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name collab
description Gemini and Codex collaboratively brainstorm solutions, building on each other's ideas across rounds. Agent synthesizes the best ideas into a plan.

Have Gemini and Codex collaboratively brainstorm solutions, then synthesize the best ideas into a plan. Both LLMs build on each other's ideas across rounds rather than critiquing positions.

Arguments: $ARGUMENTS

Phase 1: Understand the Task (No Questions)

  1. Explore the codebase - use Glob, Grep, Read to understand:

    • Relevant files and their structure
    • Existing patterns and conventions
    • Dependencies and interfaces

  2. Make reasonable assumptions - do NOT ask clarifying questions

    • Use best judgment based on codebase context
    • Prefer simpler solutions when ambiguous
    • Follow existing patterns in the codebase

  3. Prepare context summary - create a brief summary of:

    • The task to be implemented
    • Relevant files discovered
    • Key patterns and conventions in the codebase
    • Any constraints or considerations

Phase 2: Initial Ideas

Have both LLMs independently brainstorm approaches (in parallel).

Seed prompt:

I need to implement the following task:

[Task description]

Here's what I found in the codebase:
[Context summary - relevant files, patterns, conventions]

Brainstorm implementation ideas:
1. **Ideas**: List 2-3 possible approaches with brief descriptions
2. **Favorite**: Which approach do you lean toward and why?
3. **Open questions**: What aspects are you unsure about or would benefit from another perspective?
4. **Risks**: What could go wrong or be tricky?

Think creatively. Share rough ideas — we're exploring, not committing.

Spawn BOTH as parallel subagents (Agent tool, subagent_type: "general-purpose", model: "sonnet"). Each subagent prompt must include the full seed prompt text and file list so it can make the MCP call independently.

Gemini subagent — prompt must include:

  • Call mcp__consult-llm__consult_llm with model: "gemini", prompt: the seed prompt, files: [array of relevant source files]
  • Return the COMPLETE response including any [thread_id:xxx] prefix

Codex subagent — prompt must include:

  • Call mcp__consult-llm__consult_llm with model: "openai", prompt: the seed prompt, files: [array of relevant source files]
  • Return the COMPLETE response including any [thread_id:xxx] prefix

Extract thread IDs: Save gemini_thread_id and codex_thread_id from the [thread_id:xxx] prefixes in the subagent responses.

Present both sets of ideas to the user.

Phase 3: Build On Each Other

Each round, share both LLMs' ideas with each other and ask them to build on them (in parallel). Use thread_id to continue each LLM's conversation. Continue until the ideas converge into a clear approach — typically 2-3 rounds, but use as many as needed.

Build-on prompt (same for both, include the other's ideas):

A collaborator shared these ideas:

[Other LLM's response from the previous round]

Build on their thinking:
1. **What resonates**: Which ideas are strong? Why?
2. **Combinations**: Can any ideas be combined into something better?
3. **New ideas**: Did their thinking spark any new approaches?
4. **Refinements**: How would you improve the most promising ideas so far?
5. **Concerns resolved**: Did their ideas address any open questions?

Keep building — don't tear down. Refine toward the best solution.

Spawn BOTH as parallel subagents (Agent tool, subagent_type: "general-purpose", model: "sonnet"). Each subagent prompt must include the full build-on prompt text and thread_id.

Gemini subagent — prompt must include:

  • Call mcp__consult-llm__consult_llm with model: "gemini", prompt: build-on prompt with Codex's ideas, thread_id: gemini_thread_id
  • Return the COMPLETE response including any [thread_id:xxx] prefix

Codex subagent — prompt must include:

  • Call mcp__consult-llm__consult_llm with model: "openai", prompt: build-on prompt with Gemini's ideas, thread_id: codex_thread_id
  • Return the COMPLETE response including any [thread_id:xxx] prefix

Present both responses to the user after each round.

When to stop: Both LLMs are refining details rather than introducing new ideas, and a clear approach has emerged. Don't stop while there are still unresolved open questions or competing directions.

Phase 4: Synthesize

After all rounds, synthesize the brainstorm into a plan:

  1. Identify the strongest ideas — which approaches gained momentum across rounds?

  2. Note convergence — where did both LLMs naturally align?

  3. Pick the best combination — merge the strongest elements into one coherent approach

  4. Write the plan:

# [Feature Name] Implementation Plan

**Goal:** [One sentence describing what this builds]

## Brainstorm Summary

**Key ideas from Gemini:** [2-3 bullet points]
**Key ideas from Codex:** [2-3 bullet points]
**Convergence:** [Where they naturally agreed]
**Synthesis:** [How the final approach combines the best of both]

---

### Task 1: [Short description]

**Files:**
- Create: `exact/path/to/file.py`
- Modify: `exact/path/to/existing.py` (lines 123-145)

**Steps:**
1. [Specific action]
2. [Specific action]

**Code:**
```language
// Include actual code, not placeholders
```

---

Guidelines:

  • Exact file paths - never "somewhere in src/"
  • Complete code - show the actual code
  • Small tasks - 2-5 minutes of work each
  • DRY, YAGNI - only what's needed

Save the plan to history/plan-<feature-name>.md.