SkillAgentSearch skills...

llm-council

Provider-agnostic multi-LLM deliberation. Three phases — independent responses, cross-model anonymized ranking, chairman synthesis. Provider config from env (OPENAI/ANTHROPIC/FIREWORKS/OPENROUTER/custom OpenAI-compatible base URL). Persists transcript to a wiki page when --wiki <slug> is passed

Install / Use

npx skills add rohitg00/pro-workflow --skill llm-council

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

88/100

Supported Platforms

Zed

Our assessment of llm-council

llm-council scores 88/100 on our quality scale, 399th of 970 AI & Machine Learning skills we index (top 42%).

Its SKILL.md is 4.3 KB long, well organised into 9 sections with 3 code examples: a solid amount of guidance for an agent.

With 2,876 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
26/30
Structure
18/20
Description
15/15
Adoption
15/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 8 days ago, so llm-council is actively maintained.
  • No license is declared. By default that means all rights are reserved: you can read it, but reusing or redistributing it is not clearly permitted. Ask the author before building on it commercially.
  • Its trust signals score 88/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

llm-council compared with similar skills

All 4 of these similar skills score higher than llm-council; compare them before choosing.

SkillScoreStarsUpdatedFormat
llm-council (this skill)by rohitg00882.9k8d agoSKILL.md
claude-memby thedotmack10095.2ktodayCLAUDE.md
Understand-Anythingby Egonex-AI10085.0ktodayCLAUDE.md
headroomby headroomlabs-ai10074.3ktodayCLAUDE.md
CowAgentby zhayujie10047.2ktodayCLAUDE.md

Frequently asked questions

How do I install llm-council?
Run npx skills add rohitg00/pro-workflow --skill llm-council. The install tabs above show the steps for each supported agent.
Which AI agents does llm-council work with?
It is written for Zed, as a SKILL.md file. Other agents that read the same format can often use it too.
Is llm-council safe to use?
It declares no license and scores 88/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is llm-council still maintained?
The repository was last updated 8 days ago, so llm-council is actively maintained.

name: llm-council description: Provider-agnostic multi-LLM deliberation. Three phases — independent responses, cross-model anonymized ranking, chairman synthesis. Credentials come from explicit plugin configuration or pro-workflow-specific CLI variables. Persists transcript to a wiki page when --wiki <slug> is passed. Use when the user wants multiple AI perspectives, consensus-building, or the "LLM Council" approach for high-stakes reviews, plan critique, or contested learning rules. user-invocable: true

LLM Council

Karpathy's LLM Council pattern with Anthropic and OpenAI-compatible providers.

When to use

  • High-stakes plan review (/plan crosses N-file threshold)
  • Conflicting learning-rules → re-resolve via vote
  • User invokes /council "<query>" or /wiki council
  • Architecture decisions where you want multiple viewpoints captured
  • Persisting deliberation as a wiki page for future reference

Three phases

  1. Independent: each model answers in parallel
  2. Ranking: each model ranks anonymized peer responses
  3. Synthesis: chairman model reads all responses + rankings → final answer

Provider config

Configure optional keys in the plugin configuration dialog. Use the providers MCP server's run_provider_task tool with task: "council" and args: ["run", "<query>", "--provider", "openai"]. For provider status, pass args: ["providers"]. Never request keys in chat or read them from existing credentials or shell configuration.

For standalone CLI use, explicitly set a pro-workflow-specific variable below. The first configured provider is the default; --provider selects one explicitly. See provider configuration.

| Standalone CLI variable | Provider | Default base URL | |---------|----------|------------------| | PRO_WORKFLOW_ANTHROPIC_API_KEY | Anthropic | https://api.anthropic.com | | PRO_WORKFLOW_OPENAI_API_KEY | OpenAI | https://api.openai.com/v1 | | PRO_WORKFLOW_OPENROUTER_API_KEY | OpenRouter | https://openrouter.ai/api/v1 | | PRO_WORKFLOW_FIREWORKS_API_KEY | Fireworks | https://api.fireworks.ai/inference/v1 | | LLM_COUNCIL_BASE_URL + PRO_WORKFLOW_LLM_COUNCIL_API_KEY | Custom OpenAI-compat | (user-supplied) |

Override per-run with --provider openai|anthropic|openrouter|fireworks|custom.

Default model rosters per provider live in scripts/council.js and can be overridden via --models CSV and --chairman <id>.

Commands

In a plugin session, pass these runner arguments through run_provider_task. The direct commands below are for standalone CLI installations with explicit credentials.

node $SKILL_ROOT/scripts/council.js run "<query>" [--models id1,id2,id3] [--chairman id] [--provider <name>] [--wiki <slug>]
node $SKILL_ROOT/scripts/council.js providers
node $SKILL_ROOT/scripts/council.js show <session-id>

--wiki <slug> writes the full transcript to <wiki>/derived/council/<session-id>.md and registers it via wiki-cli.js page so it shows in FTS5 search.

Output

Each session writes:

~/.pro-workflow/council/<session-id>/
├── config.json           # query, models, chairman, provider
├── phase1_responses.json # raw API responses per model
├── phase2_rankings.json  # anonymized ranking outputs
├── phase3_synthesis.txt  # chairman's final answer
└── final_output.md       # human-readable bundle

Console prints the markdown bundle. Pipe to pbcopy / tee as needed.

Hard rules

  1. Never skip the ranking phase. It's the core of the council pattern.
  2. Save raw responses to disk verbatim. No summarization in storage.
  3. Anonymize responses for ranking — models see Response A/B/C/..., not peer names.
  4. The chairman sees both real names AND rankings.
  5. Display all three phases to the user. No phase elision.

Cost awareness

The script logs per-call latency + tokens on supported providers. Multiply by your provider rate to estimate. Council cost grows linearly with len(models)^2 (each model ranks all others) plus the chairman.

Default council size: 3-5 models. More models = exponentially more ranking calls.

Use with wiki

/wiki council agent-memory "should we adopt episodic memory in our agents?"

Loads agent-memory wiki context as system prompt prefix, runs council, persists transcript as wiki/derived/council/<id>.md. The transcript becomes searchable via /wiki ask.

Related Skills

View on GitHub
GitHub Stars2.9k
CategoryAI
Updated8d ago
Forks289

Languages

JavaScript

Trust signals

88/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 medium