council

council is a command for Claude Code from aaronbassett/agent-foundry. It costs 41 tokens per session (811 once invoked), scanned A, original, MIT.

A decision-review command that asks several reviewers to examine a question from different viewpoints, then combines their positions. It is intended for unclear, value-based, or multi-stakeholder decisions.

In plain words
What is it for?
Use it for architecture choices, product decisions, or other questions where operations, security, delivery speed, future maintenance, and other perspectives all matter.
Why use it?
It helps expose how different people may define the problem and judge the possible answers differently.

Command for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: mentions subagents.

Part of the decision-making plugin — 1 skill, 7 commands, 2 agents shipped together

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.

agentmods
npx agentmods add commands/aaronbassett/agent-foundry/council
Clone the repo
git clone --depth 1 https://github.com/aaronbassett/agent-foundry

Made for: Claude Code.

Or install decision-making, the plugin that ships this one along with the rest of its 1 skill, 7 commands, 2 agents.

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.

agentmods badge for council

README.md
[![agentmods](https://agentmods.dev/badge/commands/aaronbassett/agent-foundry/council.svg)](https://agentmods.dev/commands/aaronbassett/agent-foundry/council)
Your own site
<a href="https://agentmods.dev/commands/aaronbassett/agent-foundry/council"><img src="https://agentmods.dev/badge/commands/aaronbassett/agent-foundry/council.svg" alt="Measured on agentmods" height="20"></a>
Per session 41 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 811 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00041 $0.00811
Opus 5 $0.00020 $0.00405
Sonnet 5 $0.00008 $0.00162
Haiku 4.5 $0.00004 $0.00081

Measured 6d ago against content hash 1f4cb8a78e5d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

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 6d ago.

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.

plugins/decision-making/commands/council.md · 67 lines

How it starts

The opening of the file, as written. The whole thing — 67 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/decision-making:council

When to use

The question is ambiguous, value-laden, or multi-stakeholder and no single perspective can resolve it cleanly. Use when framing matters as much as conclusions. Useful when you suspect the "right answer" depends on whose pain point you're solving.

Cost tier

Medium. 4-6 parallel Lens subagents (sonnet model), one round. See references/cost-tiers.md.

Input

The question + relevant context.

Optional: --lenses=<comma-separated list> to specify lenses explicitly. Useful for recurring decision types where consistency across runs matters.

Workflow

  1. Determine lenses — if --lenses=<list> is provided, use exactly those lenses. Otherwise, the main thread identifies 4-6 distinct lenses relevant to the question and states its selection (with a short justification for each lens) in the Reasoning section of the output. Example lenses for a technical decision:

    • The ops engineer who'll be paged at 3am
    • The new hire reading this in 6 months
    • The security reviewer
    • The staff eng optimizing for system coherence
    • The PM shipping Friday
    • The principal eng thinking 2 years out
  2. Spawn one Lens subagent per lens in parallel — use the Agent tool with subagent_type: lens. Each subagent is given its assigned lens + a short description of what that lens cares about + the question + relevant context. Include this exact prompt template:

    You inhabit this perspective: [LENS NAME]
    
    What this perspective cares about: [WHAT THE LENS CARES ABOUT]
    
    The question: [QUESTION]
    
    Context: [RELEVANT CONTEXT]
    
    Write a short position statement (~150 words) from your assigned
    perspective alone. Do not frame it as "from the X perspective..."
    — write as if you ARE this perspective and it is the only one
    that matters.
    
    Do not meta-comment. Do not try to be balanced. Inhabit the lens.
    
  3. Collect positions — gather all parallel responses. Do not share them between subagents (the Lens agent definition already enforces non-cross-contamination, but the main thread should also avoid accidentally passing one lens's output to another).

Read the full file on GitHub · 67 lines

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.

  1. 6d ago First seen · 67 lines · 41 tokens per session scan A 1f4cb8a78e5d

Subscribe to this mod's changes

council is a command published in the GitHub repository aaronbassett/agent-foundry (4 stars, last pushed 21d ago), licensed MIT. It adds 41 tokens to every session and 811 once invoked, about $0.0002 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 2026-08-31.