[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6J6QYH5XSW93RVDVPSR7RC8":3,"skill-same:01M6J6QYH5XSW93RVDVPSR7RC8":109},{"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":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":62,"install":63,"resources":76},"01M6J6QYH5XSW93RVDVPSR7RC8","agent-orchestrator",null,"Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.","caobingsheng\u002Fskills","caobingsheng","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F18641728?v=4","01M6J6H1N0RR38WEA1KVM9V4GB","01M6J6H1SB287CPJQARKE9FXS0",1,"MIT","https:\u002F\u002Fgithub.com\u002Fcaobingsheng\u002Fskills\u002Fblob\u002Fmain\u002Fagent\u002Fagent-orchestrator\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 orchestrator","orchestrator","multi-agent","codex-cli","de","os","meta-skill","scan","do","community","cli","Meta","registry","code","workflows","automation","workflow","orchestration","gemini","safe","2026-11-10 10:12:30",false,"---\nname: agent-orchestrator\ndescription: Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.\nrisk: safe\nsource: community\ndate_added: '2026-03-06'\nauthor: renat\ntags:\n- orchestration\n- multi-agent\n- workflow\n- automation\ntools:\n- claude-code\n- antigravity\n- cursor\n- gemini-cli\n- codex-cli\n---\n\n# Agent Orchestrator\n\n## Overview\n\nMeta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.\n\n## When to Use This Skill\n\n- When you need specialized assistance with this domain\n\n## Do Not Use This Skill When\n\n- The task is unrelated to agent orchestrator\n- A simpler, more specific tool can handle the request\n- The user needs general-purpose assistance without domain expertise\n\n## How It Works\n\nMeta-skill que funciona como camada central de decisao e coordenacao para todo\no ecossistema de skills. Faz varredura automatica, identifica agentes relevantes\ne orquestra multiplos skills para tarefas complexas.\n\n## Principio: Zero Intervencao Manual\n\n- **SEMPRE faz varredura** antes de processar qualquer solicitacao\n- Novas skills sao **auto-detectadas e incluidas** ao criar SKILL.md em qualquer subpasta\n- Skills removidas sao **auto-excluidas** do registry\n- Nenhum comando manual e necessario para registrar novas skills\n\n---\n\n## Workflow Obrigatorio (Toda Solicitacao)\n\nExecute estes passos ANTES de processar qualquer request do usuario.\nOs scripts usam paths relativos automaticamente - funciona de qualquer diretorio.\n\n## Passo 1: Auto-Discovery (Varredura)\n\n```bash\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py\n```\n\nUltra-rapido (\u003C100ms) via cache de hashes MD5. So re-processa arquivos alterados.\nRetorna JSON com resumo de todos os skills encontrados.\n\n## Passo 2: Match De Skills\n\n```bash\npython agent-orchestrator\u002Fscripts\u002Fmatch_skills.py \"\u003Csolicitacao do usuario>\"\n```\n\nRetorna JSON com skills ranqueadas por relevancia. Interpretar o resultado:\n\n| Resultado              | Acao                                                    |\n|:-----------------------|:--------------------------------------------------------|\n| `matched: 0`          | Nenhum skill relevante. Operar normalmente sem skills.  |\n| `matched: 1`          | Um skill relevante. Carregar seu SKILL.md e seguir.     |\n| `matched: 2+`         | Multiplos skills. Executar Passo 3 (orquestracao).      |\n\n## Passo 3: Orquestracao (Se Matched >= 2)\n\n```bash\npython agent-orchestrator\u002Fscripts\u002Forchestrate.py --skills skill1,skill2 --query \"\u003Csolicitacao>\"\n```\n\nRetorna plano de execucao com padrao, ordem dos steps e data flow entre skills.\n\n## Passo Rapido (Atalho)\n\nPara queries simples, os passos 1+2 podem ser combinados em sequencia:\n```bash\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py && python agent-orchestrator\u002Fscripts\u002Fmatch_skills.py \"\u003Csolicitacao>\"\n```\n\n---\n\n## Skill Registry\n\nO registry vive em:\n```\nagent-orchestrator\u002Fdata\u002Fregistry.json\n```\n\n## Locais De Busca\n\nO scanner procura SKILL.md em:\n1. `.claude\u002Fskills\u002F*\u002F` (skills registradas no Claude Code)\n2. `*\u002F` (skills standalone no top-level)\n3. `*\u002F*\\` (skills em subpastas, ate profundidade 3)\n\n## Metadata Por Skill\n\nCada entrada no registry contem:\n\n| Campo          | Descricao                                          |\n|:---------------|:---------------------------------------------------|\n| name           | Nome da skill (do frontmatter YAML)                |\n| description    | Descricao completa (triggers inclusos)             |\n| location       | Caminho absoluto do diretorio                      |\n| skill_md       | Caminho absoluto do SKILL.md                       |\n| registered     | Se esta em .claude\u002Fskills\u002F (true\u002Ffalse)            |\n| capabilities   | Tags de capacidade (auto-extraidas + explicitas)   |\n| triggers       | Keywords de ativacao extraidas da description      |\n| language       | Linguagem principal (python\u002Fnodejs\u002Fbash\u002Fnone)      |\n| status         | active \u002F incomplete \u002F missing                      |\n\n## Comandos Do Registry\n\n```bash\n\n## Scan Rapido (Usa Cache De Hashes)\n\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py\n\n## Tabela De Status Detalhada\n\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py --status\n\n## Re-Scan Completo (Ignora Cache)\n\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py --force\n```\n\n---\n\n## Algoritmo De Matching\n\nPara cada solicitacao, o matcher pontua skills usando:\n\n| Criterio                     | Pontos | Exemplo                               |\n|:-----------------------------|:-------|:--------------------------------------|\n| Nome do skill na query       | +15    | \"use web-scraper\" -> web-scraper      |\n| Keyword trigger exata        | +10    | \"scrape\" -> web-scraper               |\n| Categoria de capacidade      | +5     | data-extraction -> web-scraper        |\n| Sobreposicao de palavras     | +1     | Palavras da query na description      |\n| Boost de projeto             | +20    | Skill atribuida ao projeto ativo      |\n\nThreshold minimo: 5 pontos. Skills abaixo disso sao ignoradas.\n\n## Match Com Projeto\n\n```bash\npython agent-orchestrator\u002Fscripts\u002Fmatch_skills.py --project meu-projeto \"query aqui\"\n```\n\nSkills atribuidas ao projeto recebem +20 de boost automatico.\n\n---\n\n## Padroes De Orquestracao\n\nQuando multiplos skills sao relevantes, o orchestrator classifica o padrao:\n\n## 1. Pipeline Sequencial\n\nSkills formam uma cadeia onde o output de uma alimenta a proxima.\n\n**Quando:** Mix de skills \"produtoras\" (data-extraction, government-data) e \"consumidoras\" (messaging, social-media).\n\n**Exemplo:** web-scraper coleta precos -> whatsapp-cloud-api envia alerta\n\n```\nuser_query -> web-scraper -> whatsapp-cloud-api -> result\n```\n\n## 2. Execucao Paralela\n\nSkills trabalham independentemente em aspectos diferentes da solicitacao.\n\n**Quando:** Todas as skills tem o mesmo papel (todas produtoras ou todas consumidoras).\n\n**Exemplo:** instagram publica post + whatsapp envia notificacao (ambos recebem o mesmo conteudo)\n\n```\nuser_query -> [instagram, whatsapp-cloud-api] -> aggregated_result\n```\n\n## 3. Primario + Suporte\n\nUma skill principal lidera; outras fornecem dados de apoio.\n\n**Quando:** Uma skill tem score muito superior as demais (>= 2x).\n\n**Exemplo:** whatsapp-cloud-api envia mensagem (primario) + web-scraper fornece dados (suporte)\n\n```\nuser_query -> whatsapp-cloud-api (primary) + web-scraper (support) -> result\n```\n\n## Detalhes Em `References\u002FOrchestration-Patterns.Md`\n\n---\n\n## Gerenciamento De Projetos\n\nAtribuir skills a projetos permite boost de relevancia e contexto persistente.\n\n## Arquivo De Projetos\n\n```\nagent-orchestrator\u002Fdata\u002Fprojects.json\n```\n\n## Operacoes\n\n**Criar projeto:**\nAdicionar entrada ao projects.json:\n```json\n{\n  \"name\": \"nome-do-projeto\",\n  \"created_at\": \"2026-02-25T12:00:00\",\n  \"skills\": [\"web-scraper\", \"whatsapp-cloud-api\"],\n  \"description\": \"Descricao do projeto\"\n}\n```\n\n**Adicionar skill a projeto:** Atualizar o array `skills` do projeto.\n\n**Remover skill de projeto:** Remover do array `skills`.\n\n**Consultar skills do projeto:** Ler o projects.json e listar skills atribuidas.\n\n---\n\n## Adicionando Novas Skills\n\nPara adicionar uma nova skill ao ecossistema:\n\n1. Criar uma pasta em qualquer lugar sob `skills root:`\n2. Criar um `SKILL.md` com frontmatter YAML:\n```yaml\n---\nname: minha-nova-skill\ndescription: \"Descricao com keywords de ativacao...\"\n---\n\n## Documentacao Da Skill\n\n```\n3. **Pronto!** O auto-discovery detecta automaticamente na proxima solicitacao.\n\nOpcionalmente, para discovery nativo do Claude Code:\n4. Copiar o SKILL.md para `.claude\u002Fskills\u002F\u003Cnome>\u002FSKILL.md`\n\n## Tags De Capacidade Explicitas (Opcional)\n\nAdicionar ao frontmatter para matching mais preciso:\n```yaml\ncapabilities: [data-extraction, web-automation]\n```\n\n---\n\n## Ver Status De Todos Os Skills\n\n```bash\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py --status\n```\n\n## Interpretar Status\n\n| Status     | Significado                                        |\n|:-----------|:---------------------------------------------------|\n| active     | SKILL.md com name + description presentes          |\n| incomplete | SKILL.md existe mas falta name ou description      |\n| missing    | Diretorio existe mas sem SKILL.md                  |\n\n---\n\n## Skills Atuais Do Ecossistema\n\n| Skill              | Capacidades                           | Status  |\n|:-------------------|:--------------------------------------|:--------|\n| web-scraper        | data-extraction, web-automation       | active  |\n| junta-leiloeiros   | government-data, data-extraction      | active  |\n| whatsapp-cloud-api | messaging, api-integration            | active  |\n| instagram          | social-media, api-integration         | partial |\n\n*Esta tabela e atualizada automaticamente via `scan_registry.py --status`.*\n\n## Best Practices\n\n- Provide clear, specific context about your project and requirements\n- Review all suggestions before applying them to production code\n- Combine with other complementary skills for comprehensive analysis\n\n## Common Pitfalls\n\n- Using this skill for tasks outside its domain expertise\n- Applying recommendations without understanding your specific context\n- Not providing enough project context for accurate analysis\n\n## Related Skills\n\n- `multi-advisor` - Complementary skill for enhanced analysis\n- `task-intelligence` - Complementary skill for enhanced analysis\n","# Agent Orchestrator\n\n## Overview\n\nMeta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.\n\n## When to Use This Skill\n\n- When you need specialized assistance with this domain\n\n## Do Not Use This Skill When\n\n- The task is unrelated to agent orchestrator\n- A simpler, more specific tool can handle the request\n- The user needs general-purpose assistance without domain expertise\n\n## How It Works\n\nMeta-skill que funciona como camada central de decisao e coordenacao para todo\no ecossistema de skills. Faz varredura automatica, identifica agentes relevantes\ne orquestra multiplos skills para tarefas complexas.\n\n## Principio: Zero Intervencao Manual\n\n- **SEMPRE faz varredura** antes de processar qualquer solicitacao\n- Novas skills sao **auto-detectadas e incluidas** ao criar SKILL.md em qualquer subpasta\n- Skills removidas sao **auto-excluidas** do registry\n- Nenhum comando manual e necessario para registrar novas skills\n\n---\n\n## Workflow Obrigatorio (Toda Solicitacao)\n\nExecute estes passos ANTES de processar qualquer request do usuario.\nOs scripts usam paths relativos automaticamente - funciona de qualquer diretorio.\n\n## Passo 1: Auto-Discovery (Varredura)\n\n```bash\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py\n```\n\nUltra-rapido (\u003C100ms) via cache de hashes MD5. So re-processa arquivos alterados.\nRetorna JSON com resumo de todos os skills encontrados.\n\n## Passo 2: Match De Skills\n\n```bash\npython agent-orchestrator\u002Fscripts\u002Fmatch_skills.py \"\u003Csolicitacao do usuario>\"\n```\n\nRetorna JSON com skills ranqueadas por relevancia. Interpretar o resultado:\n\n| Resultado              | Acao                                                    |\n|:-----------------------|:--------------------------------------------------------|\n| `matched: 0`          | Nenhum skill relevante. Operar normalmente sem skills.  |\n| `matched: 1`          | Um skill relevante. Carregar seu SKILL.md e seguir.     |\n| `matched: 2+`         | Multiplos skills. Executar Passo 3 (orquestracao).      |\n\n## Passo 3: Orquestracao (Se Matched >= 2)\n\n```bash\npython agent-orchestrator\u002Fscripts\u002Forchestrate.py --skills skill1,skill2 --query \"\u003Csolicitacao>\"\n```\n\nRetorna plano de execucao com padrao, ordem dos steps e data flow entre skills.\n\n## Passo Rapido (Atalho)\n\nPara queries simples, os passos 1+2 podem ser combinados em sequencia:\n```bash\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py && python agent-orchestrator\u002Fscripts\u002Fmatch_skills.py \"\u003Csolicitacao>\"\n```\n\n---\n\n## Skill Registry\n\nO registry vive em:\n```\nagent-orchestrator\u002Fdata\u002Fregistry.json\n```\n\n## Locais De Busca\n\nO scanner procura SKILL.md em:\n1. `.claude\u002Fskills\u002F*\u002F` (skills registradas no Claude Code)\n2. `*\u002F` (skills standalone no top-level)\n3. `*\u002F*\\` (skills em subpastas, ate profundidade 3)\n\n## Metadata Por Skill\n\nCada entrada no registry contem:\n\n| Campo          | Descricao                                          |\n|:---------------|:---------------------------------------------------|\n| name           | Nome da skill (do frontmatter YAML)                |\n| description    | Descricao completa (triggers inclusos)             |\n| location       | Caminho absoluto do diretorio                      |\n| skill_md       | Caminho absoluto do SKILL.md                       |\n| registered     | Se esta em .claude\u002Fskills\u002F (true\u002Ffalse)            |\n| capabilities   | Tags de capacidade (auto-extraidas + explicitas)   |\n| triggers       | Keywords de ativacao extraidas da description      |\n| language       | Linguagem principal (python\u002Fnodejs\u002Fbash\u002Fnone)      |\n| status         | active \u002F incomplete \u002F missing                      |\n\n## Comandos Do Registry\n\n```bash\n\n## Scan Rapido (Usa Cache De Hashes)\n\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py\n\n## Tabela De Status Detalhada\n\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py --status\n\n## Re-Scan Completo (Ignora Cache)\n\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py --force\n```\n\n---\n\n## Algoritmo De Matching\n\nPara cada solicitacao, o matcher pontua skills usando:\n\n| Criterio                     | Pontos | Exemplo                               |\n|:-----------------------------|:-------|:--------------------------------------|\n| Nome do skill na query       | +15    | \"use web-scraper\" -> web-scraper      |\n| Keyword trigger exata        | +10    | \"scrape\" -> web-scraper               |\n| Categoria de capacidade      | +5     | data-extraction -> web-scraper        |\n| Sobreposicao de palavras     | +1     | Palavras da query na description      |\n| Boost de projeto             | +20    | Skill atribuida ao projeto ativo      |\n\nThreshold minimo: 5 pontos. Skills abaixo disso sao ignoradas.\n\n## Match Com Projeto\n\n```bash\npython agent-orchestrator\u002Fscripts\u002Fmatch_skills.py --project meu-projeto \"query aqui\"\n```\n\nSkills atribuidas ao projeto recebem +20 de boost automatico.\n\n---\n\n## Padroes De Orquestracao\n\nQuando multiplos skills sao relevantes, o orchestrator classifica o padrao:\n\n## 1. Pipeline Sequencial\n\nSkills formam uma cadeia onde o output de uma alimenta a proxima.\n\n**Quando:** Mix de skills \"produtoras\" (data-extraction, government-data) e \"consumidoras\" (messaging, social-media).\n\n**Exemplo:** web-scraper coleta precos -> whatsapp-cloud-api envia alerta\n\n```\nuser_query -> web-scraper -> whatsapp-cloud-api -> result\n```\n\n## 2. Execucao Paralela\n\nSkills trabalham independentemente em aspectos diferentes da solicitacao.\n\n**Quando:** Todas as skills tem o mesmo papel (todas produtoras ou todas consumidoras).\n\n**Exemplo:** instagram publica post + whatsapp envia notificacao (ambos recebem o mesmo conteudo)\n\n```\nuser_query -> [instagram, whatsapp-cloud-api] -> aggregated_result\n```\n\n## 3. Primario + Suporte\n\nUma skill principal lidera; outras fornecem dados de apoio.\n\n**Quando:** Uma skill tem score muito superior as demais (>= 2x).\n\n**Exemplo:** whatsapp-cloud-api envia mensagem (primario) + web-scraper fornece dados (suporte)\n\n```\nuser_query -> whatsapp-cloud-api (primary) + web-scraper (support) -> result\n```\n\n## Detalhes Em `References\u002FOrchestration-Patterns.Md`\n\n---\n\n## Gerenciamento De Projetos\n\nAtribuir skills a projetos permite boost de relevancia e contexto persistente.\n\n## Arquivo De Projetos\n\n```\nagent-orchestrator\u002Fdata\u002Fprojects.json\n```\n\n## Operacoes\n\n**Criar projeto:**\nAdicionar entrada ao projects.json:\n```json\n{\n  \"name\": \"nome-do-projeto\",\n  \"created_at\": \"2026-02-25T12:00:00\",\n  \"skills\": [\"web-scraper\", \"whatsapp-cloud-api\"],\n  \"description\": \"Descricao do projeto\"\n}\n```\n\n**Adicionar skill a projeto:** Atualizar o array `skills` do projeto.\n\n**Remover skill de projeto:** Remover do array `skills`.\n\n**Consultar skills do projeto:** Ler o projects.json e listar skills atribuidas.\n\n---\n\n## Adicionando Novas Skills\n\nPara adicionar uma nova skill ao ecossistema:\n\n1. Criar uma pasta em qualquer lugar sob `skills root:`\n2. Criar um `SKILL.md` com frontmatter YAML:\n```yaml\n---\nname: minha-nova-skill\ndescription: \"Descricao com keywords de ativacao...\"\n---\n\n## Documentacao Da Skill\n\n```\n3. **Pronto!** O auto-discovery detecta automaticamente na proxima solicitacao.\n\nOpcionalmente, para discovery nativo do Claude Code:\n4. Copiar o SKILL.md para `.claude\u002Fskills\u002F\u003Cnome>\u002FSKILL.md`\n\n## Tags De Capacidade Explicitas (Opcional)\n\nAdicionar ao frontmatter para matching mais preciso:\n```yaml\ncapabilities: [data-extraction, web-automation]\n```\n\n---\n\n## Ver Status De Todos Os Skills\n\n```bash\npython agent-orchestrator\u002Fscripts\u002Fscan_registry.py --status\n```\n\n## Interpretar Status\n\n| Status     | Significado                                        |\n|:-----------|:---------------------------------------------------|\n| active     | SKILL.md com name + description presentes          |\n| incomplete | SKILL.md existe mas falta name ou description      |\n| missing    | Diretorio existe mas sem SKILL.md                  |\n\n---\n\n## Skills Atuais Do Ecossistema\n\n| Skill              | Capacidades                           | Status  |\n|:-------------------|:--------------------------------------|:--------|\n| web-scraper        | data-extraction, web-automation       | active  |\n| junta-leiloeiros   | government-data, data-extraction      | active  |\n| whatsapp-cloud-api | messaging, api-integration            | active  |\n| instagram          | social-media, api-integration         | partial |\n\n*Esta tabela e atualizada automaticamente via `scan_registry.py --status`.*\n\n## Best Practices\n\n- Provide clear, specific context about your project and requirements\n- Review all suggestions before applying them to production code\n- Combine with other complementary skills for comprehensive analysis\n\n## Common Pitfalls\n\n- Using this skill for tasks outside its domain expertise\n- Applying recommendations without understanding your specific context\n- Not providing enough project context for accurate analysis\n\n## Related Skills\n\n- `multi-advisor` - Complementary skill for enhanced analysis\n- `task-intelligence` - Complementary skill for enhanced 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cases.","https:\u002F\u002Fgithub.com\u002Fcaobingsheng\u002Fskills\u002Fblob\u002Fmain\u002Ftdd\u002Ftdd-workflows-tdd-red\u002FSKILL.md",[],[134,120,37,135,122,136,137,138,32,139,140,141,142,126],"red","tdd workflows tdd red","edge","generate","behavior","define","tests","for","expected",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":149,"publishedAt":43},"01M6J727C8Q52EG3RTQWMC2X61","tdd-workflows-tdd-green","Implement the minimal code needed to make failing tests pass in the TDD green phase.","https:\u002F\u002Fgithub.com\u002Fcaobingsheng\u002Fskills\u002Fblob\u002Fmain\u002Ftdd\u002Ftdd-workflows-tdd-green\u002FSKILL.md",[],[150,122,151,37,120,152,32,153,36,140,154,126],"green","tdd workflows tdd 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