What is agent-council?

A tool for running a structured debate among several expert AI agents about a question. It compares perspectives, tests assumptions, examines disagreements, and produces a combined decision view with confidence information.

Use it to compare technical choices, run a premortem, ask several perspectives for advice, examine alternatives, and produce a synthesis with convergence diagnostics.

It helps when a decision has real tradeoffs, hidden assumptions, high stakes, or no obvious correct answer. The debate format can expose weaknesses that a single answer may miss.

agent-council — Skill for Claude Code and Codex by magnus919

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add magnus919/agent-skills --skill agent-council

Clone the repo

git clone --depth 1 https://github.com/magnus919/agent-skills

Made for: Claude Code, Codex.

Its marketplace also offers this one on its own, as the plugin agent-council — /plugin install agent-council after adding the marketplace above.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/magnus919/agent-skills/agent-council/github.svg)](https://agentmods.dev/skills/magnus919/agent-skills/agent-council)

Your own site

<a href="https://agentmods.dev/skills/magnus919/agent-skills/agent-council"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/agent-council/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/magnus919/agent-skills/agent-council"><img src="https://agentmods.dev/badge/skills/magnus919/agent-skills/agent-council.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 129 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 4,237 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector warn 7 Sept 2026

SkillSpector: 1 finding, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

high Privilege Escalation · line 202
Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.

Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00129	$0.04237
Opus 5	$0.00064	$0.02119
Sonnet 5	$0.00026	$0.00847
Haiku 4.5	$0.00013	$0.00424

Measured 11d ago against content hash eb4ec52a1998, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

agent-council scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 17 executable files (agent_council/__init__.py, agent_council/__main__.py, agent_council/cli.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

agent-council/SKILL.md · 345 lines

How it starts

The opening of the file, as written. The whole thing — 345 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent Council

Spawn a panel of expert agents to debate any question. The council runs a structured protocol — compose, premortem, position, cross-examination (iterative), synthesis — and produces a decision landscape with convergence diagnostics.

When to Use

Invoke the council when any of these apply:

The question has genuine tradeoffs with no clear correct answer
You want multi-perspective analysis to surface hidden assumptions
A decision would benefit from adversarial collaboration
You want confidence diagnostics (not just a recommendation)
The question has high stakes or irreversible consequences

Signal phrases: "Let's get multiple perspectives on this" / "Debate this: X" / "What would experts say about X" / "What are we missing?"

Quick Start

1. Install

# One-time setup
pip install pydantic-ai
pip install agent-council

# Or install from this skill directory:
python3 scripts/bootstrap.py

2. Configure

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

3. Run

agent-council "Should we use Postgres or SQLite for this service?"

Command Reference

agent-council [OPTIONS] <question>

Options:
  --agents, -n {3,4,5,6,7}  Number of agents (default: 5)
  --mode, -m {quick,medium,deep}  Debate depth (default: medium)
  --profiles TEXT          Comma-separated profile names from the hermes-profiles
                           library (e.g. "debugger,researcher,product-manager")
  --persona-file PATH      JSON file with custom agent personas
  --json                   Output structured JSON instead of markdown
  --verbose, -v            Show phase-by-phase progress
  --max-rounds INTEGER     Max cross-examination rounds (default: 4)
  --convergence FLOAT      Convergence threshold (default: 0.10)

Mode Selection

Mode	Agents	Rounds	When to use
`quick`	3	1 cross-examine round	Low-stakes check, fast answer needed
`medium` (default)	5	Eval-driven, up to 4 rounds	Standard decisions
`deep`	7	Eval-driven, up to 4 rounds	High-stakes, hidden assumptions

Read the full file on GitHub · 345 lines

Files

What ships with it

25 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

11d ago First seen · 345 lines · 129 tokens per session scan A eb4ec52a1998

Subscribe to this mod's changes

agent-council is a skill published in the GitHub repository magnus919/agent-skills (76 stars, last pushed today), licensed MIT. It adds 129 tokens to every session and 4,237 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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