Multi-agent planning council that orchestrates independent implementation plans, anonymizes them, then merges into one final plan.
Works with
Supports configurable planner agents (Codex, Claude, Gemini, OpenCode, or custom CLI commands) running in parallel, with optional judge override
Conducts structured intake questioning before plan generation to clarify ambiguities, constraints, and success criteria
Produces validated Markdown outputs with automatic retry logic (up to 2 attempts) and failur
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionllm-councilExecute the skills CLI command in your project's root directory to begin installation:
Fetches llm-council from am-will/codex-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate llm-council. Access via /llm-council in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
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$XDG_CONFIG_HOME/llm-council/agents.json or ~/.config/llm-council/agents.json). If none exists, tell the user to run ./setup.sh to configure or update agents.python3 scripts/llm_council.py run --spec /path/to/spec.json to run the council../llm-council/runs/<timestamp> relative to the current working directory.python3 scripts/llm_council.py configure (writes $XDG_CONFIG_HOME/llm-council/agents.json or ~/.config/llm-council/agents.json).judge.md and final-plan.md.final-plan.md are confirmed saved; keep the session open during that interval to avoid closing the interface. If you yield while the Council is running, the session will be terminated and you will FAIL to complete the task. The user will escape out when they are ready or after the 30 minutes have elapsed.
Use agents.planners to define any number of planning agents, and optionally agents.judge to override the judge.
If agents.judge is omitted, the first planner config is reused as the judge.
If agents is omitted in the task spec, the CLI will use the user config file when present, otherwise it falls back to the default council.
Example with multiple OpenCode models:
{
"task": "Describe the change request here.",
"agents": {
"planners": [
{ "name": "codex", "kind": "codex", "model": "gpt-5.2-codex", "reasoning_effort": "xhigh" },
{ "name": "claude-opus", "kind": "claude", "model": "opus" },
{ "name": "opencode-claude", "kind": "opencode", "model": "anthropic/claude-sonnet-4-5" },
{ "name": "opencode-gpt", "kind": "opencode", "model": "openai/gpt-4.1" }
],
"judge": { "name": "codex-judge", "kind": "codex", "model": "gpt-5.2-codex" }
}
}
Custom commands (stdin prompt) can be used by setting kind to custom and providing command and prompt_mode (stdin or arg).
Use extra_args to append additional CLI flags for any agent.
See references/task-spec.example.json for a full copy/paste example.
references/architecture.mdreferences/prompts.mdreferences/templates/*.mdreferences/cli-notes.mdfinal-plan.md are saved; keep the session open during that interval to avoid closing the interface.Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
am-will/codex-skills
davila7/claude-code-templates
intellectronica/agent-skills
sickn33/antigravity-awesome-skills
myzy-ai/dokie-ai-ppt
sickn33/antigravity-awesome-skills
We added llm-council from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Solid pick for teams standardizing on skills: llm-council is focused, and the summary matches what you get after install.
llm-council reduced setup friction for our internal harness; good balance of opinion and flexibility.
I recommend llm-council for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for llm-council matched our evaluation — installs cleanly and behaves as described in the markdown.
llm-council reduced setup friction for our internal harness; good balance of opinion and flexibility.
llm-council has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in llm-council — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: llm-council is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added llm-council from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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