multiclaude

name	multiclaude
description	Multi-phase agent orchestration. Fan out parallel agents to explore, research, and synthesize findings. Use when the user wants comprehensive investigation of a problem, codebase area, or question that benefits from parallel exploration.
allowed-tools	Read WebSearch WebFetch Write(~/.agents/findings/*) Bash(mkdir:~/.agents/findings)
compatibility	Codex users must add ~/.agents/findings to writable_roots in config.toml or use --add-dir ~/.agents/findings

Parallel Agent Orchestration

You are orchestrating a multi-phase investigation. Follow the pattern below precisely.

Platform Notes

How you spawn sub-agents depends on your platform:

• Claude Code: Use the Task tool with subagent_type=general-purpose, model=opus. Spawn parallel agents by issuing multiple Task calls in a single message. Always use the strongest available model (opus).
• Codex: Use the sandbox execution model to run parallel investigations. Spawn sub-tasks and ensure each writes its findings to disk.
• Other agents: Use whatever sub-agent or task spawning mechanism your platform provides. The key requirements are: (1) agents can read/write files, (2) multiple agents can run in parallel, (3) each agent gets its own isolated prompt with context you provide.

Findings Directory

All findings go in ~/.agents/findings/. Generate a session slug using the adjective-verb-noun pattern (e.g., curious-mapping-auth). Each file in the session follows this naming:

• Main orchestrator file: {slug}.md
• Agent findings: {slug}-agent-{agent-id}.md

The orchestrator file ({slug}.md) tracks the overall mission, phase transitions, and final synthesis. Agent files contain each agent's raw findings.

Phase 1: Explore & Inventory (Single Agent)

Spawn ONE agent to comprehensively explore the problem space. Its prompt must include:

1. The user's original question/problem
2. Instruction to be thorough: read whole files, trace call chains, check configs
3. Instruction to write findings to ~/.agents/findings/{slug}-agent-{agent-id}.md as structured markdown
4. Instruction to end its findings with a ## Recommended Facets section listing 2-4 independent sub-investigations that would benefit from parallel exploration
5. CRITICAL: Instruction to return a list of ALL findings files it created, as the LAST thing in its response, in this exact format:

   FINDINGS_FILES:
   - /path/to/file1.md
   - /path/to/file2.md
   

After this agent returns, write the orchestrator file ({slug}.md) with:

• The mission statement
• Phase 1 summary
• The facets identified

Show the user the facets and ask if they want to adjust before fanning out.

Phase 2: Fan Out (2-4 Parallel Agents)

Spawn 2-4 agents IN PARALLEL. Each agent:

• Gets ONE facet to investigate deeply
• Must write findings to ~/.agents/findings/{slug}-agent-{agent-id}.md
• Must be told what the OTHER agents are investigating (so it avoids overlap)
• Must be told the Phase 1 context (paste the explore agent's summary, not the full file)
• CRITICAL: Must return FINDINGS_FILES: list as the last thing in its response

Phase 3: Synthesize (Single Agent)

Spawn ONE agent that:

• Reads ALL findings files from phases 1 and 2
• Looks for: contradictions, gaps, connections, patterns, surprises
• Writes synthesis to ~/.agents/findings/{slug}-synthesis.md
• Ends with ## Open Questions (things still unclear) and ## Recommendations (what to do next)
• CRITICAL: Returns FINDINGS_FILES: list

Update the orchestrator file with the synthesis summary.

Phase 4: Optional Second Fan-Out

If the synthesis reveals follow-up work worth parallelizing, repeat Phase 2-3 with new facets. Only do this if the user approves or the original request clearly warrants it.

Rules

1. Use the strongest model available. Always prefer the most capable model your platform offers for every phase.
2. Always return FINDINGS_FILES. Every agent prompt must include this instruction. Parse the returned list and track all files.
3. Never skip the confirmation between Phase 1 and Phase 2. Show the facets, let the user adjust.
4. Agents must write files, not just return text. The findings files ARE the artifact. Return messages are ephemeral; files persist.
5. Include context in every agent prompt. Agents start fresh. Give them the mission, relevant prior findings, and their specific assignment.
6. Update the orchestrator file ({slug}.md) after every phase with a running log of what happened, which agents ran, and what files were produced.
7. Final response to the user should summarize the key findings and list all files produced.

Slug Generation

Generate a slug like: {adjective}-{verb}-{noun} where:

• adjective: a descriptive word (curious, bright, fuzzy, etc.)
• verb: an -ing word (mapping, tracing, hunting, etc.)
• noun: related to the investigation topic when possible

Examples: curious-mapping-auth, bright-tracing-perf, fuzzy-hunting-memory

Orchestrator File Template

# Investigation: {mission title}

## Mission
{user's original request}

## Session
- Slug: {slug}
- Started: {timestamp}
- Status: {in-progress|complete}

## Phase 1: Explore
- Agent: {agent-id}
- File: {path}
- Summary: {2-3 sentences}
- Facets identified:
  1. {facet}
  2. {facet}
  ...

## Phase 2: Fan Out
| Facet | Agent | File | Key Findings |
|-------|-------|------|-------------|
| ... | ... | ... | ... |

## Phase 3: Synthesis
- File: {path}
- Key findings: ...
- Open questions: ...
- Recommendations: ...

## All Files
- {list every findings file produced}