\n- Archive \u002F delete operations always require explicit user confirmation\n- No verdict branching by skill origin\n",{"name":5,"origin":24,"description":7},"ECC","main","481dcd7dd2e288b9d627a2bd017db70e64e6c6fa","core\u002Fskills\u002Fskill-stocktake",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.","2026-11-13 13:15:29",{"source":8,"skillName":5,"commands":36,"prompt":46,"zipAvailable":47},[37,40,43],{"tool":38,"command":39},"npx","npx skills add Horonomos-laps\u002FYana-AI --skill skill-stocktake",{"tool":41,"command":42},"bunx","bunx skills add Horonomos-laps\u002FYana-AI --skill skill-stocktake",{"tool":44,"command":45},"pnpm","pnpm dlx skills add Horonomos-laps\u002FYana-AI --skill skill-stocktake","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002Fcore\u002Fskills\u002Fskill-stocktake\u002FSKILL.md",true,[],[50,58,65,73,81,89],{"skillNo":51,"name":52,"nameZh":6,"description":53,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":54,"licenseFileUrl":6,"sourceHtmlUrl":55,"categories":56,"keywords":57,"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":59,"name":60,"nameZh":6,"description":61,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":64,"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":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":69,"sourceHtmlUrl":70,"categories":71,"keywords":72,"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":74,"name":75,"nameZh":6,"description":76,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":77,"sourceHtmlUrl":78,"categories":79,"keywords":80,"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":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":85,"sourceHtmlUrl":86,"categories":87,"keywords":88,"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":90,"name":91,"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":92,"categories":93,"keywords":94,"publishedAt":19},"01M736TK27QMCXK0DVCH25SJN5","ramdisk-tmpfs-patterns","https:\u002F\u002Fgithub.com\u002FHoronomos-laps\u002FYana-AI\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Framdisk-tmpfs-patterns\u002FSKILL.md",[],[]]