[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6C1N6N9DZPMVJSAFWRKTDB0":3,"skill-same:01M6C1N6N9DZPMVJSAFWRKTDB0":123},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":13,"repositoryHtmlUrl":56,"repositoryHomepageUrl":6,"ownerHtmlUrl":57,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":58,"install":59,"resources":72},"01M6C1N6N9DZPMVJSAFWRKTDB0","skill-doctor",null,"Use when the user wants their agent setup graded from real conversation history, asks which installed skills are actually working, or wants evidence-backed skill edits — scores recent local Claude Code \u002F Codex sessions against efficiency and code-quality rubrics, then drafts skill changes gated by a deterministic aggregator and renders one local shareable report.","mne20114-hash\u002Fgest35bi","mne20114-hash","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F224168109?v=4","01M6C0JCR7334CZ39YWTZXX2NG","01M6C0JCWN0NBWMQTW1CPG2Z4G",0,"MIT","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"doctor","skill doctor","quality","use","session","interactive","question","repo","eval","work","common","history","codex","html report","code-quality","loop","aggregation","report","code","sessions","2026-11-10 10:12:30",false,"---\nname: skill-doctor\ndescription: Use when the user wants their agent setup graded from real conversation history, asks which installed skills are actually working, or wants evidence-backed skill edits — scores recent local Claude Code \u002F Codex sessions against efficiency and code-quality rubrics, then drafts skill changes gated by a deterministic aggregator and renders one local shareable report.\nargument-hint: \"[optional: --repo PATH, --days N, or a question about the setup]\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  build_pattern: \"Rebuild of warpdotdev\u002Fcommon-skills skill-doctor (MIT): harvest → LLM-judges-with-rubrics → deterministic aggregation gate → self-contained HTML report\"\n  distinct_from: \"skillopt-sleep (nightly automated replay loop with adopt gate; this is one interactive graded pass); write-a-skill (authors a skill from expertise; this improves skills from observed session evidence); self-eval (grades this session's own work; this grades a window of past sessions)\"\n---\n\n# skill-doctor — grade the agent setup from real sessions\n\n> **Privacy is the contract.** Everything runs locally. Transcripts are condensed,\n> secret-redacted, chmod-0600, and never uploaded — the only shareable artifact is\n> the report the user chooses to share.\n\nRun from the repo being graded. Every artifact goes to one fresh scratch dir, never\ninto the repo:\n\n```bash\nRUN=\"$(mktemp -d \"${TMPDIR:-\u002Ftmp}\u002Fskill-doctor-XXXXXXXX\")\"\npython scripts\u002Fcollect_sessions.py --out \"$RUN\"          # 1 — harvest + redact\n```\n\n**1 — Collect.** Scans Claude Code project-history JSONL and Codex rollouts,\ndiscovers repo skills (`.claude\u002Fskills`, `.agents\u002Fskills`, `.codex\u002Fskills`, plugin\nlayouts), detects skill usage (`Skill` invocations, slash commands, SKILL.md paths), samples\nnewest-first, and writes redacted transcripts. Read `$RUN\u002Finventory.json`: if\n`sessions_sampled` is 0, tell the user there is nothing recent to score (suggest\n`--days 90` or `--repo`) and stop. `skills_found` 0 is fine — the report becomes a\ncase for creating skills.\n\n**2 — Score.** `python scripts\u002Fscore_aggregator.py --inventory \"$RUN\u002Finventory.json\"\n--emit-template > \"$RUN\u002Fsession_scores.json\"`. Read each transcript in\n`$RUN\u002Ftranscripts\u002F` and judge it against **both** rubrics — `scorers\u002Fefficiency.md`\nand `scorers\u002Fcode-quality.md`. Fill the template with a **label from the rubric's\ntable** and a 1–3 sentence reason citing transcript specifics. Never invent numeric\nscores — the aggregator derives them from labels. Use `insufficient_evidence` when\na transcript shows no judgeable diff. Also write 1–5 `top_findings`: the most\nimpactful cross-session patterns, concrete and specific.\n\n**3 — Draft edits.** Follow `references\u002Fskill_edit_governance.md` (the filing bar:\nwould a competent agent with the current instructions still fail this way?). For\neach suggestion that clears it, write the full improved SKILL.md to\n`$RUN\u002Fproposed\u002F\u003Cskill>\u002FSKILL.md`, produce `diff -u \u003Ccurrent> \u003Cproposed>`, and record\nit in `$RUN\u002Fsuggestions.json` citing the sampled session id(s) that motivated it.\nZero suggestions is a valid success — say why per finding. Never modify the user's\nreal skill files in this step.\n\n**4 — Aggregate (the gate).** `python scripts\u002Fscore_aggregator.py --inventory\n\"$RUN\u002Finventory.json\" --scores \"$RUN\u002Fsession_scores.json\" --suggestions\n\"$RUN\u002Fsuggestions.json\"`. It validates labels against the rubric tables, refuses\nscores for unsampled sessions, requires substantive reasons, rejects suggestions\nthat cite no scored session, computes `overall = 0.5·efficiency +\n0.35·code_quality + 0.15·skill_coverage`, and writes `report.json`. **Exit 4 is a\nstop**: fix what it names and re-run; never hand-edit report.json around it.\n\n**5 — Render + tell.** `python scripts\u002Frender_report.py --report \"$RUN\u002Freport.json\"`\n→ one self-contained `report.html` (no JS, no CDN, dark-mode + print-to-PDF). Then\ntell the user the grade and the top findings in text, link\n`file:\u002F\u002F$RUN\u002Freport.html`, and ask whether to apply the proposed diffs to their\nreal skills — apply only on an explicit yes, skill by skill.\n\n## Hard rules\n\n1. **Never upload transcripts, session files, or any excerpt.** Local only.\n2. **Labels only, from the rubric tables.** The aggregator owns all arithmetic.\n3. **Every suggestion traces to a scored session** — or it is dropped. Generic best practice is not evidence.\n4. **Zero suggestions is a success**, not a failure to report around.\n5. **Exit 4 from the aggregator is a stop**, not an error to swallow or bypass.\n6. **Never touch the user's real skill files** without an explicit per-skill yes; proposed edits live under `$RUN\u002Fproposed\u002F`.\n7. **A proposed skill edit follows write-a-skill discipline** — trigger phrase in the description, smallest change that expresses the rule, replace over append.\n\n## Scripts\n\n| Script | Role | Exit codes |\n|---|---|---|\n| `scripts\u002Fcollect_sessions.py` | Harvest Claude Code + Codex sessions, redact secrets, sample, inventory | 0 · 3 bad input |\n| `scripts\u002Fscore_aggregator.py` | Validate labels\u002Freasons\u002Fsuggestions, compute grade, emit report.json | 0 · 2 warnings · 3 bad input · 4 validation failure |\n| `scripts\u002Frender_report.py` | report.json → single self-contained report.html | 0 · 3 bad input |\n\nAll support `--help`, `--output json`, and `--sample` (no real history needed).\n\n## References and assets\n\n- [`scorers\u002Fefficiency.md`](scorers\u002Fefficiency.md) · [`scorers\u002Fcode-quality.md`](scorers\u002Fcode-quality.md) — the two rubrics, preserved verbatim from upstream\n- [`references\u002Ftranscript_scoring_canon.md`](references\u002Ftranscript_scoring_canon.md) — why rubric-anchored LLM judging works and where it fails (7 sources)\n- [`references\u002Fsession_mining_privacy.md`](references\u002Fsession_mining_privacy.md) — the local-only contract, redaction pattern canon (7 sources)\n- [`references\u002Fskill_edit_governance.md`](references\u002Fskill_edit_governance.md) — the filing bar for proposing skill edits (7 sources)\n- [`assets\u002Fsession_scores.example.json`](assets\u002Fsession_scores.example.json) · [`assets\u002Fsuggestions.example.json`](assets\u002Fsuggestions.example.json) · [`assets\u002Freport.example.json`](assets\u002Freport.example.json) — the three handoff shapes\n","# skill-doctor — grade the agent setup from real sessions\n\n> **Privacy is the contract.** Everything runs locally. Transcripts are condensed,\n> secret-redacted, chmod-0600, and never uploaded — the only shareable artifact is\n> the report the user chooses to share.\n\nRun from the repo being graded. Every artifact goes to one fresh scratch dir, never\ninto the repo:\n\n```bash\nRUN=\"$(mktemp -d \"${TMPDIR:-\u002Ftmp}\u002Fskill-doctor-XXXXXXXX\")\"\npython scripts\u002Fcollect_sessions.py --out \"$RUN\"          # 1 — harvest + redact\n```\n\n**1 — Collect.** Scans Claude Code project-history JSONL and Codex rollouts,\ndiscovers repo skills (`.claude\u002Fskills`, `.agents\u002Fskills`, `.codex\u002Fskills`, plugin\nlayouts), detects skill usage (`Skill` invocations, slash commands, SKILL.md paths), samples\nnewest-first, and writes redacted transcripts. Read `$RUN\u002Finventory.json`: if\n`sessions_sampled` is 0, tell the user there is nothing recent to score (suggest\n`--days 90` or `--repo`) and stop. `skills_found` 0 is fine — the report becomes a\ncase for creating skills.\n\n**2 — Score.** `python scripts\u002Fscore_aggregator.py --inventory \"$RUN\u002Finventory.json\"\n--emit-template > \"$RUN\u002Fsession_scores.json\"`. Read each transcript in\n`$RUN\u002Ftranscripts\u002F` and judge it against **both** rubrics — `scorers\u002Fefficiency.md`\nand `scorers\u002Fcode-quality.md`. Fill the template with a **label from the rubric's\ntable** and a 1–3 sentence reason citing transcript specifics. Never invent numeric\nscores — the aggregator derives them from labels. Use `insufficient_evidence` when\na transcript shows no judgeable diff. Also write 1–5 `top_findings`: the most\nimpactful cross-session patterns, concrete and specific.\n\n**3 — Draft edits.** Follow `references\u002Fskill_edit_governance.md` (the filing bar:\nwould a competent agent with the current instructions still fail this way?). For\neach suggestion that clears it, write the full improved SKILL.md to\n`$RUN\u002Fproposed\u002F\u003Cskill>\u002FSKILL.md`, produce `diff -u \u003Ccurrent> \u003Cproposed>`, and record\nit in `$RUN\u002Fsuggestions.json` citing the sampled session id(s) that motivated it.\nZero suggestions is a valid success — say why per finding. Never modify the user's\nreal skill files in this step.\n\n**4 — Aggregate (the gate).** `python scripts\u002Fscore_aggregator.py --inventory\n\"$RUN\u002Finventory.json\" --scores \"$RUN\u002Fsession_scores.json\" --suggestions\n\"$RUN\u002Fsuggestions.json\"`. It validates labels against the rubric tables, refuses\nscores for unsampled sessions, requires substantive reasons, rejects suggestions\nthat cite no scored session, computes `overall = 0.5·efficiency +\n0.35·code_quality + 0.15·skill_coverage`, and writes `report.json`. **Exit 4 is a\nstop**: fix what it names and re-run; never hand-edit report.json around it.\n\n**5 — Render + tell.** `python scripts\u002Frender_report.py --report \"$RUN\u002Freport.json\"`\n→ one self-contained `report.html` (no JS, no CDN, dark-mode + print-to-PDF). Then\ntell the user the grade and the top findings in text, link\n`file:\u002F\u002F$RUN\u002Freport.html`, and ask whether to apply the proposed diffs to their\nreal skills — apply only on an explicit yes, skill by skill.\n\n## Hard rules\n\n1. **Never upload transcripts, session files, or any excerpt.** Local only.\n2. **Labels only, from the rubric tables.** The aggregator owns all arithmetic.\n3. **Every suggestion traces to a scored session** — or it is dropped. Generic best practice is not evidence.\n4. **Zero suggestions is a success**, not a failure to report around.\n5. **Exit 4 from the aggregator is a stop**, not an error to swallow or bypass.\n6. **Never touch the user's real skill files** without an explicit per-skill yes; proposed edits live under `$RUN\u002Fproposed\u002F`.\n7. **A proposed skill edit follows write-a-skill discipline** — trigger phrase in the description, smallest change that expresses the rule, replace over append.\n\n## Scripts\n\n| Script | Role | Exit codes |\n|---|---|---|\n| `scripts\u002Fcollect_sessions.py` | Harvest Claude Code + Codex sessions, redact secrets, sample, inventory | 0 · 3 bad input |\n| `scripts\u002Fscore_aggregator.py` | Validate labels\u002Freasons\u002Fsuggestions, compute grade, emit report.json | 0 · 2 warnings · 3 bad input · 4 validation failure |\n| `scripts\u002Frender_report.py` | report.json → single self-contained report.html | 0 · 3 bad input |\n\nAll support `--help`, `--output json`, and `--sample` (no real history needed).\n\n## References and assets\n\n- [`scorers\u002Fefficiency.md`](scorers\u002Fefficiency.md) · [`scorers\u002Fcode-quality.md`](scorers\u002Fcode-quality.md) — the two rubrics, preserved verbatim from upstream\n- [`references\u002Ftranscript_scoring_canon.md`](references\u002Ftranscript_scoring_canon.md) — why rubric-anchored LLM judging works and where it fails (7 sources)\n- [`references\u002Fsession_mining_privacy.md`](references\u002Fsession_mining_privacy.md) — the local-only contract, redaction pattern canon (7 sources)\n- [`references\u002Fskill_edit_governance.md`](references\u002Fskill_edit_governance.md) — the filing bar for proposing skill edits (7 sources)\n- [`assets\u002Fsession_scores.example.json`](assets\u002Fsession_scores.example.json) · [`assets\u002Fsuggestions.example.json`](assets\u002Fsuggestions.example.json) · [`assets\u002Freport.example.json`](assets\u002Freport.example.json) — the three handoff shapes\n",{"name":5,"license":14,"metadata":48,"description":7,"argument-hint":52},{"version":49,"build_pattern":50,"distinct_from":51},"1.0.0","Rebuild of warpdotdev\u002Fcommon-skills skill-doctor (MIT): harvest → LLM-judges-with-rubrics → deterministic aggregation gate → self-contained HTML report","skillopt-sleep (nightly automated replay loop with adopt gate; this is one interactive graded pass); write-a-skill (authors a skill from expertise; this improves skills from observed session evidence); self-eval (grades this session's own work; this grades a window of past sessions)","[optional: --repo PATH, --days N, or a question about the setup]","main","510dd2917e051cc953dc55f56fd6ef442aa01b38","claude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi","https:\u002F\u002Fgithub.com\u002Fmne20114-hash","2026-10-28 07:50:31",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add mne20114-hash\u002Fgest35bi --skill skill-doctor",{"tool":65,"command":66},"bunx","bunx skills add mne20114-hash\u002Fgest35bi --skill skill-doctor",{"tool":68,"command":69},"pnpm","pnpm dlx skills add mne20114-hash\u002Fgest35bi --skill skill-doctor","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002FSKILL.md",true,[73,79,83,87,93,97,101,105,109,115,119],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"assets\u002Freport.example.json","ASSET","资产文件",2132,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Fassets\u002Freport.example.json",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"assets\u002Fsession_scores.example.json",1675,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Fassets\u002Fsession_scores.example.json",{"path":84,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":85,"sourceHtmlUrl":86},"assets\u002Fsuggestions.example.json",1197,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Fassets\u002Fsuggestions.example.json",{"path":88,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":91,"sourceHtmlUrl":92},"references\u002Fsession_mining_privacy.md","REFERENCE","参考文档",5082,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Freferences\u002Fsession_mining_privacy.md",{"path":94,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fskill_edit_governance.md",5895,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Freferences\u002Fskill_edit_governance.md",{"path":98,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Ftranscript_scoring_canon.md",4804,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Freferences\u002Ftranscript_scoring_canon.md",{"path":102,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":103,"sourceHtmlUrl":104},"scorers\u002Fcode-quality.md",4781,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Fscorers\u002Fcode-quality.md",{"path":106,"resourceType":89,"resourceTypeDesc":90,"sizeBytes":107,"sourceHtmlUrl":108},"scorers\u002Fefficiency.md",3340,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Fscorers\u002Fefficiency.md",{"path":110,"resourceType":111,"resourceTypeDesc":112,"sizeBytes":113,"sourceHtmlUrl":114},"scripts\u002Fcollect_sessions.py","SCRIPT","脚本",33284,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Fscripts\u002Fcollect_sessions.py",{"path":116,"resourceType":111,"resourceTypeDesc":112,"sizeBytes":117,"sourceHtmlUrl":118},"scripts\u002Frender_report.py",11737,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Fscripts\u002Frender_report.py",{"path":120,"resourceType":111,"resourceTypeDesc":112,"sizeBytes":121,"sourceHtmlUrl":122},"scripts\u002Fscore_aggregator.py",15734,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fskill-doctor\u002Fskills\u002Fskill-doctor\u002Fscripts\u002Fscore_aggregator.py",[124,158,180,203,228,247],{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":139,"publishedAt":43},"01M6C27N3H0F9RGG2KASPASH04","syllabus","Generates a curated supplementary reading list from any course syllabus using Consensus academic search. Grill-me intake (syllabus input format + course audience + year range) plus a grouping forcing-options checkpoint before any search runs — so the reading list matches the course's level and recency need. Parses the syllabus to extract topics and learning outcomes, searches Consensus for recent peer-reviewed papers per topic, and produces a professionally formatted .docx with clickable Consensus links, plain-language summaries calibrated to audience level, and Bloom-higher-order discussion questions tied to course learning goals. Use when the user uploads a syllabus, course outline, or curriculum document and wants supplementary readings (e.g., 'create a reading list from this syllabus', 'find recent papers for my course') — even casual mentions with a syllabus attached should trigger this skill.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fsyllabus\u002Fskills\u002Fsyllabus\u002FSKILL.md",[130,133,134],{"categoryNo":131,"categoryName":132,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[126,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,26,157],"docx","pr","create","intake","search","audit","integrity","checkpoint","learning","js","format","direct","Rules","find","consensus","list","extract","outcomes",{"skillNo":159,"name":160,"nameZh":6,"description":161,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":162,"categories":163,"keywords":168,"publishedAt":43},"01M6C27DMGZ4D6ERNHSVA8H8RJ","pulse","Multi-source recency research skill that takes the pulse of any topic across Reddit, Hacker News, the open web, and optionally X\u002FTwitter within a configurable recent window (default 30 days). Forcing intake clarifies topic specificity, angle (trend\u002Fsentiment\u002Fproblems\u002Fopportunities\u002Fcomparison), time window, and platform scope before searching. Returns a synthesized briefing with citations, engagement metrics, and cross-platform pattern analysis. Use when the user requests multi-source recency intelligence on a topic (e.g., 'pulse on [topic]', 'what's happening with [topic]', 'what are people saying about [topic]', 'current conversation about [topic]', 'take the pulse of [topic]', 'trending: [topic]', 'find me info on [topic]'), and for competitor research, trend discovery, tool comparisons, and audience sentiment analysis.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fpulse\u002Fskills\u002Fpulse\u002FSKILL.md",[164,167],{"categoryNo":165,"categoryName":166,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[160,169,170,141,171,143,145,172,173,146,174,175,176,177,151,152,178,153,179,26],"research","web","sentiment analysis","sentiment","Citations","analysis","discovery","open","tool","twitter","user",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":191,"publishedAt":43},"01M6C279M59X635VPD6Y9R616J","patent","Patent prior-art and landscape intelligence skill — not generic patent help. Commits to one of five sub-use-cases via forcing intake (novelty search \u002F freedom-to-operate \u002F competitive landscape \u002F acquisition diligence \u002F litigation prior-art) before any search runs. Searches Google Patents, Espacenet, USPTO, and optionally Lens.org for citation-graph signals. Output is an editable Word document (.docx) with verdict, ranked closest art (claim-text extracted), CPC-class-aware landscape, family-resolved hits, geographic coverage, FTO flags where applicable, strategy recommendations, and full audit log. Use when the user asks for patent searching or analysis (e.g., 'prior art search for [invention]', 'freedom to operate analysis for [product]'). Produces search signal, not legal advice — always recommends consulting a patent attorney before filing or licensing decisions. Trademark, copyright, and trade-secret questions are out of scope.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fpatent\u002Fskills\u002Fpatent\u002FSKILL.md",[186,187,188],{"categoryNo":131,"categoryName":132,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":189,"categoryName":190,"taxonomyType":137,"taxonomyTypeDesc":138},"01JBSEEDCAT000000000000037","律师与法务",[182,140,141,192,193,194,143,144,145,174,195,196,197,152,198,199,200,201,26,202],"competitive landscape","routing","product","commits","Word","text","acquisition","decisions","strategy","coverage","advice",{"skillNo":204,"name":205,"nameZh":6,"description":206,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":207,"categories":208,"keywords":213,"publishedAt":43},"01M6C275RZWNRB3CCB14QQM7ES","notebooklm","Browser automation skill for controlling Google's NotebookLM. Use when the user wants anything done in NotebookLM (e.g., 'open NotebookLM', 'check my [name] notebook', 'ask my notebook about X', 'add [source] to NotebookLM', 'generate a Video Overview from my notebook', 'use NotebookLM Studio'). Handles reading and querying notebooks, adding sources (URLs, text, files, YouTube links, synthesized content), generating Studio outputs (Audio\u002FVideo Overviews, Mind Maps, Reports incl. Briefing Doc\u002FStudy Guide\u002FFAQ, Flashcards, Quiz, slide decks, infographics — discover the exact set from the live Studio panel; the UI evolves fast), and creating new notebooks. Requires browser automation environment — fails gracefully when unavailable.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fnotebooklm\u002Fskills\u002Fnotebooklm\u002FSKILL.md",[209,212],{"categoryNo":210,"categoryName":211,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[205,214,215,169,216,217,218,219,220,221,222,223,176,197,151,224,225,226,227,26],"google","browser automation","automation","generate","browser","fast","SET","doc","Video","Audio","check","ask","environment","overview",{"skillNo":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":232,"categories":233,"keywords":238,"publishedAt":43},"01M6C2717B0QDJ3Y4PJP77BMW5","litreview","Academic literature orientation skill that searches papers via free keyless APIs (PubMed E-utilities + OpenAlex) by default — with the Consensus MCP as an optional enhancement lane when connected — builds a strategic search plan using PICO (default) or SPIDER \u002F Decomposition \u002F hybrid as fallbacks, and synthesizes findings into a formatted Word (.docx) research guide. Grill-me intake (research question specificity + framework hint + tentative depth) before the recon search; a second forcing checkpoint after Phase 2 confirms framework + sub-areas + depth before searches consume budget. Configurable depth (5\u002F10\u002F20 queries) controls coverage vs. speed. Output is a 'launching pad' — an orientation guide that lets a researcher dive in confidently, not a finished review. Use when the user starts literature-oriented research (e.g., 'litreview on [topic]', 'literature review on [topic]', 'I'm starting a literature review on X', 'I'm writing a paper on X', 'help me research X', 'I'm doing research on X', 'can you help me research X'). Do NOT use for single one-off paper searches wanting a quick list — that's a plain PubMed\u002FOpenAlex (or Consensus) query.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Flitreview\u002Fskills\u002Flitreview\u002FSKILL.md",[234,235,236,237],{"categoryNo":131,"categoryName":132,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":165,"categoryName":166,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},[230,140,239,240,241,141,143,144,242,243,244,147,245,196,246,152,154,29,201,26],"MCP","writing","review","lane","keyless","literature review","plan","researcher",{"skillNo":248,"name":249,"nameZh":6,"description":250,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":251,"categories":252,"keywords":257,"publishedAt":43},"01M6C26X5G3WDVWQ40Y16ZS19F","grants","NIH grant research skill for clinical researchers. Grill-me intake (research idea + career stage + preliminary data + environment + submission posture + known institute targets) locks down the funding strategy before any search runs. Runs a 5-facet Consensus positioning analysis (with draft Significance\u002FInnovation language), maps the research to the right NIH institutes and study sections via RePORTER, finds NOSIs and funded overlap, and produces an editable Word document (.docx) with budget\u002Fscope-aware mechanism recommendations, submission timelines, and a mandatory program officer recommendation. Use when the user asks about research funding or makes any grant-related request (e.g., 'grants for [topic]', 'find grants for my research idea', 'what grants match my research', 'help me find NIH funding', 'grant opportunities for my research'). NIH-only scope — non-NIH funders (PCORI, DOD CDMRP, VA, foundations) are out of scope and flagged at intake.","https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fresearch\u002Fgrants\u002Fskills\u002Fgrants\u002FSKILL.md",[253,254,255,256],{"categoryNo":131,"categoryName":132,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":165,"categoryName":166,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":135,"categoryName":136,"taxonomyType":137,"taxonomyTypeDesc":138},[249,140,169,141,258,143,144,145,259,260,146,174,196,152,153,154,200,226,26,261],"help","data","Budget","innovation"]