brains-trust

$27

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