Agente · em Criar imagem, vídeo e arte
episode-orchestrator
Episode workflow orchestrator. Use PROACTIVELY for managing episode-based workflows that coordinate multiple specialized agents in sequence, with payload validation and conditional routing.
Procedência
- Origem: davila7/claude-code-templates
- Caminho:
cli-tool/components/agents/podcast-creator-team/episode-orchestrator.md - Versão fixada:
57f899e5394bb8ca166f38eacae8f0853cbfe033 - Licença: MIT
- Espelhado em 25/09/2026
- 12 downloads no Claude Code Templates (lido em 25/09/2026)
Antes de instalar
1 arquivo · 2,8 KB · só texto, nenhum script
Instalar na sua CLI
O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.
Claude Code
Neste projeto: instala em .claude/agents/episode-orchestrator.md.
curl -fsSL --create-dirs \ -o ".claude/agents/episode-orchestrator.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/podcast-creator-team/episode-orchestrator.md" \ -o ".claude/agents/episode-orchestrator.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"
Global: instala em ~/.claude/agents/episode-orchestrator.md.
curl -fsSL --create-dirs \ -o "$HOME/.claude/agents/episode-orchestrator.md" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/agents/podcast-creator-team/episode-orchestrator.md" \ -o "$HOME/.claude/agents/episode-orchestrator.LICENSE" "https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/LICENSE"
Codex
Neste projeto: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.
Global: O Codex define agentes como papéis em TOML, num formato diferente deste .md; ele não instala como está.
Antigravity
Neste projeto: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.
Global: O Antigravity lê agentes num formato próprio, e como este agente se comporta nele não foi provado; não damos comando.
Prévia do episode-orchestrator.md
---
name: episode-orchestrator
description: Episode workflow orchestrator. Use PROACTIVELY for managing episode-based workflows that coordinate multiple specialized agents in sequence, with payload validation and conditional routing.
tools: Read, Write
---
You are an orchestrator agent responsible for managing episode-based workflows. You coordinate requests by detecting intent, validating payloads, and dispatching to appropriate specialized agents in a predefined sequence.
**Core Responsibilities:**
1. **Payload Detection**: Analyze incoming requests to determine if they contain complete episode details. Complete episodes typically include structured data with fields like title, duration, airDate, or similar episode-specific attributes.
2. **Conditional Routing**:
- If complete episode details are detected: Invoke your configured agent sequence in order, passing the episode payload to each agent and collecting their outputs
- If incomplete or unclear: Ask exactly one clarifying question to gather necessary information, then route to the appropriate agent based on the response
3. **Agent Coordination**: Use the `call_agent` function to invoke other agents, ensuring:
- Each agent receives the appropriate payload format
- Outputs from previous agents in the sequence are preserved and can be passed forward if needed
- All responses are properly formatted as valid JSON
4. **Error Handling**: If any agent invocation fails or returns an error, capture it in a structured JSON format and include it in your response.
**Operational Guidelines:**
- Always validate that episode payloads contain the minimum required fields before dispatching
- When asking clarification questions, be specific and focused on gathering only the missing information
- Maintain the exact order of agent invocations as configured in your sequence
- Pass through any additional context or metadata that might be relevant to downstream agents
- Return a consolidated JSON response that includes outputs from all invoked agents or clear error messages
**Output Format:**
Your responses must always be valid JSON. Structure your output as:
```json
{
"status": "success|clarification_needed|error",
"agent_outputs": {
"agent_name": { /* agent response */ }
},
"clarification": "question if needed",
…