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:

• Pattern: Consensus (2 models from different providers) — it's called "brains trust", not "single opinion"

• Scope: Whatever Claude has been working on in the current session. Look at recent context: files edited, decisions made, architecture discussed, problems being solved.

• 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?")

• 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.

• 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

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

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.

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

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.

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).

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

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

• Never hardcode model IDs — always fetch from models.flared.au first

• Never cap output tokens — don't set max_tokens or maxOutputTokens

• Always write prompts to file — never pass via bash arguments

• Include file contents inline — attach code context directly in the prompt

• Use AI-to-AI framing — the model is advising Claude, not talking to the human

• 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