[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CD3Q1SNF9PP99JC6D44J1P":3,"skill-same:01M7CD3Q1SNF9PP99JC6D44J1P":61},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":14,"repositoryHtmlUrl":33,"repositoryHomepageUrl":6,"ownerHtmlUrl":34,"ownerBlogUrl":35,"ownerDescription":36,"updatedAt":37,"install":38,"resources":51},"01M7CD3Q1SNF9PP99JC6D44J1P","rag-query",null,"Consulta ao corpus RAG do projeto. Busca conhecimento em decisoes,\ndocumentacao, learnings e padroes armazenados.\nUse quando: buscar decisoes passadas, encontrar documentacao, recuperar padroes.","shenyimings\u002Fskillet","shenyimings","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F89332218?v=4","01M7C9KHMXPPXMQJCKN78FKB06","Yiming Shen","01M7C9KHP4KS809T2A9T64YDWE",0,"MIT","https:\u002F\u002Fgithub.com\u002Fshenyimings\u002Fskillet\u002Fblob\u002Fmain\u002Fbenchmark\u002Fwild\u002Fheldout\u002Fsuspicious\u002Farbgjr__sdlc_agentico__.claude__skills__rag-query\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: rag-query\ndescription: |\n  Consulta ao corpus RAG do projeto. Busca conhecimento em decisoes,\n  documentacao, learnings e padroes armazenados.\n  Use quando: buscar decisoes passadas, encontrar documentacao, recuperar padroes.\nallowed-tools:\n  - Read\n  - Glob\n  - Grep\n  - Bash\nuser-invocable: false\n---\n\n# RAG Query Skill\n\n## Proposito\n\nEsta skill permite consultar o corpus de conhecimento do projeto para:\n\n1. **Buscar decisoes** - Encontrar ADRs e decisoes tecnicas relevantes\n2. **Encontrar documentacao** - Localizar docs oficiais e referencias\n3. **Recuperar padroes** - Identificar padroes ja usados no projeto\n4. **Acessar learnings** - Consultar licoes aprendidas\n\n## Tipos de Consulta\n\n### 1. Busca por Decisoes\n\n```python\nresults = query_decisions(\n    keywords=[\"database\", \"postgresql\"],\n    phase=3,\n    type=\"architectural\",\n    limit=5\n)\n```\n\n### 2. Busca por Documentacao\n\n```python\nresults = query_docs(\n    topic=\"authentication\",\n    source=[\"official\", \"internal\"],\n    limit=10\n)\n```\n\n### 3. Busca por Padroes\n\n```python\nresults = query_patterns(\n    category=\"resilience\",\n    tags=[\"circuit-breaker\", \"retry\"]\n)\n```\n\n### 4. Busca por Learnings\n\n```python\nresults = query_learnings(\n    type=\"incident\",\n    keywords=[\"timeout\", \"performance\"]\n)\n```\n\n### 5. Busca Geral\n\n```python\nresults = query(\n    question=\"Como lidar com falhas de conexao ao banco?\",\n    sources=[\"decisions\", \"learnings\", \"patterns\"],\n    limit=5\n)\n```\n\n## Estrutura do Corpus\n\nO corpus RAG está consolidado no diretório de projeto (via `path_resolver.py`):\n\n```bash\n# Resolve project directory dynamically\nPROJECT_DIR=$(python3 .claude\u002Flib\u002Fpython\u002Fpath_resolver.py --project-dir)\n\n# Corpus structure:\n${PROJECT_DIR}\u002Fcorpus\u002F\n├── nodes\u002F\n│   ├── decisions\u002F       # ADRs e decisoes indexadas\n│   │   └── *.yml\n│   ├── learnings\u002F       # Licoes aprendidas\n│   │   └── *.yml\n│   ├── patterns\u002F        # Padroes conhecidos\n│   │   └── *.yml\n│   └── concepts\u002F        # Conceitos de dominio\n│       └── *.yml\n├── graph.json           # Grafo semantico\n└── index.yml            # Indice textual\n\n# Project-specific decisions (not yet indexed):\n${PROJECT_DIR}\u002Fprojects\u002F{project-id}\u002F\n├── decisions\u002F           # ADRs do projeto atual\n├── specs\u002F               # Especificacoes\n└── manifest.yml         # Estado do projeto\n```\n\n**Path Resolution**:\n- Paths are resolved from `settings.json` (default: `.project`)\n- Compatible with \"Natural Language First\" policy\n- Never hardcodes `.agentic_sdlc\u002F` (framework files only)\n\n**External References**:\n- `${PROJECT_DIR}\u002Freferences\u002F` - Documentos externos via reference-indexer\n\n## Formato de Resultado\n\n```yaml\nquery_result:\n  query: string\n  sources_searched: list[string]\n  total_results: number\n\n  results:\n    - id: string\n      type: [decision | doc | pattern | learning]\n      title: string\n      relevance_score: float (0-1)\n      snippet: string\n      source_path: string\n      metadata:\n        created_at: datetime\n        tags: list[string]\n        phase: number\n\n  suggestions:\n    - string\n```\n\n## Integracoes\n\n### Com rag-curator\n\nO rag-curator adiciona e organiza o corpus que o rag-query consulta:\n- Indexa ADRs de `${PROJECT_DIR}\u002Fprojects\u002F{id}\u002Fdecisions\u002F` para `${PROJECT_DIR}\u002Fcorpus\u002Fnodes\u002Fdecisions\u002F`\n- Indexa learnings para `${PROJECT_DIR}\u002Fcorpus\u002Fnodes\u002Flearnings\u002F`\n- Mantém grafo semântico em `${PROJECT_DIR}\u002Fcorpus\u002Fgraph.json`\n\n### Com path_resolver\n\nUsa `path_resolver.py` para resolver paths dinamicamente:\n```bash\nPROJECT_DIR=$(python3 .claude\u002Flib\u002Fpython\u002Fpath_resolver.py --project-dir)\n```\n\n## Scripts\n\n### query_corpus.py\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"\nConsulta o corpus de conhecimento do projeto.\nSuporta paths consolidados (.project\u002Fcorpus\u002F) e legados (.claude\u002F).\n\"\"\"\nimport yaml\nfrom pathlib import Path\nfrom typing import List, Dict, Any, Optional\nimport re\n\n# Paths consolidados (preferencia)\nCORPUS_DIR = Path(\".project\u002Fcorpus\")\nPROJECTS_DIR = Path(\".agentic_sdlc\u002Fprojects\")\nREFERENCES_DIR = Path(\".project\u002Freferences\")\n\n# Paths legados (fallback)\nKNOWLEDGE_DIR = Path(\".claude\u002Fknowledge\")\nMEMORY_DIR = Path(\".claude\u002Fmemory\")\n\ndef load_all_decisions() -> List[Dict]:\n    \"\"\"Carrega todas as decisoes de paths consolidados e legados.\"\"\"\n    decisions = []\n\n    # Paths consolidados (preferencia)\n    corpus_decisions = CORPUS_DIR \u002F \"decisions\"\n    if corpus_decisions.exists():\n        for f in corpus_decisions.glob(\"adr-*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"decision\" in data:\n                decisions.append({\n                    **data[\"decision\"],\n                    \"source_path\": str(f),\n                    \"type\": \"decision\"\n                })\n\n    # Paths por projeto\n    for project_dir in PROJECTS_DIR.glob(\"*\u002Fdecisions\"):\n        for f in project_dir.glob(\"adr-*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"decision\" in data:\n                decisions.append({\n                    **data[\"decision\"],\n                    \"source_path\": str(f),\n                    \"type\": \"decision\"\n                })\n\n    # Paths legados (fallback)\n    legacy_decisions = MEMORY_DIR \u002F \"decisions\"\n    if legacy_decisions.exists():\n        for f in legacy_decisions.glob(\"adr-*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"decision\" in data:\n                decisions.append({\n                    **data[\"decision\"],\n                    \"source_path\": str(f),\n                    \"type\": \"decision\"\n                })\n\n    return decisions\n\ndef load_all_learnings() -> List[Dict]:\n    \"\"\"Carrega todos os learnings de paths consolidados e legados.\"\"\"\n    learnings = []\n\n    # Paths consolidados (preferencia)\n    corpus_learnings = CORPUS_DIR \u002F \"learnings\"\n    if corpus_learnings.exists():\n        for f in corpus_learnings.glob(\"*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"learning\" in data:\n                learnings.append({\n                    **data[\"learning\"],\n                    \"source_path\": str(f),\n                    \"type\": \"learning\"\n                })\n\n    # Paths por projeto\n    for project_dir in PROJECTS_DIR.glob(\"*\u002Flearnings\"):\n        for f in project_dir.glob(\"*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"learning\" in data:\n                learnings.append({\n                    **data[\"learning\"],\n                    \"source_path\": str(f),\n                    \"type\": \"learning\"\n                })\n\n    # Paths legados (fallback)\n    legacy_learnings = MEMORY_DIR \u002F \"learnings\"\n    if legacy_learnings.exists():\n        for f in legacy_learnings.glob(\"*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"learning\" in data:\n                learnings.append({\n                    **data[\"learning\"],\n                    \"source_path\": str(f),\n                    \"type\": \"learning\"\n                })\n\n    return learnings\n\ndef calculate_relevance(item: Dict, keywords: List[str]) -> float:\n    \"\"\"Calcula score de relevancia baseado em keywords.\"\"\"\n    text = \" \".join([\n        str(item.get(\"title\", \"\")),\n        str(item.get(\"context\", \"\")),\n        str(item.get(\"decision\", \"\")),\n        str(item.get(\"insight\", \"\")),\n        \" \".join(item.get(\"tags\", []))\n    ]).lower()\n\n    if not keywords:\n        return 0.5\n\n    matches = sum(1 for kw in keywords if kw.lower() in text)\n    return min(matches \u002F len(keywords), 1.0)\n\ndef search(\n    keywords: List[str] = None,\n    sources: List[str] = None,\n    type_filter: str = None,\n    phase_filter: int = None,\n    limit: int = 10\n) -> Dict[str, Any]:\n    \"\"\"Busca geral no corpus.\"\"\"\n    keywords = keywords or []\n    sources = sources or [\"decisions\", \"learnings\"]\n\n    all_items = []\n\n    if \"decisions\" in sources:\n        all_items.extend(load_all_decisions())\n\n    if \"learnings\" in sources:\n        all_items.extend(load_all_learnings())\n\n    # Filtrar por tipo\n    if type_filter:\n        all_items = [i for i in all_items if i.get(\"type\") == type_filter]\n\n    # Filtrar por fase\n    if phase_filter is not None:\n        all_items = [i for i in all_items if i.get(\"phase\") == phase_filter]\n\n    # Calcular relevancia\n    for item in all_items:\n        item[\"relevance_score\"] = calculate_relevance(item, keywords)\n\n    # Ordenar por relevancia\n    all_items.sort(key=lambda x: x[\"relevance_score\"], reverse=True)\n\n    # Limitar resultados\n    results = all_items[:limit]\n\n    return {\n        \"query\": \" \".join(keywords),\n        \"sources_searched\": sources,\n        \"total_results\": len(results),\n        \"results\": [\n            {\n                \"id\": r.get(\"id\"),\n                \"type\": r.get(\"type\"),\n                \"title\": r.get(\"title\"),\n                \"relevance_score\": r.get(\"relevance_score\"),\n                \"snippet\": r.get(\"context\", r.get(\"insight\", \"\"))[:200],\n                \"source_path\": r.get(\"source_path\"),\n                \"metadata\": {\n                    \"created_at\": r.get(\"created_at\"),\n                    \"tags\": r.get(\"tags\", []),\n                    \"phase\": r.get(\"phase\")\n                }\n            }\n            for r in results\n        ]\n    }\n\ndef query_decisions(\n    keywords: List[str] = None,\n    phase: int = None,\n    decision_type: str = None,\n    limit: int = 5\n) -> Dict:\n    \"\"\"Busca especifica em decisoes.\"\"\"\n    decisions = load_all_decisions()\n\n    if phase is not None:\n        decisions = [d for d in decisions if d.get(\"phase\") == phase]\n\n    if decision_type:\n        decisions = [d for d in decisions if d.get(\"type\") == decision_type]\n\n    for d in decisions:\n        d[\"relevance_score\"] = calculate_relevance(d, keywords or [])\n\n    decisions.sort(key=lambda x: x[\"relevance_score\"], reverse=True)\n\n    return {\n        \"query_type\": \"decisions\",\n        \"filters\": {\"phase\": phase, \"type\": decision_type},\n        \"results\": decisions[:limit]\n    }\n\nif __name__ == \"__main__\":\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--keywords\", nargs=\"+\", default=[])\n    parser.add_argument(\"--sources\", nargs=\"+\", default=[\"decisions\", \"learnings\"])\n    parser.add_argument(\"--limit\", type=int, default=10)\n    args = parser.parse_args()\n\n    result = search(\n        keywords=args.keywords,\n        sources=args.sources,\n        limit=args.limit\n    )\n    print(yaml.dump(result, default_flow_style=False))\n```\n\n## Uso no Fluxo SDLC\n\n1. **Antes de decidir** - Buscar decisoes similares\n2. **Ao pesquisar** - Consultar documentacao oficial\n3. **Ao resolver problemas** - Verificar learnings relevantes\n4. **Ao implementar** - Buscar padroes ja usados\n\n## Pontos de Pesquisa\n\nPara melhorar esta skill:\n- \"RAG retrieval augmented generation best practices\"\n- \"semantic search for code documentation\"\n- \"knowledge graph software engineering\"\n","# RAG Query Skill\n\n## Proposito\n\nEsta skill permite consultar o corpus de conhecimento do projeto para:\n\n1. **Buscar decisoes** - Encontrar ADRs e decisoes tecnicas relevantes\n2. **Encontrar documentacao** - Localizar docs oficiais e referencias\n3. **Recuperar padroes** - Identificar padroes ja usados no projeto\n4. **Acessar learnings** - Consultar licoes aprendidas\n\n## Tipos de Consulta\n\n### 1. Busca por Decisoes\n\n```python\nresults = query_decisions(\n    keywords=[\"database\", \"postgresql\"],\n    phase=3,\n    type=\"architectural\",\n    limit=5\n)\n```\n\n### 2. Busca por Documentacao\n\n```python\nresults = query_docs(\n    topic=\"authentication\",\n    source=[\"official\", \"internal\"],\n    limit=10\n)\n```\n\n### 3. Busca por Padroes\n\n```python\nresults = query_patterns(\n    category=\"resilience\",\n    tags=[\"circuit-breaker\", \"retry\"]\n)\n```\n\n### 4. Busca por Learnings\n\n```python\nresults = query_learnings(\n    type=\"incident\",\n    keywords=[\"timeout\", \"performance\"]\n)\n```\n\n### 5. Busca Geral\n\n```python\nresults = query(\n    question=\"Como lidar com falhas de conexao ao banco?\",\n    sources=[\"decisions\", \"learnings\", \"patterns\"],\n    limit=5\n)\n```\n\n## Estrutura do Corpus\n\nO corpus RAG está consolidado no diretório de projeto (via `path_resolver.py`):\n\n```bash\n# Resolve project directory dynamically\nPROJECT_DIR=$(python3 .claude\u002Flib\u002Fpython\u002Fpath_resolver.py --project-dir)\n\n# Corpus structure:\n${PROJECT_DIR}\u002Fcorpus\u002F\n├── nodes\u002F\n│   ├── decisions\u002F       # ADRs e decisoes indexadas\n│   │   └── *.yml\n│   ├── learnings\u002F       # Licoes aprendidas\n│   │   └── *.yml\n│   ├── patterns\u002F        # Padroes conhecidos\n│   │   └── *.yml\n│   └── concepts\u002F        # Conceitos de dominio\n│       └── *.yml\n├── graph.json           # Grafo semantico\n└── index.yml            # Indice textual\n\n# Project-specific decisions (not yet indexed):\n${PROJECT_DIR}\u002Fprojects\u002F{project-id}\u002F\n├── decisions\u002F           # ADRs do projeto atual\n├── specs\u002F               # Especificacoes\n└── manifest.yml         # Estado do projeto\n```\n\n**Path Resolution**:\n- Paths are resolved from `settings.json` (default: `.project`)\n- Compatible with \"Natural Language First\" policy\n- Never hardcodes `.agentic_sdlc\u002F` (framework files only)\n\n**External References**:\n- `${PROJECT_DIR}\u002Freferences\u002F` - Documentos externos via reference-indexer\n\n## Formato de Resultado\n\n```yaml\nquery_result:\n  query: string\n  sources_searched: list[string]\n  total_results: number\n\n  results:\n    - id: string\n      type: [decision | doc | pattern | learning]\n      title: string\n      relevance_score: float (0-1)\n      snippet: string\n      source_path: string\n      metadata:\n        created_at: datetime\n        tags: list[string]\n        phase: number\n\n  suggestions:\n    - string\n```\n\n## Integracoes\n\n### Com rag-curator\n\nO rag-curator adiciona e organiza o corpus que o rag-query consulta:\n- Indexa ADRs de `${PROJECT_DIR}\u002Fprojects\u002F{id}\u002Fdecisions\u002F` para `${PROJECT_DIR}\u002Fcorpus\u002Fnodes\u002Fdecisions\u002F`\n- Indexa learnings para `${PROJECT_DIR}\u002Fcorpus\u002Fnodes\u002Flearnings\u002F`\n- Mantém grafo semântico em `${PROJECT_DIR}\u002Fcorpus\u002Fgraph.json`\n\n### Com path_resolver\n\nUsa `path_resolver.py` para resolver paths dinamicamente:\n```bash\nPROJECT_DIR=$(python3 .claude\u002Flib\u002Fpython\u002Fpath_resolver.py --project-dir)\n```\n\n## Scripts\n\n### query_corpus.py\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv python3\n\"\"\"\nConsulta o corpus de conhecimento do projeto.\nSuporta paths consolidados (.project\u002Fcorpus\u002F) e legados (.claude\u002F).\n\"\"\"\nimport yaml\nfrom pathlib import Path\nfrom typing import List, Dict, Any, Optional\nimport re\n\n# Paths consolidados (preferencia)\nCORPUS_DIR = Path(\".project\u002Fcorpus\")\nPROJECTS_DIR = Path(\".agentic_sdlc\u002Fprojects\")\nREFERENCES_DIR = Path(\".project\u002Freferences\")\n\n# Paths legados (fallback)\nKNOWLEDGE_DIR = Path(\".claude\u002Fknowledge\")\nMEMORY_DIR = Path(\".claude\u002Fmemory\")\n\ndef load_all_decisions() -> List[Dict]:\n    \"\"\"Carrega todas as decisoes de paths consolidados e legados.\"\"\"\n    decisions = []\n\n    # Paths consolidados (preferencia)\n    corpus_decisions = CORPUS_DIR \u002F \"decisions\"\n    if corpus_decisions.exists():\n        for f in corpus_decisions.glob(\"adr-*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"decision\" in data:\n                decisions.append({\n                    **data[\"decision\"],\n                    \"source_path\": str(f),\n                    \"type\": \"decision\"\n                })\n\n    # Paths por projeto\n    for project_dir in PROJECTS_DIR.glob(\"*\u002Fdecisions\"):\n        for f in project_dir.glob(\"adr-*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"decision\" in data:\n                decisions.append({\n                    **data[\"decision\"],\n                    \"source_path\": str(f),\n                    \"type\": \"decision\"\n                })\n\n    # Paths legados (fallback)\n    legacy_decisions = MEMORY_DIR \u002F \"decisions\"\n    if legacy_decisions.exists():\n        for f in legacy_decisions.glob(\"adr-*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"decision\" in data:\n                decisions.append({\n                    **data[\"decision\"],\n                    \"source_path\": str(f),\n                    \"type\": \"decision\"\n                })\n\n    return decisions\n\ndef load_all_learnings() -> List[Dict]:\n    \"\"\"Carrega todos os learnings de paths consolidados e legados.\"\"\"\n    learnings = []\n\n    # Paths consolidados (preferencia)\n    corpus_learnings = CORPUS_DIR \u002F \"learnings\"\n    if corpus_learnings.exists():\n        for f in corpus_learnings.glob(\"*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"learning\" in data:\n                learnings.append({\n                    **data[\"learning\"],\n                    \"source_path\": str(f),\n                    \"type\": \"learning\"\n                })\n\n    # Paths por projeto\n    for project_dir in PROJECTS_DIR.glob(\"*\u002Flearnings\"):\n        for f in project_dir.glob(\"*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"learning\" in data:\n                learnings.append({\n                    **data[\"learning\"],\n                    \"source_path\": str(f),\n                    \"type\": \"learning\"\n                })\n\n    # Paths legados (fallback)\n    legacy_learnings = MEMORY_DIR \u002F \"learnings\"\n    if legacy_learnings.exists():\n        for f in legacy_learnings.glob(\"*.yml\"):\n            data = yaml.safe_load(f.read_text())\n            if data and \"learning\" in data:\n                learnings.append({\n                    **data[\"learning\"],\n                    \"source_path\": str(f),\n                    \"type\": \"learning\"\n                })\n\n    return learnings\n\ndef calculate_relevance(item: Dict, keywords: List[str]) -> float:\n    \"\"\"Calcula score de relevancia baseado em keywords.\"\"\"\n    text = \" \".join([\n        str(item.get(\"title\", \"\")),\n        str(item.get(\"context\", \"\")),\n        str(item.get(\"decision\", \"\")),\n        str(item.get(\"insight\", \"\")),\n        \" \".join(item.get(\"tags\", []))\n    ]).lower()\n\n    if not keywords:\n        return 0.5\n\n    matches = sum(1 for kw in keywords if kw.lower() in text)\n    return min(matches \u002F len(keywords), 1.0)\n\ndef search(\n    keywords: List[str] = None,\n    sources: List[str] = None,\n    type_filter: str = None,\n    phase_filter: int = None,\n    limit: int = 10\n) -> Dict[str, Any]:\n    \"\"\"Busca geral no corpus.\"\"\"\n    keywords = keywords or []\n    sources = sources or [\"decisions\", \"learnings\"]\n\n    all_items = []\n\n    if \"decisions\" in sources:\n        all_items.extend(load_all_decisions())\n\n    if \"learnings\" in sources:\n        all_items.extend(load_all_learnings())\n\n    # Filtrar por tipo\n    if type_filter:\n        all_items = [i for i in all_items if i.get(\"type\") == type_filter]\n\n    # Filtrar por fase\n    if phase_filter is not None:\n        all_items = [i for i in all_items if i.get(\"phase\") == phase_filter]\n\n    # Calcular relevancia\n    for item in all_items:\n        item[\"relevance_score\"] = calculate_relevance(item, keywords)\n\n    # Ordenar por relevancia\n    all_items.sort(key=lambda x: x[\"relevance_score\"], reverse=True)\n\n    # Limitar resultados\n    results = all_items[:limit]\n\n    return {\n        \"query\": \" \".join(keywords),\n        \"sources_searched\": sources,\n        \"total_results\": len(results),\n        \"results\": [\n            {\n                \"id\": r.get(\"id\"),\n                \"type\": r.get(\"type\"),\n                \"title\": r.get(\"title\"),\n                \"relevance_score\": r.get(\"relevance_score\"),\n                \"snippet\": r.get(\"context\", r.get(\"insight\", \"\"))[:200],\n                \"source_path\": r.get(\"source_path\"),\n                \"metadata\": {\n                    \"created_at\": r.get(\"created_at\"),\n                    \"tags\": r.get(\"tags\", []),\n                    \"phase\": r.get(\"phase\")\n                }\n            }\n            for r in results\n        ]\n    }\n\ndef query_decisions(\n    keywords: List[str] = None,\n    phase: int = None,\n    decision_type: str = None,\n    limit: int = 5\n) -> Dict:\n    \"\"\"Busca especifica em decisoes.\"\"\"\n    decisions = load_all_decisions()\n\n    if phase is not None:\n        decisions = [d for d in decisions if d.get(\"phase\") == phase]\n\n    if decision_type:\n        decisions = [d for d in decisions if d.get(\"type\") == decision_type]\n\n    for d in decisions:\n        d[\"relevance_score\"] = calculate_relevance(d, keywords or [])\n\n    decisions.sort(key=lambda x: x[\"relevance_score\"], reverse=True)\n\n    return {\n        \"query_type\": \"decisions\",\n        \"filters\": {\"phase\": phase, \"type\": decision_type},\n        \"results\": decisions[:limit]\n    }\n\nif __name__ == \"__main__\":\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--keywords\", nargs=\"+\", default=[])\n    parser.add_argument(\"--sources\", nargs=\"+\", default=[\"decisions\", \"learnings\"])\n    parser.add_argument(\"--limit\", type=int, default=10)\n    args = parser.parse_args()\n\n    result = search(\n        keywords=args.keywords,\n        sources=args.sources,\n        limit=args.limit\n    )\n    print(yaml.dump(result, default_flow_style=False))\n```\n\n## Uso no Fluxo SDLC\n\n1. **Antes de decidir** - Buscar decisoes similares\n2. **Ao pesquisar** - Consultar documentacao oficial\n3. **Ao resolver problemas** - Verificar learnings relevantes\n4. **Ao implementar** - Buscar padroes ja usados\n\n## Pontos de Pesquisa\n\nPara melhorar esta skill:\n- \"RAG retrieval augmented generation best practices\"\n- \"semantic search for code documentation\"\n- \"knowledge graph software engineering\"\n",{"name":5,"description":24,"allowed-tools":25,"user-invocable":20},"Consulta ao corpus RAG do projeto. 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