Borrowing it
Nothing to install: this file belongs to arcasilesgroup/ai-engineering. 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/arcasilesgroup/ai-engineering/main/.agents/skills/ai-council/SKILL.mdgit clone --depth 1 https://github.com/arcasilesgroup/ai-engineeringWrote 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/arcasilesgroup/ai-engineering/ai-council)<a href="https://agentmods.dev/skills/arcasilesgroup/ai-engineering/ai-council"><img src="https://agentmods.dev/badge/skills/arcasilesgroup/ai-engineering/ai-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/arcasilesgroup/ai-engineering/ai-council"><img src="https://agentmods.dev/badge/skills/arcasilesgroup/ai-engineering/ai-council.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00182 | $0.01420 |
| Opus 5 | $0.00091 | $0.00710 |
| Sonnet 5 | $0.00036 | $0.00284 |
| Haiku 4.5 | $0.00018 | $0.00142 |
Grade A, and why
ai-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 9d 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 — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
One pass: five lenses, one cross-read, a verdict inside the spec
What it produces
The specification's ## Council section: three machine-read headings, the two counts,
a verdict, a recommendation and one first step. No transcript file and no page beside
it — the section is the record and the record is the artifact.
The lenses, and none of them sees another
Cost, reversibility, the undecidable path, what is taken on trust, the example nobody wrote. Each is a question, not a personality. Each reads the spec and nothing else. Not the plan, not the chat, not another lens's answer.
That rule is not taste. Put one answer in a reader's context and a right answer turns wrong 66.5% of the time, against 10.3% for a plain re-ask (report 003). Nobody was named as its author. So the harm comes from the words being there, not from being told who wrote them.
Reading alone also buys ground. Human reviewers who do not confer raise 14 issues a session against 9 (report 003). And 70% (report 003) of what they find is seen by one reader only.
Every finding carries a command a reader can run to see the gap. One with no command is cut before its section is written, and it is listed under its own heading below so the count can be recomputed rather than believed.
The cross-read, inside the same pass
Each lens then sees the other four answers, relabelled and shuffled, and not its own. It answers two questions. Which of these findings is a false alarm, and what command shows it? And what did all of us miss?
It is never asked which answer is best. Ranking five good answers is the worst case in the measured work: one judge falls from 0.70 to 0.34 moving from pairs to a list (report 003). What the cross-read buys is aim: false alarms fall from 22% to 5.3% (report 003), and no true finding is lost.
A refutation carries a command. The command is run and its output is written down. One with no command is dropped, the same way a finding with none is.
A refuted finding is struck through and kept, with the refuting command beside it. It is never erased: a real gap killed by a good-looking answer must leave more than a number. Where one lens refutes and another agrees, the finding stays under the gaps and is not also listed under the refuted heading — one heading owns one finding, or the two counts double-count what the counter exists to keep honest.
What ships with it
1 file 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.
- 9d ago First seen · 119 lines · 182 tokens per session scan A 605d2710bf4a
ai-council is a skill published in the GitHub repository arcasilesgroup/ai-engineering (54 stars, last pushed yesterday), licensed Apache-2.0. It adds 182 tokens to every session and 1,420 once invoked, about $0.0009 per session on Opus 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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