Agent Orchestrator

Agent Orchestrator

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/agent-orchestrator from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Agent Orchestrator

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: How It Works, Principio: Zero Intervencao Manual, Passo 1: Auto-Discovery (Varredura), Passo 2: Match De Skills, Passo 3: Orquestracao (Se Matched >= 2), Passo Rapido (Atalho).

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• When you need specialized assistance with this domain
• The task is unrelated to agent orchestrator
• A simpler, more specific tool can handle the request
• The user needs general-purpose assistance without domain expertise
• Use when the request clearly matches the imported source intent: Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Execute estes passos ANTES de processar qualquer request do usuario.
2. Os scripts usam paths relativos automaticamente - funciona de qualquer diretorio.
3. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
4. Read the overview and provenance files before loading any copied upstream support files.
5. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
6. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
7. Validate the result against the upstream expectations and the evidence you can point to in the copied files.

Imported Workflow Notes

Imported: Workflow Obrigatorio (Toda Solicitacao)

Execute estes passos ANTES de processar qualquer request do usuario. Os scripts usam paths relativos automaticamente - funciona de qualquer diretorio.

Imported: Overview

Meta-skill que orquestra todos os agentes do ecossistema. Scan automatico de skills, match por capacidades, coordenacao de workflows multi-skill e registry management.

Imported: How It Works

Meta-skill que funciona como camada central de decisao e coordenacao para todo o ecossistema de skills. Faz varredura automatica, identifica agentes relevantes e orquestra multiplos skills para tarefas complexas.

Examples

Example 1: Ask for the upstream workflow directly

Use @agent-orchestrator to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @agent-orchestrator against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @agent-orchestrator for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @agent-orchestrator using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Provide clear, specific context about your project and requirements
• Review all suggestions before applying them to production code
• Combine with other complementary skills for comprehensive analysis
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.

Imported Operating Notes

Imported: Best Practices

• Provide clear, specific context about your project and requirements
• Review all suggestions before applying them to production code
• Combine with other complementary skills for comprehensive analysis

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/agent-orchestrator, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• capability-taxonomy.md
• orchestration-patterns.md
• match_skills.py
• orchestrate.py
• requirements.txt
• scan_registry.py

Imported Reference Notes

Imported: Detalhes Em References/Orchestration-Patterns.Md

Imported: Principio: Zero Intervencao Manual

• SEMPRE faz varredura antes de processar qualquer solicitacao
• Novas skills sao auto-detectadas e incluidas ao criar SKILL.md em qualquer subpasta
• Skills removidas sao auto-excluidas do registry
• Nenhum comando manual e necessario para registrar novas skills

Imported: Passo 1: Auto-Discovery (Varredura)

python agent-orchestrator/scripts/scan_registry.py

Ultra-rapido (<100ms) via cache de hashes MD5. So re-processa arquivos alterados. Retorna JSON com resumo de todos os skills encontrados.

Imported: Passo 2: Match De Skills

python agent-orchestrator/scripts/match_skills.py "<solicitacao do usuario>"

Retorna JSON com skills ranqueadas por relevancia. Interpretar o resultado:

Imported: Passo 3: Orquestracao (Se Matched >= 2)

python agent-orchestrator/scripts/orchestrate.py --skills skill1,skill2 --query "<solicitacao>"

Retorna plano de execucao com padrao, ordem dos steps e data flow entre skills.

Imported: Passo Rapido (Atalho)

Para queries simples, os passos 1+2 podem ser combinados em sequencia:

python agent-orchestrator/scripts/scan_registry.py && python agent-orchestrator/scripts/match_skills.py "<solicitacao>"

Imported: Skill Registry

O registry vive em:

agent-orchestrator/data/registry.json

Imported: Locais De Busca

O scanner procura SKILL.md em:

1. .claude/skills/*/ (skills registradas no Claude Code)
2. */ (skills standalone no top-level)
3. */*\ (skills em subpastas, ate profundidade 3)

Imported: Metadata Por Skill

Cada entrada no registry contem:

Imported: Comandos Do Registry

#### Imported: Scan Rapido (Usa Cache De Hashes)

python agent-orchestrator/scripts/scan_registry.py

#### Imported: Tabela De Status Detalhada

python agent-orchestrator/scripts/scan_registry.py --status

#### Imported: Re-Scan Completo (Ignora Cache)

python agent-orchestrator/scripts/scan_registry.py --force

Imported: Algoritmo De Matching

Para cada solicitacao, o matcher pontua skills usando:

Threshold minimo: 5 pontos. Skills abaixo disso sao ignoradas.

Imported: Match Com Projeto

python agent-orchestrator/scripts/match_skills.py --project meu-projeto "query aqui"

Skills atribuidas ao projeto recebem +20 de boost automatico.

Imported: Padroes De Orquestracao

Quando multiplos skills sao relevantes, o orchestrator classifica o padrao:

Imported: 1. Pipeline Sequencial

Skills formam uma cadeia onde o output de uma alimenta a proxima.

Quando: Mix de skills "produtoras" (data-extraction, government-data) e "consumidoras" (messaging, social-media).

Exemplo: web-scraper coleta precos -> whatsapp-cloud-api envia alerta

user_query -> web-scraper -> whatsapp-cloud-api -> result

Imported: 2. Execucao Paralela

Skills trabalham independentemente em aspectos diferentes da solicitacao.

Quando: Todas as skills tem o mesmo papel (todas produtoras ou todas consumidoras).

Exemplo: instagram publica post + whatsapp envia notificacao (ambos recebem o mesmo conteudo)

user_query -> [instagram, whatsapp-cloud-api] -> aggregated_result

Imported: 3. Primario + Suporte

Uma skill principal lidera; outras fornecem dados de apoio.

Quando: Uma skill tem score muito superior as demais (>= 2x).

Exemplo: whatsapp-cloud-api envia mensagem (primario) + web-scraper fornece dados (suporte)

user_query -> whatsapp-cloud-api (primary) + web-scraper (support) -> result

Imported: Gerenciamento De Projetos

Atribuir skills a projetos permite boost de relevancia e contexto persistente.

Imported: Arquivo De Projetos

agent-orchestrator/data/projects.json

Imported: Operacoes

Criar projeto: Adicionar entrada ao projects.json:

{
  "name": "nome-do-projeto",
  "created_at": "2026-02-25T12:00:00",
  "skills": ["web-scraper", "whatsapp-cloud-api"],
  "description": "Descricao do projeto"
}

Adicionar skill a projeto: Atualizar o array skills do projeto.

Remover skill de projeto: Remover do array skills.

Consultar skills do projeto: Ler o projects.json e listar skills atribuidas.

Imported: Adicionando Novas Skills

Para adicionar uma nova skill ao ecossistema:

1. Criar uma pasta em qualquer lugar sob skills root:
2. Criar um SKILL.md com frontmatter YAML:

---
name: minha-nova-skill
description: "Descricao com keywords de ativacao..."
---

#### Imported: Documentacao Da Skill

3. Pronto! O auto-discovery detecta automaticamente na proxima solicitacao.

Opcionalmente, para discovery nativo do Claude Code: 4. Copiar o SKILL.md para .claude/skills/<nome>/SKILL.md

Imported: Tags De Capacidade Explicitas (Opcional)

Adicionar ao frontmatter para matching mais preciso:

capabilities: [data-extraction, web-automation]

Imported: Ver Status De Todos Os Skills

python agent-orchestrator/scripts/scan_registry.py --status

Imported: Interpretar Status

Imported: Skills Atuais Do Ecossistema

Esta tabela e atualizada automaticamente via scan_registry.py --status.

Imported: Common Pitfalls

• Using this skill for tasks outside its domain expertise
• Applying recommendations without understanding your specific context
• Not providing enough project context for accurate analysis

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.