llm-council

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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.

10installs

Sourcerohitg00/pro-workflow

Added on2026-10-25

NPX Install

npx skill4agent add rohitg00/pro-workflow llm-council

SKILL.md Content

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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

Independent: each model answers in parallel
Ranking: each model ranks anonymized peer responses
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

Never skip the ranking phase. It's the core of the council pattern.
Save raw responses to disk verbatim. No summarization in storage.
Anonymize responses for ranking — models see
```
Response A/B/C/...
```
, not peer names.
The chairman sees both real names AND rankings.
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

.