Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/graycodeai/starling/llm-councilnpx skills add GrayCodeAI/starling --skill llm-councilgit clone --depth 1 https://github.com/GrayCodeAI/starlingWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00013 | $0.01109 |
| Opus 5 | $0.00006 | $0.00554 |
| Sonnet 5 | $0.00003 | $0.00222 |
| Haiku 4.5 | $0.00001 | $0.00111 |
Grade A, and why
llm-council scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to llm-council — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Council Skill
Quick start
- Always check for an existing agents config file first (
$XDG_CONFIG_HOME/llm-council/agents.jsonor~/.config/llm-council/agents.json). If none exists, tell the user to run./setup.shto configure or update agents. - The orchestrator must always ask thorough intake questions first, then generates prompts so planners do not ask questions.
- Even if the initial prompt is strong, ask at least a few clarifying questions about ambiguities, constraints, and success criteria.
- Tell the user that answering intake questions is optional, but more detail improves the quality of the final plan.
- Use
python3 scripts/llm_council.py run --spec /path/to/spec.jsonto run the council. - Plans are produced as Markdown files for auditability.
- Run artifacts are saved under
./llm-council/runs/<timestamp>relative to the current working directory. - Configure defaults interactively with
python3 scripts/llm_council.py configure(writes$XDG_CONFIG_HOME/llm-council/agents.jsonor~/.config/llm-council/agents.json).
Workflow
- Load the task spec, and explore the codebase you are in to get a strong sense of the product.
- Always ask thorough intake questions to build a clear task brief. Clarify any ambiguities, constraints, and success criteria. Remind the user that answers are optional but improve plan quality.
- Build planner prompts (Markdown template) and launch the configured planner agents in parallel background shells.
- Collect outputs, validate Markdown structure, and retry up to 2 times on failure. If any agents fails, yield and alert the user to fix the issue.
- Anonymize plan contents and randomize order before judging.
- Run the judge with the rubric and Markdown template, then save
judge.mdandfinal-plan.md. - Session management: DO NOT yield/finish the response until a full 30-minute timer has completed and judge +
final-plan.mdare 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.- Note on Session Management: Plans can take quite some time to build, do not panic if it seems stuck. You do not need to poll every few seconds. Once every 20-30 seconds is sufficient. Continue to allow them as much time as needed up to the 30-minute mark.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 66 lines · 13 tokens per session scan A d9edc75d8c89
llm-council is a skill published in the GitHub repository GrayCodeAI/starling (2 stars, last pushed 3d ago), licensed MIT. It adds 13 tokens to every session and 1,109 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to llm-council, differing in 9 lines, and is treated as a copy.
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