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Llm Council

  • 67 installs
  • 2.8k repo stars
  • Updated August 3, 2026
  • rohitg00/pro-workflow

Helps with ai & agent building tasks during AI-assisted development.

About

llm-council is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted coding.

  • llm-council
  • AI & Agent Building
  • AI-coding skill

Llm Council by the numbers

  • 67 all-time installs (skills.sh)
  • +12 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Ranked #5,935 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/rohitg00/pro-workflow --skill llm-council

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Listed on Skillselion
Installs67
repo stars2.8k
Last updatedAugust 3, 2026
Repositoryrohitg00/pro-workflow

What it does

Helps with ai & agent building tasks during AI-assisted development.

Files

SKILL.mdMarkdownGitHub ↗

LLM Council

Karpathy's LLM Council pattern, provider-agnostic. dair-academy's version hardcoded Fireworks; ours reads any OpenAI-compatible endpoint via env.

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

Provider chosen via env. First-match wins:

Env varProviderDefault base URL
ANTHROPIC_API_KEYAnthropichttps://api.anthropic.com
OPENAI_API_KEYOpenAIhttps://api.openai.com/v1
OPENROUTER_API_KEYOpenRouterhttps://openrouter.ai/api/v1
FIREWORKS_API_KEYFireworkshttps://api.fireworks.ai/inference/v1
LLM_COUNCIL_BASE_URL + LLM_COUNCIL_API_KEYCustom 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

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.

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