Borrowing it
Nothing to install: this file belongs to warpdotdev/common-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/warpdotdev/common-skills/main/.agents/skills/council/SKILL.mdgit clone --depth 1 https://github.com/warpdotdev/common-skillsWrote 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/warpdotdev/common-skills/council)<a href="https://agentmods.dev/skills/warpdotdev/common-skills/council"><img src="https://agentmods.dev/badge/skills/warpdotdev/common-skills/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/warpdotdev/common-skills/council"><img src="https://agentmods.dev/badge/skills/warpdotdev/common-skills/council.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00070 | $0.01387 |
| Opus 5.5 | $0.00028 | $0.00555 |
| Sonnet 5.5 | $0.00014 | $0.00277 |
| Haiku 4.5 | $0.00007 | $0.00139 |
Grade A, and why
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 1mo 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Council
Use this skill to coordinate multiple subagents investigating the same question, with different models first and different assigned perspectives second, then synthesize their reports into one recommendation.
This skill is best for judgment-heavy tasks: architecture tradeoffs, risky bug fixes, code review red-teaming, rollout decisions, incident analysis, and “is this alternative worth pursuing?” questions.
Workflow
1. Frame the council question
State the decision the council should answer in one sentence. Identify:
- the competing options or hypothesis under review;
- the codebase, branch, PR, issue, design, or artifact to inspect;
- whether agents should be read-only or may make code changes;
- the final decision criteria, such as correctness, risk, implementation cost, testability, rollout safety, or product behavior.
If the user’s request is ambiguous, ask only the minimum clarification needed. Otherwise choose sensible defaults and proceed.
2. Choose council members
Prioritize model diversity. A council should not default to three agents on the same model with different angles; use that only when the available launch configuration cannot provide multiple useful models, or when the user explicitly asks for one model. If model diversity is unavailable, say so briefly before falling back to perspective-only diversity.
Preferred default roster for a three-member council:
- Opus 4.7 or the strongest available Claude/Opus reasoning model: architecture, correctness, and edge-case analysis.
- GPT 5.5 or the strongest available GPT/Codex model: implementation-grounded review, feasibility, and test strategy.
- An open-source model such as Kimi 2.6, GLM 5.1, or the strongest available OSS/local model: contrarian critique, hidden assumptions, and alternative framing.
If one of these exact models is unavailable in the active harness, use the closest available model from that family and note the substitution. If no open-source model is available, use a third distinct frontier model if possible; otherwise use the strongest remaining model with a deliberately adversarial or specialist angle.
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.
- 1mo ago First seen · 131 lines · 70 tokens per session scan A 0580180ec5b0
council is a skill published in the GitHub repository warpdotdev/common-skills (636 stars, last pushed today), licensed MIT. It adds 70 tokens to every session and 1,387 once invoked, about $0.0003 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 2026-08-30.
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