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Llm Council

Checked
am-will·vlatest·ai-ml

Provides LLMs guidance and assistance for building AI and machine learning applications.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add am-will/codex-skills --skill llm-council -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

am-will

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Only contacts known or declared hosts: 127.0.0.1.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    127.0.0.1local

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned am-will/codex-skills at 9f954c3b63.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
9f954c3b63
Manifest
skills/llm-council/SKILL.md
Scanned
22 files, 3,056 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The llm-council skill enables AI agents to simulate a group of LLMs collaborating on complex tasks, allowing for consensus-based decision-making and improved accuracy through multiple perspectives. It supports structured debate, voting mechanisms, and role assignment among participating models.

When to use it

  • To resolve ambiguous or conflicting information by leveraging multiple model viewpoints.
  • For high-stakes decisions where a single AI’s judgment may be insufficient.
  • In scenarios requiring ethical review or bias mitigation through diverse perspectives.
  • When simulating collaborative problem-solving in research, strategy planning, or creative tasks.

Key capabilities

  • Multi-model collaboration and debate simulation
  • Role-based assignment for LLM participants
  • Voting and consensus mechanisms
  • Structured discussion frameworks

Example prompts

  • "Simulate a council of three AI models to evaluate the ethical implications of this new technology."
  • "Have your LLM council debate and vote on the best approach to solving this complex problem."
  • "Assign roles (e.g., analyst, ethicist, strategist) to multiple models and have them collaborate on this task."

Tips & gotchas

  • Ensure all participating models are compatible with the skill’s interface.
  • Be mindful of potential biases in model responses; use diverse model architectures where possible for balanced outcomes.