brains-trust

name

brains-trust

description

"Get a second opinion from leading AI models on code, architecture, strategy, prompting, or anything. Queries models via OpenRouter, Gemini, or OpenAI APIs. Supports single opinion, multi-model consensus, and devil's advocate patterns. Use whenever the user says 'brains trust', 'second opinion', 'ask gemini', 'ask gpt', 'peer review', 'consult another model', 'challenge this', or 'devil's advocate'."

Brains Trust

Consult other leading AI models for a second opinion. Not limited to code — works for architecture, strategy, prompting, debugging, writing, or any question where a fresh perspective helps.

Defaults (When User Just Says "Brains Trust")

If the user triggers this skill without specifying what to consult about, apply these defaults:

1. Pattern: Consensus (2 models from different providers) — it's called "brains trust", not "single opinion"
2. Scope: Whatever Claude has been working on in the current session. Look at recent context: files edited, decisions made, architecture discussed, problems being solved.
3. Mode: Infer from context:
   • Recently wrote/edited code → Code Review
   • In a planning or design discussion → Architecture
   • Debugging something → Debug
   • Building prompts or skills → Prompting
   • No clear signal → General (ask: "what are we missing? what are our blind spots?")
4. Models: Pick the newest pro-tier model from 2 different providers (check models.flared.au). Prefer diversity: e.g. one Google + one OpenAI, or one Qwen + one Google. Never two from the same provider.
5. Prompt focus: "Review what we've been working on. What are we missing? What could be improved? What blind spots might we have? Are there simpler approaches we haven't considered?"

Trigger → Default Mapping

Trigger	Default pattern	Default scope
"brains trust"	Consensus (2 models)	Current session work
"second opinion"	Single (1 model)	Current session work
"ask gemini" / "ask gpt"	Single (specified provider)	Current session work
"peer review"	Consensus (2 models)	Recently changed files
"challenge this" / "devil's advocate"	Devil's advocate (1 model)	Claude's current position

The user can always override by being specific: "brains trust this config file", "ask gemini about the auth approach", etc.

Setup

Set at least one API key as an environment variable:

# Recommended — one key covers all providers
export OPENROUTER_API_KEY="your-key"

# Optional — direct access (often faster/cheaper)
export GEMINI_API_KEY="your-key"
export OPENAI_API_KEY="your-key"

OpenRouter is the universal path — one key gives access to Gemini, GPT, Qwen, DeepSeek, Llama, Mistral, and more.

Current Models

Do not use hardcoded model IDs. Before every consultation, fetch the current leading models:

https://models.flared.au/llms.txt

This is a live-updated, curated list of ~40 leading models from 11 providers, filtered from OpenRouter's full catalogue. Use it to pick the right model for the task.

For programmatic use in the generated Python script: https://models.flared.au/json

Consultation Patterns

Pattern	Default for	What happens
Consensus	"brains trust", "peer review"	Ask 2 models from different providers in parallel, compare where they agree/disagree
Single	"second opinion", "ask gemini", "ask gpt"	Ask one model, synthesise with your own view
Devil's advocate	"challenge this", "devil's advocate"	Ask a model to explicitly argue against your current position

For consensus, always pick models from different providers (e.g. one Google + one Qwen) for maximum diversity of perspective.

Modes

Mode	When	Model tier
Code Review	Review files for bugs, patterns, security	Flash
Architecture	Design decisions, trade-offs	Pro
Debug	Stuck after 2+ failed attempts	Flash
Security	Vulnerability scan	Pro
Strategy	Business, product, approach decisions	Pro
Prompting	Improve prompts, system prompts, KB files	Flash
General	Any question, brainstorm, challenge	Flash

Pro tier: The most capable model from the chosen provider (e.g. google/gemini-3.1-pro-preview, openai/gpt-5.4). Flash tier: Fast, cheaper models for straightforward analysis (e.g. google/gemini-3-flash-preview, qwen/qwen3.5-flash-02-23).

Workflow

1. Detect available keys — check OPENROUTER_API_KEY, GEMINI_API_KEY, OPENAI_API_KEY in environment. If none found, show setup instructions and stop.

2. Fetch current models — WebFetch https://models.flared.au/llms.txt and pick appropriate models based on mode (pro vs flash) and consultation pattern (single vs consensus). If user requested a specific provider ("ask gemini"), use that.

3. Read target files into context (if code-related). For non-code questions (strategy, prompting, general), skip file reading.

4. Build prompt using the AI-to-AI template from references/prompt-templates.md. Include file contents inline with --- filename --- separators. Do not set output token limits — let models reason fully.

5. Create consultation directory at .jez/artifacts/brains-trust/{timestamp}-{topic}/ (e.g. 2026-03-10-1423-auth-architecture/). Write the prompt to prompt.txt inside it — never pass code inline via bash arguments (shell escaping breaks it).

6. Generate and run Python script at .jez/scripts/brains-trust.py using patterns from references/provider-api-patterns.md:

   • Reads prompt from the consultation directory's prompt.txt
   • Calls the selected API(s)
   • For consensus mode: calls multiple APIs in parallel using concurrent.futures
   • Saves each response to {model}.md in the consultation directory
   • Prints results to stdout

7. Synthesise — read the responses, present findings to the user. Note where models agree and disagree. Add your own perspective (agree/disagree with reasoning). Let the user decide what to act on.

When to Use

Good use cases:

• Before committing major architectural changes
• When stuck debugging after multiple attempts
• Architecture decisions with multiple valid options
• Reviewing security-sensitive code
• Challenging your own assumptions on strategy or approach
• Improving system prompts or KB files
• Any time you want a fresh perspective

Avoid using for:

• Simple syntax checks (Claude handles these)
• Every single edit (too slow, costs money)
• Questions with obvious, well-known answers

Critical Rules

1. Never hardcode model IDs — always fetch from models.flared.au first
2. Never cap output tokens — don't set max_tokens or maxOutputTokens
3. Always write prompts to file — never pass via bash arguments
4. Include file contents inline — attach code context directly in the prompt
5. Use AI-to-AI framing — the model is advising Claude, not talking to the human
6. Print progress to stderr — the Python script must print status updates (Calling gemini-2.5-pro..., Received response from qwen3.5-plus.) so the user knows it's working during the 30-90 second wait

Reference Files

When	Read
Building prompts for any mode	references/prompt-templates.md
Generating the Python API call script	references/provider-api-patterns.md