[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6C1HFQCMAM09FQQ8FF7XQ01":3,"skill-same:01M6C1HFQCMAM09FQQ8FF7XQ01":143},{"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},"01M6C1HFQCMAM09FQQ8FF7XQ01","agent-memory",null,"Use when a project's CLAUDE.md has grown past what anyone reads and you want the agent to learn durable facts from its own sessions instead — or when asking why the agent keeps re-learning the same correction, why a remembered rule is wrong, or where a memory line came from. Implements a four-tier store (L0 transcripts \u002F L1 candidates \u002F L2 project context \u002F L3 stable persona) where promotion is earned by recurrence across sessions and days, never by one confident statement, and nothing reaches a committed file without a human adopting it.","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\u002Fagent-memory\u002Fskills\u002Fagent-memory\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],"agent memory","memory","vault","use","claude code hooks","write","system","improve","wiki","learning","persona","why","engineering","audit","file","database","learn","remember","status","sessions","2026-11-10 10:12:30",false,"---\nname: agent-memory\ndescription: Use when a project's CLAUDE.md has grown past what anyone reads and you want the agent to learn durable facts from its own sessions instead — or when asking why the agent keeps re-learning the same correction, why a remembered rule is wrong, or where a memory line came from. Implements a four-tier store (L0 transcripts \u002F L1 candidates \u002F L2 project context \u002F L3 stable persona) where promotion is earned by recurrence across sessions and days, never by one confident statement, and nothing reaches a committed file without a human adopting it.\nargument-hint: \"[optional: status | why \\\"\u003Cclaim>\\\" | a tier name]\"\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  build_pattern: \"Tencent TencentDB-Agent-Memory's tiering concept rebuilt natively on Claude Code hooks; deterministic recurrence gates, no LLM, no database\"\n  distinct_from: \"llm-wiki (a vault you write on purpose; this writes itself from sessions); skillopt-sleep (replays tasks to improve a skill; this extracts facts to remember); memory-engineering (audits and prices any memory system; this IS one, and is a legitimate subject of that audit)\"\n---\n\n# Agent Memory — promotion is earned, not asserted\n\n> **Portability:** stdlib only. No database, no embeddings, no network, no LLM calls.\n\n## The problem\n\nA project's `CLAUDE.md` is a memory system with one tier and no eviction: every\ndurable fact and every passing preference land in the same always-loaded file,\nuntil the important lines are diluted by the incidental ones. Facts learned\nmid-session vanish at teardown unless someone writes them down.\n\n**The fix is not more storage — it is a promotion ladder.** A claim earns its\nway toward always-loaded context by recurring; a human confirms the last step.\n\n## The four tiers\n\nTiers are distinguished by **injection policy**, not storage format.\n\n| Tier | Holds | Injected | Committed |\n|---|---|---|---|\n| **L0** | raw session transcripts | never | no (already on disk) |\n| **L1** | candidate atoms | on relevance, at prompt time | no (gitignored) |\n| **L2** | this project's context | every session start | yes, after adopt |\n| **L3** | stable cross-project persona | always | yes, after adopt |\n\n## The gates\n\nNothing moves up because it sounded important. It moves up because it recurred.\n\n- **L0 → L1** — an explicit marker fires (a directive, a correction, a stated\n  preference, a named lesson, a reproducible failure). Rule-based, high\n  precision, deliberately low recall.\n- **L1 → L2** — ≥ 3 distinct sessions spanning ≥ 2 distinct calendar days. A\n  claim stated outright needs 2 sessions; the distinct-day rule still applies. A\n  verified claim promotes on one observation and is the only day-exempt path.\n- **L2 → L3** — held in ≥ 2 distinct projects, aged ≥ 30 days, uncontested.\n\n**Two gates refuse rather than guess.** A claim whose text was altered by\nredaction never promotes on evidence alone — the flag firing is evidence the\nsource was sensitive, and a lexical filter finding one secret is not proof it\nfound all of them. A claim with an open contradiction is frozen at L1 until a\nhuman resolves it; the incumbent is never silently overwritten.\n\n## Use it\n\n```bash\n# what is remembered, and what is blocking the next promotion\npython3 scripts\u002Fmemory_inspect.py --tier L1\n\n# where did this line come from — sessions, days, transcript, quoted source\npython3 scripts\u002Fmemory_inspect.py --why \"PR base branch is dev\"\n\n# every claim with an open contradiction, both directions of the join\npython3 scripts\u002Fmemory_inspect.py --contested\n\n# dry-run the promotion pass; writes nothing\npython3 scripts\u002Fmemory_promote.py\n```\n\nThree hooks run the loop unattended: `SessionStart` injects L2 + L3,\n`UserPromptSubmit` recalls relevant L1 atoms, `SessionEnd` captures and stages.\nEach is disabled independently with `AGENT_MEMORY_SESSIONSTART=0`,\n`AGENT_MEMORY_USERPROMPTSUBMIT=0`, `AGENT_MEMORY_SESSIONEND=0`. Every hook fails\nopen: a broken memory system costs you memory, never a session.\n\n## Hard rules\n\n1. **Redact before writing.** Every atom passes the filter before it reaches\n   disk. Anything altered is quarantined from promotion.\n2. **Propose, never apply.** Promotions land in `.memory\u002Fstaged\u002F`. Only an\n   explicit `\u002Fcs:memory adopt` touches a `CLAUDE.md`, and it backs both up first.\n3. **Cite, don't invent.** Every atom carries a back-pointer to the transcript\n   line that produced it. `--why` resolving to *ambiguous* prints nothing rather\n   than guess: a wrong citation is worse than a missing one.\n4. **Never surface a contested claim as fact.** It is still injected — hiding\n   the conflict is worse — but always tagged.\n5. **The committed tiers carry no paths.** Promotion strips the back-pointer\n   prefix, which embeds an OS username.\n\n## Forcing questions\n\nWalk these one at a time before trusting the store.\n\n1. Which line in your `CLAUDE.md` did you last actually read before acting?\n2. Would you rather the agent forget a true thing, or remember a false one?\n3. When two remembered rules disagree, who decides — and when?\n4. What would make you delete `.memory\u002F` entirely?\n\nRationale, open decisions, field schema: [`..\u002F..\u002FDESIGN.md`](..\u002F..\u002FDESIGN.md).\n","# Agent Memory — promotion is earned, not asserted\n\n> **Portability:** stdlib only. No database, no embeddings, no network, no LLM calls.\n\n## The problem\n\nA project's `CLAUDE.md` is a memory system with one tier and no eviction: every\ndurable fact and every passing preference land in the same always-loaded file,\nuntil the important lines are diluted by the incidental ones. Facts learned\nmid-session vanish at teardown unless someone writes them down.\n\n**The fix is not more storage — it is a promotion ladder.** A claim earns its\nway toward always-loaded context by recurring; a human confirms the last step.\n\n## The four tiers\n\nTiers are distinguished by **injection policy**, not storage format.\n\n| Tier | Holds | Injected | Committed |\n|---|---|---|---|\n| **L0** | raw session transcripts | never | no (already on disk) |\n| **L1** | candidate atoms | on relevance, at prompt time | no (gitignored) |\n| **L2** | this project's context | every session start | yes, after adopt |\n| **L3** | stable cross-project persona | always | yes, after adopt |\n\n## The gates\n\nNothing moves up because it sounded important. It moves up because it recurred.\n\n- **L0 → L1** — an explicit marker fires (a directive, a correction, a stated\n  preference, a named lesson, a reproducible failure). Rule-based, high\n  precision, deliberately low recall.\n- **L1 → L2** — ≥ 3 distinct sessions spanning ≥ 2 distinct calendar days. A\n  claim stated outright needs 2 sessions; the distinct-day rule still applies. A\n  verified claim promotes on one observation and is the only day-exempt path.\n- **L2 → L3** — held in ≥ 2 distinct projects, aged ≥ 30 days, uncontested.\n\n**Two gates refuse rather than guess.** A claim whose text was altered by\nredaction never promotes on evidence alone — the flag firing is evidence the\nsource was sensitive, and a lexical filter finding one secret is not proof it\nfound all of them. A claim with an open contradiction is frozen at L1 until a\nhuman resolves it; the incumbent is never silently overwritten.\n\n## Use it\n\n```bash\n# what is remembered, and what is blocking the next promotion\npython3 scripts\u002Fmemory_inspect.py --tier L1\n\n# where did this line come from — sessions, days, transcript, quoted source\npython3 scripts\u002Fmemory_inspect.py --why \"PR base branch is dev\"\n\n# every claim with an open contradiction, both directions of the join\npython3 scripts\u002Fmemory_inspect.py --contested\n\n# dry-run the promotion pass; writes nothing\npython3 scripts\u002Fmemory_promote.py\n```\n\nThree hooks run the loop unattended: `SessionStart` injects L2 + L3,\n`UserPromptSubmit` recalls relevant L1 atoms, `SessionEnd` captures and stages.\nEach is disabled independently with `AGENT_MEMORY_SESSIONSTART=0`,\n`AGENT_MEMORY_USERPROMPTSUBMIT=0`, `AGENT_MEMORY_SESSIONEND=0`. Every hook fails\nopen: a broken memory system costs you memory, never a session.\n\n## Hard rules\n\n1. **Redact before writing.** Every atom passes the filter before it reaches\n   disk. Anything altered is quarantined from promotion.\n2. **Propose, never apply.** Promotions land in `.memory\u002Fstaged\u002F`. Only an\n   explicit `\u002Fcs:memory adopt` touches a `CLAUDE.md`, and it backs both up first.\n3. **Cite, don't invent.** Every atom carries a back-pointer to the transcript\n   line that produced it. `--why` resolving to *ambiguous* prints nothing rather\n   than guess: a wrong citation is worse than a missing one.\n4. **Never surface a contested claim as fact.** It is still injected — hiding\n   the conflict is worse — but always tagged.\n5. **The committed tiers carry no paths.** Promotion strips the back-pointer\n   prefix, which embeds an OS username.\n\n## Forcing questions\n\nWalk these one at a time before trusting the store.\n\n1. Which line in your `CLAUDE.md` did you last actually read before acting?\n2. Would you rather the agent forget a true thing, or remember a false one?\n3. When two remembered rules disagree, who decides — and when?\n4. What would make you delete `.memory\u002F` entirely?\n\nRationale, open decisions, field schema: [`..\u002F..\u002FDESIGN.md`](..\u002F..\u002FDESIGN.md).\n",{"name":5,"license":14,"metadata":48,"description":7,"argument-hint":52},{"version":49,"build_pattern":50,"distinct_from":51},"1.0.0","Tencent TencentDB-Agent-Memory's tiering concept rebuilt natively on Claude Code hooks; deterministic recurrence gates, no LLM, no database","llm-wiki (a vault you write on purpose; this writes itself from sessions); skillopt-sleep (replays tasks to improve a skill; this extracts facts to remember); memory-engineering (audits and prices any memory system; this IS one, and is a legitimate subject of that audit)","[optional: status | why \"\u003Cclaim>\" | a tier name]","main","510dd2917e051cc953dc55f56fd6ef442aa01b38","claude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory","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 agent-memory",{"tool":65,"command":66},"bunx","bunx skills add mne20114-hash\u002Fgest35bi --skill agent-memory",{"tool":68,"command":69},"pnpm","pnpm dlx skills add mne20114-hash\u002Fgest35bi --skill agent-memory","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory\u002FSKILL.md",true,[73,79,85,89,93,97,101,105,109,113,117,123,127,131,135,139],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"assets\u002Fmemory_schema.json","ASSET","资产文件",15600,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory\u002Fassets\u002Fmemory_schema.json",{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"CHANGELOG.md","REFERENCE","参考文档",710,"https:\u002F\u002Fgithub.com\u002Fluongnv89\u002Fasm\u002Fblob\u002Fmain\u002Fskills\u002Fskill-changelog\u002FCHANGELOG.md",{"path":86,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":87,"sourceHtmlUrl":88},"docs\u002FREADME.md",2958,"https:\u002F\u002Fgithub.com\u002Fluongnv89\u002Fasm\u002Fblob\u002Fmain\u002Fskills\u002Fskill-changelog\u002Fdocs\u002FREADME.md",{"path":90,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":91,"sourceHtmlUrl":92},"evals\u002Fevals.json",6999,"https:\u002F\u002Fgithub.com\u002Fluongnv89\u002Fasm\u002Fblob\u002Fmain\u002Fskills\u002Fskill-changelog\u002Fevals\u002Fevals.json",{"path":94,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":95,"sourceHtmlUrl":96},"references\u002Fbackfill.md",1928,"https:\u002F\u002Fgithub.com\u002Fluongnv89\u002Fasm\u002Fblob\u002Fmain\u002Fskills\u002Fskill-changelog\u002Freferences\u002Fbackfill.md",{"path":98,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":99,"sourceHtmlUrl":100},"references\u002Fedge-cases.md",2195,"https:\u002F\u002Fgithub.com\u002Fluongnv89\u002Fasm\u002Fblob\u002Fmain\u002Fskills\u002Fskill-changelog\u002Freferences\u002Fedge-cases.md",{"path":102,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":103,"sourceHtmlUrl":104},"references\u002Fentry-format.md",3506,"https:\u002F\u002Fgithub.com\u002Fluongnv89\u002Fasm\u002Fblob\u002Fmain\u002Fskills\u002Fskill-changelog\u002Freferences\u002Fentry-format.md",{"path":106,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":107,"sourceHtmlUrl":108},"references\u002Fhook_capture_discipline.md",6977,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory\u002Freferences\u002Fhook_capture_discipline.md",{"path":110,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":111,"sourceHtmlUrl":112},"references\u002Fpromotion_gate_design.md",7529,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory\u002Freferences\u002Fpromotion_gate_design.md",{"path":114,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":115,"sourceHtmlUrl":116},"references\u002Ftiered_memory_canon.md",6800,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory\u002Freferences\u002Ftiered_memory_canon.md",{"path":118,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":121,"sourceHtmlUrl":122},"scripts\u002Fmemory_core.py","SCRIPT","脚本",14961,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory\u002Fscripts\u002Fmemory_core.py",{"path":124,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":125,"sourceHtmlUrl":126},"scripts\u002Fmemory_extract.py",8296,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory\u002Fscripts\u002Fmemory_extract.py",{"path":128,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":129,"sourceHtmlUrl":130},"scripts\u002Fmemory_inspect.py",10983,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory\u002Fscripts\u002Fmemory_inspect.py",{"path":132,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":133,"sourceHtmlUrl":134},"scripts\u002Fmemory_promote.py",10269,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory\u002Fscripts\u002Fmemory_promote.py",{"path":136,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":137,"sourceHtmlUrl":138},"scripts\u002Fskill_versions.py",15081,"https:\u002F\u002Fgithub.com\u002Fluongnv89\u002Fasm\u002Fblob\u002Fmain\u002Fskills\u002Fskill-changelog\u002Fscripts\u002Fskill_versions.py",{"path":140,"resourceType":119,"resourceTypeDesc":120,"sizeBytes":141,"sourceHtmlUrl":142},"scripts\u002Fvalidate_examples.py",22759,"https:\u002F\u002Fgithub.com\u002Fmne20114-hash\u002Fgest35bi\u002Fblob\u002Fmain\u002Fclaude-skills\u002Fengineering\u002Fagent-memory\u002Fskills\u002Fagent-memory\u002Fscripts\u002Fvalidate_examples.py",[144,176,198,221,246,266],{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":159,"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",[150,153,154],{"categoryNo":151,"categoryName":152,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":155,"categoryName":156,"taxonomyType":157,"taxonomyTypeDesc":158},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[146,160,161,162,163,164,36,165,166,32,167,168,169,170,171,172,173,174,26,175],"docx","pr","create","intake","search","integrity","checkpoint","js","format","direct","Rules","find","consensus","list","extract","outcomes",{"skillNo":177,"name":178,"nameZh":6,"description":179,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":180,"categories":181,"keywords":186,"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",[182,185],{"categoryNo":183,"categoryName":184,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[178,187,188,161,189,163,36,190,191,165,192,193,194,195,169,170,196,171,197,26],"research","web","sentiment analysis","sentiment","Citations","analysis","discovery","open","tool","twitter","user",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":209,"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",[204,205,206],{"categoryNo":151,"categoryName":152,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":207,"categoryName":208,"taxonomyType":157,"taxonomyTypeDesc":158},"01JBSEEDCAT000000000000037","律师与法务",[200,160,161,210,211,212,163,164,36,192,213,214,215,170,216,217,218,219,26,220],"competitive landscape","routing","product","commits","Word","text","acquisition","decisions","strategy","coverage","advice",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":231,"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",[227,230],{"categoryNo":228,"categoryName":229,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[223,232,233,187,234,235,236,237,238,239,240,241,194,215,169,242,243,244,245,26],"google","browser automation","automation","generate","browser","fast","SET","doc","Video","Audio","check","ask","environment","overview",{"skillNo":247,"name":248,"nameZh":6,"description":249,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":250,"categories":251,"keywords":256,"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",[252,253,254,255],{"categoryNo":151,"categoryName":152,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":183,"categoryName":184,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":155,"categoryName":156,"taxonomyType":157,"taxonomyTypeDesc":158},[248,160,257,258,259,161,163,164,260,261,262,166,263,214,264,170,172,265,219,26],"MCP","writing","review","lane","keyless","literature review","plan","researcher","question",{"skillNo":267,"name":268,"nameZh":6,"description":269,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":270,"categories":271,"keywords":276,"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",[272,273,274,275],{"categoryNo":151,"categoryName":152,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":183,"categoryName":184,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":155,"categoryName":156,"taxonomyType":157,"taxonomyTypeDesc":158},[268,160,187,161,277,163,164,36,278,279,165,192,214,170,171,172,218,244,26,280],"help","data","Budget","innovation"]