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 Avyayalaya/agent-council --skill evidence-calibration-reviewgit clone --depth 1 https://github.com/Avyayalaya/agent-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/avyayalaya/agent-council/evidence-calibration-review)<a href="https://agentmods.dev/skills/avyayalaya/agent-council/evidence-calibration-review"><img src="https://agentmods.dev/badge/skills/avyayalaya/agent-council/evidence-calibration-review/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/avyayalaya/agent-council/evidence-calibration-review"><img src="https://agentmods.dev/badge/skills/avyayalaya/agent-council/evidence-calibration-review.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 | $0.00129 | $0.03965 |
| Opus 5 | $0.00064 | $0.01982 |
| Sonnet 5 | $0.00026 | $0.00793 |
| Haiku 4.5 | $0.00013 | $0.00396 |
Grade A, and why
evidence-calibration-review 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 12d 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 — 259 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Run a per-claim evidence-tier audit on a text artifact before it ships. The Evidence & Calibration role reads the artifact claim by claim and asks one question per claim: what tier of evidence supports it, and is the artifact's stated confidence consistent with that tier?
A claim asserted with high confidence on Tier 6 (inferred) evidence is a calibration failure. A claim hedged with "perhaps" when the evidence is Tier 1 (primary source, verified) is also a calibration failure — under-claiming is its own honesty failure. The skill catches both directions.
This is the boring and the load-bearing role on the panel. "Where is the source for X?" is the question that ends careers. Evidence & Calibration surfaces every unsourced claim before it ships.
The skill encodes the Evidence & Calibration role from the agent-council 5-deliberator quality gate. Use standalone for fast evidence audit, or compose with the other 4 deliberator skills for fuller coverage.
When to Use / When NOT to Use
Use this skill when:
- A claim-dense artifact (analysis, memo, public pitch) is about to ship and you want every load-bearing claim tiered
- You suspect over-claiming (high confidence on weak evidence) or under-claiming (hedging what is actually verified) and want both directions surfaced
- A piece relies on attributions ("X said Y" / "Microsoft did Z") and you want each verified or hedged appropriately
- You need to catch P11 (citation-as-decoration), P17 (pile-of-anecdotes-as-evidence), or P54 (unverifiable-single-source) patterns explicitly
- You are running a multi-deliberator review and need the Evidence & Calibration seat filled
Do NOT use this skill when:
- You need a structural critique (load-bearing claims, counter-positions) — use
skeptic-reviewinstead - You need a voice critique (banned patterns, register check, CXO test) — use
voice-identity-reviewinstead - You need a strategic-fit check (goal alignment, opportunity cost) — use
strategy-stakes-reviewinstead - The artifact has no factual claims (a pure brainstorming note or speculative framing)
- You want the operator's own source-finding work done for them — this skill names the gap; the operator either finds the source or weakens the claim
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.
- 12d ago First seen · 259 lines · 129 tokens per session scan A ed5dab580a87
evidence-calibration-review is a skill published in the GitHub repository Avyayalaya/agent-council (10 stars, last pushed 1mo ago), licensed MIT. It adds 129 tokens to every session and 3,965 once invoked, about $0.0006 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 2026-08-31.
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