[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ADVXVP7H7885R7WMXRQQ1Z":3,"skill-same:01M6ADVXVP7H7885R7WMXRQQ1Z":134},{"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":23,"publishedAt":45,"linkOnly":46,"markdownContent":47,"markdownBody":48,"frontMatter":49,"refName":50,"commitSha":51,"skillDirectory":52,"forksCount":53,"repositoryHtmlUrl":54,"repositoryHomepageUrl":55,"ownerHtmlUrl":56,"ownerBlogUrl":57,"ownerDescription":58,"updatedAt":59,"install":60,"resources":73},"01M6ADVXVP7H7885R7WMXRQQ1Z","mini-context-graph",null,"A persistent, compounding knowledge base combining Karpathy's LLM Wiki pattern\nwith a structured knowledge graph. Ingest documents once — the LLM writes wiki\npages, extracts entities\u002Frelations into the graph, and stores raw content for\nevidence retrieval. Knowledge accumulates and cross-references; it is never\nre-derived from scratch.","github\u002Fawesome-copilot","github","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F9919?v=4","01M6AC83DS133TPXFSP1DHNCC6","GitHub","01M6AD3PBDYPK7NVFJNJ66Y8RS",41162,"MIT","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44],"graph","mini context graph","context","never","entities","scratch","for","re","pages","knowledge base","pattern","references","ingest","evidence","documents","karpathy","llm wiki","knowledge","wiki","knowledge graph","content","2026-11-10 10:12:30",false,"---\nname: mini-context-graph\ndescription: |\n  A persistent, compounding knowledge base combining Karpathy's LLM Wiki pattern\n  with a structured knowledge graph. Ingest documents once — the LLM writes wiki\n  pages, extracts entities\u002Frelations into the graph, and stores raw content for\n  evidence retrieval. Knowledge accumulates and cross-references; it is never\n  re-derived from scratch.\n---\n\n# Mini Context Graph Skill\n\n## The Core Idea\n\nStandard RAG re-discovers knowledge from scratch on every query. This skill is different:\n\n1. **Wiki layer** — The LLM writes and maintains persistent markdown pages (summaries, entity pages, topic syntheses). Cross-references are already there. The wiki gets richer with every ingest.\n2. **Graph layer** — Entities and relations are extracted once and stored as a navigable knowledge graph. BFS traversal answers structural queries without re-reading sources.\n3. **Raw source layer** — Original documents are stored immutably with chunks. Provenance links tie every graph node and edge back to the exact text that supports it.\n\n> The LLM writes; the Python tools handle all bookkeeping.\n\n---\n\n## Three Layers\n\n| Layer | Where | What the LLM does | What Python does |\n|-------|-------|-------------------|-----------------|\n| **Raw Sources** | `data\u002Fdocuments.json` | Reads (never modifies) | Stores chunks + metadata |\n| **Wiki** | `wiki\u002F` (markdown) | Writes\u002Fupdates pages | Manages index.md + log.md |\n| **Graph** | `data\u002Fgraph.json` | Extracts entities + relations | Persists, deduplicates, traverses |\n\n---\n\n## ⚡ Quick Start for Agents\n\nA complete runnable version of this workflow is in `scripts\u002Ftemplate_agent_workflow.py` — copy and adapt it.\n\n```python\nfrom scripts.contextgraph import ContextGraphSkill\nfrom scripts.tools import wiki_store\n\nskill = ContextGraphSkill()\n\n# ===== INGEST WITH FULL RAG + WIKI =====\n# 1. Read references\u002Fingestion.md and references\u002Fontology.md first\n# 2. Extract entities and relations (LLM reasoning step)\nentities = [\n    {\"name\": \"memory leak\",   \"type\": \"issue\",  \"supporting_text\": \"memory leaks cause crashes\"},\n    {\"name\": \"system crash\",  \"type\": \"issue\",  \"supporting_text\": \"system crashes due to memory leaks\"},\n]\nrelations = [\n    {\"source\": \"memory leak\", \"target\": \"system crash\", \"type\": \"causes\",\n     \"confidence\": 1.0, \"supporting_text\": \"System crashes due to memory leaks.\"},\n]\n\nresult = skill.ingest_with_content(\n    doc_id=\"doc_001\",\n    title=\"System Crash Analysis\",\n    source=\"\u002Fdocs\u002Fincident_report.pdf\",\n    raw_content=\"System crashes due to memory leaks. Memory leaks occur when objects are not released.\",\n    entities=entities,\n    relations=relations,\n)\n# result = {\"doc_id\": \"doc_001\", \"chunk_count\": 1, \"nodes_added\": 2, \"edges_added\": 1}\n\n# 3. Write a wiki summary page for this document\nwiki_store.write_page(\n    category=\"summary\",\n    title=\"System Crash Analysis Summary\",\n    content=\"\"\"---\ntitle: System Crash Analysis\nsource_document: doc_001\ntags: [summary, incident]\n---\n\n# System Crash Analysis\n\n**Source:** incident_report.pdf\n\n## Key Claims\n\n- [[memory-leak]] causes [[system-crash]] (confidence: 1.0)\n\n## Entities\n\n- [[memory-leak]] (issue)\n- [[system-crash]] (issue)\n\"\"\",\n    summary=\"Incident report: memory leaks cause system crashes.\",\n)\n\n# ===== QUERY WITH EVIDENCE =====\nresult = skill.query_with_evidence(\"Why does the system crash?\")\n# Returns: {\"query\": ..., \"subgraph\": ..., \"supporting_documents\": [...], \"evidence_chain\": ...}\n\n# ===== WIKI SEARCH (read wiki before answering) =====\npages = wiki_store.search_wiki(\"memory leak\")\n# Returns: [{slug, category, path, snippet}, ...]\n```\n\n---\n\n## Operations\n\n### Ingest\n\nWhen a user provides a new document:\n\n1. Read `references\u002Fingestion.md` — entity\u002Frelation extraction rules.\n2. Read `references\u002Fontology.md` — type normalization rules.\n3. Extract entities and relations using your LLM reasoning.\n4. Call `skill.ingest_with_content(...)` — stores raw content + chunks + graph nodes + provenance.\n5. **Write a wiki summary page** using `wiki_store.write_page(category=\"summary\", ...)`.\n6. **Update entity pages** — for each new\u002Fupdated entity, write or update `wiki_store.write_page(category=\"entity\", ...)`.\n7. **Update topic pages** if the document touches an existing synthesis topic.\n8. A single document ingest will typically touch 3–10 wiki pages.\n\n### Query\n\nWhen a user asks a question:\n\n1. **Check the wiki first** — `wiki_store.search_wiki(query)` to find relevant pages. Read them.\n2. If the wiki has a good answer, synthesize from wiki pages (fast path).\n3. If deeper graph traversal is needed, call `skill.query_with_evidence(query)`.\n4. Return the answer with evidence citations from `supporting_documents`.\n5. If the answer is valuable, file it back as a new wiki topic page.\n\n### Lint\n\nPeriodically health-check the wiki:\n\n```python\nfrom scripts.tools import wiki_store\nissues = wiki_store.lint_wiki()\n# Returns: {orphan_pages, missing_pages, broken_wikilinks, isolated_pages}\n```\n\nAsk the LLM to review and fix: broken links, orphan pages, stale claims, missing cross-references. See `references\u002Flint.md` for full lint workflow.\n\n---\n\n## Ingestion Constraints\n\n- ❌ Do NOT hallucinate entities not present in the text\n- ❌ Do NOT add relations without explicit textual evidence\n- ❌ Do NOT add edges with confidence \u003C 0.6\n- ✅ Provide `supporting_text` for every entity and relation — this enables provenance\n- ✅ Write a wiki summary page for every ingested document\n- ✅ Update existing entity pages when new information arrives\n- ✅ Flag contradictions in wiki pages when new data conflicts with old claims\n\n---\n\n## Retrieval Constraints\n\n- ? Traversal depth MUST NOT exceed 2 (config: MAX_GRAPH_DEPTH)\n- ? Only edges with confidence ≥ 0.6 (config: MIN_CONFIDENCE)\n- ? Maximum 50 nodes returned (config: MAX_NODES)\n- ❌ Do NOT fabricate nodes or edges not in the graph\n\n---\n\n## Full Python API Reference\n\n| Method | Purpose | When to Use |\n|--------|---------|-------------|\n| `skill.ingest_with_content(doc_id, title, source, raw_content, entities, relations)` | Full RAG ingest: raw docs + graph + provenance | Every new document |\n| `skill.add_node(name, node_type)` | Add single entity (no provenance) | Quick additions without a source doc |\n| `skill.add_edge(source_name, target_name, relation, confidence)` | Add single relation | Quick additions without a source doc |\n| `skill.query(query)` | Graph-only retrieval → subgraph | Structural queries |\n| `skill.query_with_evidence(query)` | Graph + provenance → subgraph + source chunks | Queries requiring citations |\n| `wiki_store.write_page(category, title, content, summary)` | Write\u002Fupdate a wiki page | After every ingest; after answering queries |\n| `wiki_store.read_page(category, title)` | Read a wiki page | Before answering; for cross-referencing |\n| `wiki_store.search_wiki(query)` | Keyword search across wiki | Fast path before graph traversal |\n| `wiki_store.list_pages(category)` | List all wiki pages | Getting an overview |\n| `wiki_store.get_log(last_n)` | Read recent operations | Understanding wiki history |\n| `wiki_store.lint_wiki()` | Health check | Periodic maintenance |\n| `documents_store.list_documents()` | List all ingested raw sources | Audit \u002F provenance checking |\n| `documents_store.search_chunks(query)` | Chunk-level search | Finding specific evidence |\n\n---\n\n## Design Philosophy\n\n> \"The wiki is a persistent, compounding artifact. The cross-references are already there. The synthesis already reflects everything you've read.\" — Karpathy\n\n| Layer | What Happens | Who Owns It |\n|-------|-----------|-------------|\n| **LLM Reasoning** | Extraction, synthesis, writing wiki pages | Agent (.md guidance files) |\n| **Wiki Persistence** | Index, log, file I\u002FO | `wiki_store.py` |\n| **Graph Persistence** | Dedup, index, BFS traverse | `graph_store.py`, `retrieval_engine.py` |\n| **Raw Source Storage** | Immutable docs + chunks + provenance | `documents_store.py` |\n\nThe human curates sources and asks questions. The LLM writes the wiki, extracts the graph, and answers with citations. Python handles all bookkeeping.\n\n","# Mini Context Graph Skill\n\n## The Core Idea\n\nStandard RAG re-discovers knowledge from scratch on every query. This skill is different:\n\n1. **Wiki layer** — The LLM writes and maintains persistent markdown pages (summaries, entity pages, topic syntheses). Cross-references are already there. The wiki gets richer with every ingest.\n2. **Graph layer** — Entities and relations are extracted once and stored as a navigable knowledge graph. BFS traversal answers structural queries without re-reading sources.\n3. **Raw source layer** — Original documents are stored immutably with chunks. Provenance links tie every graph node and edge back to the exact text that supports it.\n\n> The LLM writes; the Python tools handle all bookkeeping.\n\n---\n\n## Three Layers\n\n| Layer | Where | What the LLM does | What Python does |\n|-------|-------|-------------------|-----------------|\n| **Raw Sources** | `data\u002Fdocuments.json` | Reads (never modifies) | Stores chunks + metadata |\n| **Wiki** | `wiki\u002F` (markdown) | Writes\u002Fupdates pages | Manages index.md + log.md |\n| **Graph** | `data\u002Fgraph.json` | Extracts entities + relations | Persists, deduplicates, traverses |\n\n---\n\n## ⚡ Quick Start for Agents\n\nA complete runnable version of this workflow is in `scripts\u002Ftemplate_agent_workflow.py` — copy and adapt it.\n\n```python\nfrom scripts.contextgraph import ContextGraphSkill\nfrom scripts.tools import wiki_store\n\nskill = ContextGraphSkill()\n\n# ===== INGEST WITH FULL RAG + WIKI =====\n# 1. Read references\u002Fingestion.md and references\u002Fontology.md first\n# 2. Extract entities and relations (LLM reasoning step)\nentities = [\n    {\"name\": \"memory leak\",   \"type\": \"issue\",  \"supporting_text\": \"memory leaks cause crashes\"},\n    {\"name\": \"system crash\",  \"type\": \"issue\",  \"supporting_text\": \"system crashes due to memory leaks\"},\n]\nrelations = [\n    {\"source\": \"memory leak\", \"target\": \"system crash\", \"type\": \"causes\",\n     \"confidence\": 1.0, \"supporting_text\": \"System crashes due to memory leaks.\"},\n]\n\nresult = skill.ingest_with_content(\n    doc_id=\"doc_001\",\n    title=\"System Crash Analysis\",\n    source=\"\u002Fdocs\u002Fincident_report.pdf\",\n    raw_content=\"System crashes due to memory leaks. Memory leaks occur when objects are not released.\",\n    entities=entities,\n    relations=relations,\n)\n# result = {\"doc_id\": \"doc_001\", \"chunk_count\": 1, \"nodes_added\": 2, \"edges_added\": 1}\n\n# 3. Write a wiki summary page for this document\nwiki_store.write_page(\n    category=\"summary\",\n    title=\"System Crash Analysis Summary\",\n    content=\"\"\"---\ntitle: System Crash Analysis\nsource_document: doc_001\ntags: [summary, incident]\n---\n\n# System Crash Analysis\n\n**Source:** incident_report.pdf\n\n## Key Claims\n\n- [[memory-leak]] causes [[system-crash]] (confidence: 1.0)\n\n## Entities\n\n- [[memory-leak]] (issue)\n- [[system-crash]] (issue)\n\"\"\",\n    summary=\"Incident report: memory leaks cause system crashes.\",\n)\n\n# ===== QUERY WITH EVIDENCE =====\nresult = skill.query_with_evidence(\"Why does the system crash?\")\n# Returns: {\"query\": ..., \"subgraph\": ..., \"supporting_documents\": [...], \"evidence_chain\": ...}\n\n# ===== WIKI SEARCH (read wiki before answering) =====\npages = wiki_store.search_wiki(\"memory leak\")\n# Returns: [{slug, category, path, snippet}, ...]\n```\n\n---\n\n## Operations\n\n### Ingest\n\nWhen a user provides a new document:\n\n1. Read `references\u002Fingestion.md` — entity\u002Frelation extraction rules.\n2. Read `references\u002Fontology.md` — type normalization rules.\n3. Extract entities and relations using your LLM reasoning.\n4. Call `skill.ingest_with_content(...)` — stores raw content + chunks + graph nodes + provenance.\n5. **Write a wiki summary page** using `wiki_store.write_page(category=\"summary\", ...)`.\n6. **Update entity pages** — for each new\u002Fupdated entity, write or update `wiki_store.write_page(category=\"entity\", ...)`.\n7. **Update topic pages** if the document touches an existing synthesis topic.\n8. A single document ingest will typically touch 3–10 wiki pages.\n\n### Query\n\nWhen a user asks a question:\n\n1. **Check the wiki first** — `wiki_store.search_wiki(query)` to find relevant pages. Read them.\n2. If the wiki has a good answer, synthesize from wiki pages (fast path).\n3. If deeper graph traversal is needed, call `skill.query_with_evidence(query)`.\n4. Return the answer with evidence citations from `supporting_documents`.\n5. If the answer is valuable, file it back as a new wiki topic page.\n\n### Lint\n\nPeriodically health-check the wiki:\n\n```python\nfrom scripts.tools import wiki_store\nissues = wiki_store.lint_wiki()\n# Returns: {orphan_pages, missing_pages, broken_wikilinks, isolated_pages}\n```\n\nAsk the LLM to review and fix: broken links, orphan pages, stale claims, missing cross-references. See `references\u002Flint.md` for full lint workflow.\n\n---\n\n## Ingestion Constraints\n\n- ❌ Do NOT hallucinate entities not present in the text\n- ❌ Do NOT add relations without explicit textual evidence\n- ❌ Do NOT add edges with confidence \u003C 0.6\n- ✅ Provide `supporting_text` for every entity and relation — this enables provenance\n- ✅ Write a wiki summary page for every ingested document\n- ✅ Update existing entity pages when new information arrives\n- ✅ Flag contradictions in wiki pages when new data conflicts with old claims\n\n---\n\n## Retrieval Constraints\n\n- ? Traversal depth MUST NOT exceed 2 (config: MAX_GRAPH_DEPTH)\n- ? Only edges with confidence ≥ 0.6 (config: MIN_CONFIDENCE)\n- ? Maximum 50 nodes returned (config: MAX_NODES)\n- ❌ Do NOT fabricate nodes or edges not in the graph\n\n---\n\n## Full Python API Reference\n\n| Method | Purpose | When to Use |\n|--------|---------|-------------|\n| `skill.ingest_with_content(doc_id, title, source, raw_content, entities, relations)` | Full RAG ingest: raw docs + graph + provenance | Every new document |\n| `skill.add_node(name, node_type)` | Add single entity (no provenance) | Quick additions without a source doc |\n| `skill.add_edge(source_name, target_name, relation, confidence)` | Add single relation | Quick additions without a source doc |\n| `skill.query(query)` | Graph-only retrieval → subgraph | Structural queries |\n| `skill.query_with_evidence(query)` | Graph + provenance → subgraph + source chunks | Queries requiring citations |\n| `wiki_store.write_page(category, title, content, summary)` | Write\u002Fupdate a wiki page | After every ingest; after answering queries |\n| `wiki_store.read_page(category, title)` | Read a wiki page | Before answering; for cross-referencing |\n| `wiki_store.search_wiki(query)` | Keyword search across wiki | Fast path before graph traversal |\n| `wiki_store.list_pages(category)` | List all wiki pages | Getting an overview |\n| `wiki_store.get_log(last_n)` | Read recent operations | Understanding wiki history |\n| `wiki_store.lint_wiki()` | Health check | Periodic maintenance |\n| `documents_store.list_documents()` | List all ingested raw sources | Audit \u002F provenance checking |\n| `documents_store.search_chunks(query)` | Chunk-level search | Finding specific evidence |\n\n---\n\n## Design Philosophy\n\n> \"The wiki is a persistent, compounding artifact. The cross-references are already there. The synthesis already reflects everything you've read.\" — Karpathy\n\n| Layer | What Happens | Who Owns It |\n|-------|-----------|-------------|\n| **LLM Reasoning** | Extraction, synthesis, writing wiki pages | Agent (.md guidance files) |\n| **Wiki Persistence** | Index, log, file I\u002FO | `wiki_store.py` |\n| **Graph Persistence** | Dedup, index, BFS traverse | `graph_store.py`, `retrieval_engine.py` |\n| **Raw Source Storage** | Immutable docs + chunks + provenance | `documents_store.py` |\n\nThe human curates sources and asks questions. The LLM writes the wiki, extracts the graph, and answers with citations. Python handles all bookkeeping.\n\n",{"name":5,"description":7},"main","5269f7ea2d6c3a9bfda5ed878b6726cf509121fd","skills\u002Fmini-context-graph",5360,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot","https:\u002F\u002Fawesome-copilot.github.com\u002F","https:\u002F\u002Fgithub.com\u002Fgithub","https:\u002F\u002Fgithub.com\u002Fabout","How people build software.","2026-11-15 04:42:30",{"source":8,"skillName":5,"commands":61,"prompt":71,"zipAvailable":72},[62,65,68],{"tool":63,"command":64},"npx","npx skills add github\u002Fawesome-copilot --skill mini-context-graph",{"tool":66,"command":67},"bunx","bunx skills add github\u002Fawesome-copilot --skill mini-context-graph",{"tool":69,"command":70},"pnpm","pnpm dlx skills add github\u002Fawesome-copilot --skill mini-context-graph","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002FSKILL.md",true,[74,80,84,88,92,98,102,106,110,114,118,122,126,130],{"path":75,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":78,"sourceHtmlUrl":79},"references\u002Fingestion.md","REFERENCE","参考文档",5161,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Freferences\u002Fingestion.md",{"path":81,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Flint.md",4235,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Freferences\u002Flint.md",{"path":85,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Fontology.md",3238,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Freferences\u002Fontology.md",{"path":89,"resourceType":76,"resourceTypeDesc":77,"sizeBytes":90,"sourceHtmlUrl":91},"references\u002Fretrieval.md",4399,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Freferences\u002Fretrieval.md",{"path":93,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":96,"sourceHtmlUrl":97},"scripts\u002Fconfig.py","SCRIPT","脚本",893,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Fscripts\u002Fconfig.py",{"path":99,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":100,"sourceHtmlUrl":101},"scripts\u002Fcontextgraph.py",11794,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Fscripts\u002Fcontextgraph.py",{"path":103,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":104,"sourceHtmlUrl":105},"scripts\u002Ftemplate_agent_workflow.py",6708,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Fscripts\u002Ftemplate_agent_workflow.py",{"path":107,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":108,"sourceHtmlUrl":109},"scripts\u002Ftools\u002F__init__.py",0,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Fscripts\u002Ftools\u002F__init__.py",{"path":111,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":112,"sourceHtmlUrl":113},"scripts\u002Ftools\u002Fdocuments_store.py",5849,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Fscripts\u002Ftools\u002Fdocuments_store.py",{"path":115,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":116,"sourceHtmlUrl":117},"scripts\u002Ftools\u002Fgraph_store.py",6163,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Fscripts\u002Ftools\u002Fgraph_store.py",{"path":119,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":120,"sourceHtmlUrl":121},"scripts\u002Ftools\u002Findex_store.py",2821,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Fscripts\u002Ftools\u002Findex_store.py",{"path":123,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":124,"sourceHtmlUrl":125},"scripts\u002Ftools\u002Fontology_store.py",4953,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Fscripts\u002Ftools\u002Fontology_store.py",{"path":127,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":128,"sourceHtmlUrl":129},"scripts\u002Ftools\u002Fretrieval_engine.py",1601,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Fscripts\u002Ftools\u002Fretrieval_engine.py",{"path":131,"resourceType":94,"resourceTypeDesc":95,"sizeBytes":132,"sourceHtmlUrl":133},"scripts\u002Ftools\u002Fwiki_store.py",9635,"https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fmini-context-graph\u002Fscripts\u002Ftools\u002Fwiki_store.py",[135,166,193,223,257,274],{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":145,"publishedAt":45},"01M70KXZNX9ES38N2A8M58ZZMZ","supabase-rls-policies","Write, review, and test Supabase Row Level Security (RLS) policies so that one user cannot read or change another user's rows. Use when creating or changing tables, policies, views, or security definer functions in a Supabase project, when reviewing a migration that touches RLS, or when asked to prove user isolation with pgTAP tests that sign in as two users.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fsupabase-rls-policies\u002FSKILL.md",[141,144],{"categoryNo":142,"categoryName":143,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165],"supabase","rls","supabase rls","supabase rls policies","project","when","review","change","read","write","security","isolation","tables","as","tests","prove","use","users","functions","with",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":170,"categories":171,"keywords":173,"publishedAt":45},"01M70KWA5YWXDV6V8DZ19E3T2Y","skill-distribution-audit","Audit a skill collection before release or investigate installer omissions, archived\u002Ftemplate leakage, duplicate identities, and manifest drift. Reconcile the intended public source set, distribution manifest, and actual discovery output for a specified consumer version. Includes a deterministic set-comparison helper.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fskill-distribution-audit\u002FSKILL.md",[172],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[174,175,176,177,178,179,30,180,181,182,183,184,185,186,187,188,189,190,191,192],"distribution","skill distribution audit","audit","SET","deterministic","installer","discovery","comparison","helper","manifest","version","collection","drift","output","investigate","duplicate","template","release","reconcile",{"skillNo":194,"name":195,"nameZh":6,"description":196,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":197,"sourceHtmlUrl":198,"categories":199,"keywords":205,"publishedAt":45},"01M70KCJPFVW7PPQ49CC5R8JGK","logicapps-custom-code-net10-migration","Migrate Azure Logic Apps Standard local-function custom-code projects from .NET 8 to .NET 10. Use for legacy Logic Apps custom-code workspace upgrades, project\u002Fpackage configuration, custom-worker settings, publication, and integrated VS Code debugging. Not for ordinary .NET applications, standalone Azure Functions, Consumption workflows, or cloud deployment.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Flogicapps-custom-code-net10-migration\u002FSKILL.md",[200],{"categoryNo":201,"categoryName":202,"taxonomyType":203,"taxonomyTypeDesc":204},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[206,207,208,209,210,211,30,150,212,213,214,215,216,217,218,162,219,220,221,222],"custom","code","logicapps custom code net10 migration","migration","projects","standalone","deployment","applications","local","azure","worker","upgrades","package","apps","Legacy","debugging","workflows",{"skillNo":224,"name":225,"nameZh":6,"description":226,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":227,"sourceHtmlUrl":228,"categories":229,"keywords":234,"publishedAt":45},"01M6AEBN37RQMXJ3298PEY7PG1","winmd-api-search","Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI\u002FML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fwinmd-api-search\u002FSKILL.md",[230,233],{"categoryNo":231,"categoryName":232,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":201,"categoryName":202,"taxonomyType":203,"taxonomyTypeDesc":204},[235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,151,250,251,252,253,254,30,255,256],"winmd api search","search","api","windows","desktop","file access","enumeration","ui","ml","file","networking","capabilities","notifications","explore","access","features","full","camera","events","platform","methods","controls",{"skillNo":258,"name":259,"nameZh":6,"description":260,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":261,"licenseFileUrl":262,"sourceHtmlUrl":263,"categories":264,"keywords":270,"publishedAt":45},"01M6AE571R3SC1ZGE202B3NB5S","resemble-detect","Deepfake detection and media safety — detect AI-generated audio, images, video, and text, trace synthesis sources, apply watermarks, verify speaker identity, and analyze media intelligence using Resemble AI","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fresemble-detect\u002FSKILL.md",[265,266,269],{"categoryNo":231,"categoryName":232,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":267,"categoryName":268,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[271,177,244,272,273],"resemble detect","https","identity",{"skillNo":275,"name":276,"nameZh":6,"description":277,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":261,"licenseFileUrl":278,"sourceHtmlUrl":279,"categories":280,"keywords":285,"publishedAt":45},"01M6AE30BSK2HCY4TQQ22Z8JRD","quality-playbook","Run a complete quality engineering audit on any codebase. Derives behavioral requirements from the code, generates spec-traced functional tests, runs a three-pass code review with regression tests, executes a multi-model spec audit (Council of Three), and produces a consolidated bug report with TDD-verified patches. Finds the 35% of real defects that structural code review alone cannot catch. Works with any language. Trigger on 'quality playbook', 'spec audit', 'Council of Three', 'fitness-to-purpose', or 'coverage theater'.","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fgithub\u002Fawesome-copilot\u002Fblob\u002Fmain\u002Fskills\u002Fquality-playbook\u002FSKILL.md",[281,284],{"categoryNo":282,"categoryName":283,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000007","代码审查",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[286,287,288,289,207,152,9,176,290,291,272,292,293,294,295,296,297,298,299,300,301,302,160,303,304,305,306],"playbook","quality playbook","quality","code review","tdd","run","report","spec","bug report","council","engineering","functional","spec audit","bug","verified","multi-model","three","coverage","model","regression","structural"]