Agent skill

LLM Council

Multi-LLM collaborative brainstorming and planning. An agent skill from gcpdev/llm-council-skill.

Install LLM Council

skills CLI

$ npx skills add gcpdev/llm-council-skill --skill llm-council -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install gcpdev/llm-council-skill llm-council --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/gcpdev/llm-council-skill.git skills-src && mkdir -p .claude/skills && cp -r skills-src/llm-council .claude/skills/llm-council && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "llm-council" agent skill from https://github.com/gcpdev/llm-council-skill/tree/main/llm-council into .claude/skills/llm-council/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-council", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/gcpdev/llm-council-skill/tree/main/llm-council

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add gcpdev/llm-council-skill --skill llm-council -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install gcpdev/llm-council-skill llm-council --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/gcpdev/llm-council-skill.git skills-src && mkdir -p .agents/skills && cp -r skills-src/llm-council .agents/skills/llm-council && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "llm-council" agent skill from https://github.com/gcpdev/llm-council-skill/tree/main/llm-council into .agents/skills/llm-council/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-council", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add gcpdev/llm-council-skill --skill llm-council -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install gcpdev/llm-council-skill llm-council --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/gcpdev/llm-council-skill.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/llm-council .cursor/skills/llm-council && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "llm-council" agent skill from https://github.com/gcpdev/llm-council-skill/tree/main/llm-council into .cursor/skills/llm-council/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-council", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/gcpdev/llm-council-skill.git --path llm-council

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add gcpdev/llm-council-skill --skill llm-council -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install gcpdev/llm-council-skill llm-council --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/gcpdev/llm-council-skill.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/llm-council .gemini/skills/llm-council && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "llm-council" agent skill from https://github.com/gcpdev/llm-council-skill/tree/main/llm-council into .gemini/skills/llm-council/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-council", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install gcpdev/llm-council-skill llm-council

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add gcpdev/llm-council-skill --skill llm-council -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/gcpdev/llm-council-skill.git skills-src && mkdir -p .github/skills && cp -r skills-src/llm-council .github/skills/llm-council && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "llm-council" agent skill from https://github.com/gcpdev/llm-council-skill/tree/main/llm-council into .github/skills/llm-council/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-council", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add gcpdev/llm-council-skill --skill llm-council -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install gcpdev/llm-council-skill llm-council --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/gcpdev/llm-council-skill.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/llm-council .opencode/skills/llm-council && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "llm-council" agent skill from https://github.com/gcpdev/llm-council-skill/tree/main/llm-council into .opencode/skills/llm-council/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "llm-council", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: llm-council
GitHub stars: 461
Used in: 1 other repo
Token cost: ~1k tokens
SKILL.md length: 464 words
Files: 4 (incl. scripts, references)
Skills in repo: 1
Repo updated: Jan 8, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

Multi-LLM collaborative brainstorming and planning. An agent skill from gcpdev/llm-council-skill.

Works in 4 steps: Query external LLMs: Run… → Analyze responses: Review what each… → Synthesize plan: Create an… → …
User explicitly requests consultation with multiple AI models (ChatGPT
SKILL.md covers Workflow, Setup Requirements, Usage Example and Output Format, plus 1 more section
Runs Python scripts from its folder; calls python3; needs OPENAI_API_KEY and GEMINI_API_KEY

What it does

LLM Council is an agent skill from gcpdev/llm-council-skill. Multi-LLM collaborative brainstorming and planning. Use when user explicitly requests consultation with multiple AI models (ChatGPT, Gemini, other LLMs) before presenting an implementation plan, or asks to "consult the council", "ask other models", or "get perspectives from other AIs". Queries external LLM APIs, synthesizes their perspectives, and presents an adapted implementation plan.

Its SKILL.md is about 1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/SETUP.md` and `scripts/query_llms.py`).

It sits in Agent Workflows, covering Planning, Brainstorming and LLM API integration. It works with OpenAI. The repository describes itself as: A skill to claude code that enables brainstorming with other LLMs (ChatGPT, Gemini) before presenting the implementation plan to the user. The licence is MIT.

When your agent uses it

User explicitly requests consultation with multiple AI models (ChatGPT
Other LLMs) before presenting an implementation plan
Asks to consult the council
Ask other models

Example prompts

“consult the council”
“ask other models”
“get perspectives from other AIs”
“/llm-council”

Requirements

Python 3
A credential in OPENAI_API_KEY
A credential in GEMINI_API_KEY

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

Query external LLMs: Run scripts/query_llms.py with the user's prompt to get perspectives from both ChatGPT and Gemini
Analyze responses: Review what each model suggests, identifying valuable insights, alternative approaches, and potential concerns
Synthesize plan: Create an implementation plan that incorporates the best ideas from all three models (Claude's own analysis + ChatGPT +…
Present to user: Show the final plan along with a brief summary of key contributions from each model

What it can do on your machine

Read from SKILL.md and the folder at commit 0f95431. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
- python3
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
- OPENAI_API_KEY
- GEMINI_API_KEY
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

LLM Council loads about 1k tokens when it runs, and up to ~1.8k if it reads all its reference files. Until then it costs about 101 tokens; SKILL.md has 464 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~101
When it runs · the whole SKILL.md, loaded when a task matches: ~1k
With references · SKILL.md plus every file in references/, read only if the agent opens them: ~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

NoteMentions a .env fileSKILL.md:27

ptional model configuration stored in a `.env` file in the working directory:

NoteMentions a .env fileSKILL.md:60

If the `.env` file doesn't exist or keys are missing, inform the user and provide setup instructions.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from gcpdev/llm-council-skill at commit 0f95431, republished under its MIT licence (© gcpdev). 464 words, ~1,046 tokens.

Download SKILL.mdSave it as .claude/skills/llm-council/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.

name: llm-council
description: Multi-LLM collaborative brainstorming and planning. Use when user explicitly requests consultation with multiple AI models (ChatGPT, Gemini, other LLMs) before presenting an implementation plan, or asks to "consult the council", "ask other models", or "get perspectives from other AIs". Queries external LLM APIs, synthesizes their perspectives, and presents an adapted implementation plan.

LLM Council

Consult multiple AI models (ChatGPT and Gemini) for their perspectives before presenting implementation plans to users.

Workflow

When user requests consultation with other AI models, use phrases like:

"Consult with ChatGPT and Gemini about..."
"Ask other AI models what they think about..."
"Get perspectives from the council on..."
"Consult the LLM council: [your question]"

Process:

Query external LLMs: Run scripts/query_llms.py with the user's prompt to get perspectives from both ChatGPT and Gemini
Analyze responses: Review what each model suggests, identifying valuable insights, alternative approaches, and potential concerns
Synthesize plan: Create an implementation plan that incorporates the best ideas from all three models (Claude's own analysis + ChatGPT + Gemini)
Present to user: Show the final plan along with a brief summary of key contributions from each model

Setup Requirements

The skill requires API keys and optional model configuration stored in a .env file in the working directory:

OPENAI_API_KEY=sk-...
GEMINI_API_KEY=...

# Optional: Specify which models to use (defaults shown below)
OPENAI_MODEL=gpt-5-nano
GEMINI_MODEL=gemini-3-flash-preview

Default Models:

ChatGPT: gpt-5-nano (fastest, most cost-efficient - $0.05/1M input, $0.40/1M output)
Gemini: gemini-3-flash-preview (balanced speed and intelligence)

Upgrade Options for Better Collaboration:

OpenAI models (ordered by capability and cost):

gpt-5-nano - Fastest, most cost-efficient ($0.05/1M in, $0.40/1M out) - DEFAULT
gpt-5-mini - Faster, cost-efficient for well-defined tasks ($0.25/1M in, $2.00/1M out)
gpt-5.2 - Best for coding and agentic tasks ($1.75/1M in, $14.00/1M out)
gpt-5.2-pro - Smarter, more precise for complex problems ($21.00/1M in, $168.00/1M out)

All models support reasoning tokens, 400K context window, and image input.

Gemini models (ordered by capability):

gemini-2.5-flash-lite - Ultra-fast, optimized for throughput
gemini-2.5-flash - Best price-performance, large-scale processing
gemini-3-flash-preview - Balanced speed and frontier intelligence (default)
gemini-3-pro-preview - Most intelligent multimodal model, best for complex reasoning

Higher-tier models provide more sophisticated analysis but cost more per API call.

If the .env file doesn't exist or keys are missing, inform the user and provide setup instructions.

Show full SKILL.md (172 more words)Show less

Usage Example

User input: "Consult the council: How should I architect a real-time data pipeline for IoT sensors?"

Claude's process:

Execute: python3 scripts/query_llms.py "How should I architect a real-time data pipeline for IoT sensors?"
Parse JSON responses from ChatGPT and Gemini
Analyze their suggestions (e.g., ChatGPT suggests Kafka, Gemini recommends considering edge computing)
Synthesize final plan incorporating valuable insights from all models
Present the adapted plan to user with attribution

Output Format

Present the final implementation plan naturally, mentioning key insights from other models inline where relevant. For example:

"Based on consultation with ChatGPT and Gemini, here's the recommended architecture:

[Implementation plan with inline references like "ChatGPT highlighted the importance of..." or "Gemini suggested..."]

Key contributions:

ChatGPT: [brief summary]
Gemini: [brief summary]"

Error Handling

If API keys are missing, inform user and provide setup instructions
If an API call fails, note which model's perspective is unavailable and proceed with available responses
If both APIs fail, inform user and offer to provide Claude's own analysis without external consultation

Files

SKILL.md and 3 other files (scripts, references) in llm-council of gcpdev/llm-council-skill.

SKILL.md
.env.template
references/SETUP.md
scripts/query_llms.py

Open the folder on GitHubat commit 0f95431

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in gcpdev/llm-council-skill, which our catalogue first saw on October 7, 2026.

majiayu000/claude-skill-registryskills/ai-llm/llm-council-gcpdev-llm-council-skill-2 skills/ai-llm/llm-council-gcpdev-llm-council-skill-3

Compare with similar skills

LLM Council next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

LLM Council compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
LLM Council this skillgcpdev/llm-council-skill	461	1 repos	~1k	Automated safety check: Notes	MIT	9 mo ago
Codex with ChatGPT Planning LoopXiaoDuoYa/codex-with-chatgpt	7.1k	—	~11k	Automated safety check: Notes	MIT	7 days ago
Brainstorming Before BuildingjnMetaCode/superpowers-zh	8.3k	—	~1.8k	Automated safety check: Pass	MIT	3 days ago
Chatgpt App Builderalpic-ai/skybridge	2.1k	—	~1k	Automated safety check: Pass	MIT	2 days ago
CCG Tri-Model Orchestrationzereight/gitlab-mcp	2k	1 repos	~657	Automated safety check: Pass	MIT	2 days ago
CE BrainstormEveryInc/compound-engineering-plugin	25k	—	~1.9k	Automated safety check: Pass	MIT	today

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Works with

OpenAI

Questions about LLM Council

What does LLM Council do?

Multi-LLM collaborative brainstorming and planning. An agent skill from gcpdev/llm-council-skill. LLM Council is an agent skill from gcpdev/llm-council-skill. Multi-LLM collaborative brainstorming and planning.

When should I use LLM Council?

LLM Council fits situations like: user explicitly requests consultation with multiple AI models (ChatGPT; other LLMs) before presenting an implementation plan; asks to consult the council; ask other models.

How do I install LLM Council in Claude Code?

Run `npx skills add gcpdev/llm-council-skill --skill llm-council -a claude-code`. Or copy the skill folder (llm-council in gcpdev/llm-council-skill) into .claude/skills/llm-council in your project. Claude Code loads it when a task matches its description.

How do I install LLM Council in Codex?

Run `npx skills add gcpdev/llm-council-skill --skill llm-council -a codex`. Or copy the skill folder (llm-council in gcpdev/llm-council-skill) into .agents/skills/llm-council in your project. Codex loads it when a task matches its description.

Can I use LLM Council in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gcpdev/llm-council-skill --skill llm-council -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/llm-council, .gemini/skills/llm-council, .github/skills/llm-council and .opencode/skills/llm-council in your project.

What does LLM Council need to run?

Going by SKILL.md and its folder, LLM Council needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named OPENAI_API_KEY and GEMINI_API_KEY. Our summary lists: Python 3; A credential in OPENAI_API_KEY; A credential in GEMINI_API_KEY.

Does LLM Council access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is LLM Council safe to install?

Our automated static check of SKILL.md found notes only (mentions a .env file), nothing it rates as a warning. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does LLM Council use?

LLM Council is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does LLM Council use?

About 1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 773 tokens, read only when the agent opens those files.

What are the alternatives to LLM Council?

Skills that share tags, products or a category with LLM Council: Codex with ChatGPT Planning Loop (XiaoDuoYa/codex-with-chatgpt, 7.1k stars), Brainstorming Before Building (jnMetaCode/superpowers-zh, 8.3k stars), Chatgpt App Builder (alpic-ai/skybridge, 2.1k stars) and CCG Tri-Model Orchestration (zereight/gitlab-mcp, 2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains LLM Council?

gcpdev (a GitHub user) maintains it in gcpdev/llm-council-skill, which has 461 GitHub stars. The repository was last updated on January 8, 2026.

Source: gcpdev/llm-council-skill on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install LLM Council

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

LLM Council

Workflow

Setup Requirements

Usage Example

Output Format

Error Handling

Files

Used in 1 other repository

Compare with similar skills

Similar skills

Codex with ChatGPT Planning Loop

Brainstorming Before Building

Chatgpt App Builder

CCG Tri-Model Orchestration

CE Brainstorm

MCP App Builder

Related

Questions about LLM Council