magnus919_agent-skills/agent-council

Agent Council

Multi-agent structured debate system — spawn a panel of expert agents to debate any question with convergence-aware iteration.

pip install agent-council
agent-council "Should we migrate from SQLite to Postgres?"

Quick Start

export AGENT_COUNCIL_API_KEY="sk-..."
export AGENT_COUNCIL_MODEL="openai:gpt-5.6-luna"

agent-council "Should we use WebSockets or SSE for real-time notifications?"

Features

• Structured debate protocol — compose, premortem, position, cross-examine (iterative), synthesis
• Convergence-aware iteration — the protocol measures confidence dispersion and stops when diminishing returns set in, not at a hardcoded round count
• Typed outputs — every phase produces validated Pydantic models, consumable as JSON or human-readable markdown
• Custom personas — supply your own agent definitions, or let the compose phase generate them from the question
• Convergence diagnostics — confidence dispersion, position overlap, argument novelty — surfaced in every synthesis report
• Cross-platform — works with any AI harness that supports agentskills.io skills (Claude Code, Cursor, Hermes Agent, OpenHands, etc.)

Installation

pip install pydantic-ai
pip install agent-council

Or install from the skill directory:

pip install -e /path/to/agent-council/

Pip and wheel installs include generated and user-supplied personas, but not the hermes-profiles library. To use real bundled profiles, start from a recursive source checkout.

Usage

# Quick debate (3 agents, 1 cross-examine round)
agent-council --mode quick "Should we use Postgres or SQLite?"

# Standard debate (5 agents, iterative cross-examination)
agent-council "What architecture should we choose for this service?"

# Deep debate (7 agents, full protocol with assumption mapping)
agent-council --mode deep --agents 7 "Should we migrate to microservices?"

# With custom personas
agent-council --persona-file personas.json "Evaluate our cloud strategy"

# JSON output for programmatic consumption
agent-council --json "Which cloud provider should we choose?"

Output

The synthesis report includes:

• Confidence dispersion table — agent-by-agent confidence before and after debate
• Shared risks — failure modes identified in the pre-mortem (before positional commitment)
• Shared concerns — what survived cross-examination as genuine shared risk
• Genuine disagreements — positions that remained unresolved after debate
• Assumptions per position — what would need to be true for each position to be correct
• Principal's path — narrative synthesis of the decision landscape

Configuration

Env var	Required	Default	Description
AGENT_COUNCIL_API_KEY	Yes	—	API key for your LLM provider
AGENT_COUNCIL_MODEL	No	openai:gpt-5.6-luna	Model string in provider:model format (e.g. deepseek:deepseek-v4-flash)
AGENT_COUNCIL_BASE_URL	No	Provider default	Custom API endpoint (e.g. https://api.deepseek.com/v1)

License

MIT

Why Install This Skill

This skill packages practical, reusable guidance for this domain so you can move from a real task to a dependable result without rebuilding the workflow each time.

What You Get

A focused workflow in SKILL.md, with the referenced scripts, templates, and supporting material available when the task needs them.

Triggers

Use this skill for the task types and keywords described in its SKILL.md description.

Requirements

Check the compatibility requirements in SKILL.md before using commands or integrations from this skill.