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 skills add YonasValentin/llm-council --skill llm-councilgit clone --depth 1 https://github.com/YonasValentin/llm-councilWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yonasvalentin/llm-council/llm-council)<a href="https://agentmods.dev/skills/yonasvalentin/llm-council/llm-council"><img src="https://agentmods.dev/badge/skills/yonasvalentin/llm-council/llm-council/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yonasvalentin/llm-council/llm-council"><img src="https://agentmods.dev/badge/skills/yonasvalentin/llm-council/llm-council.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00225 | $0.01933 |
| Opus 5.5 | $0.00090 | $0.00773 |
| Sonnet 5.5 | $0.00045 | $0.00387 |
| Haiku 4.5 | $0.00022 | $0.00193 |
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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Council
One AI gives you one answer. That answer feels smart because it was shaped by how you asked. Ask the same question with different framing and you get a different answer, often opposite, equally confident.
The council breaks that loop. Five advisors with different thinking styles answer your question independently. They peer-review each other anonymously. A chairman synthesizes everything into a verdict with a clear recommendation and one concrete next step.
This skill runs the whole protocol inside a single Claude Code session.
When NOT to convene
If the question fits one of these, just answer directly instead of convening:
- Factual lookup (one correct answer exists).
- Creation task (write, summarize, translate, refactor).
- Trivial choice (e.g. "should I use markdown or plaintext for this note").
- The user has already decided and wants validation. Warn them the council may dissent, then proceed only if they confirm.
If you are unsure, ask the user once: "Is this a real tradeoff you want pressure-tested, or do you want a direct answer?"
The seven steps
This workflow is rigid. Execute in order. Do not skip, merge, or parallelize across steps.
Step 1. Scan workspace for context (30 seconds max)
Before framing the question, surface any workspace context the advisors will need. Use Glob and Read. Do not spend more than 30 seconds on this step.
Look for:
CLAUDE.md,AGENTS.md,GEMINI.md(user or project instructions).memory/**/*.md(user profile, past decisions, voice).- Files the user referenced by name or @-mention.
- Recent
council/**/council-report.htmlartifacts so you avoid re-counciling ground already covered.
Pick at most 2 to 3 files that would move advisors from generic to grounded. Skip entirely if nothing relevant is present. Never read the whole workspace indiscriminately.
Step 2. Frame the question
Restate the user's raw question as a single neutral prompt with four parts:
- Core decision, stripped of emotional lean.
- User-provided context: facts, numbers, constraints.
- Workspace context: the 2 to 3 facts from Step 1 that matter here.
- Stakes: why being wrong is expensive.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 2mo ago First seen · 169 lines · 225 tokens per session scan A 40406f06f74d
llm-council is a skill published in the GitHub repository YonasValentin/llm-council (6 stars, last pushed 6mo ago), licensed MIT. It adds 225 tokens to every session and 1,933 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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