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 wanshuiyin/Anti-Autoresearch --skill eval-design-forensicsgit clone --depth 1 https://github.com/wanshuiyin/Anti-AutoresearchWrote 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/wanshuiyin/anti-autoresearch/eval-design-forensics)<a href="https://agentmods.dev/skills/wanshuiyin/anti-autoresearch/eval-design-forensics"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/eval-design-forensics/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/wanshuiyin/anti-autoresearch/eval-design-forensics"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/eval-design-forensics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 372 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
- high Anti-Refusal · line 479 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00458 | $0.16764 |
| Opus 5.5 | $0.00183 | $0.06706 |
| Sonnet 5.5 | $0.00092 | $0.03353 |
| Haiku 4.5 | $0.00046 | $0.01676 |
Grade A, and why
eval-design-forensics 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- eval-design-forensics — 94% identical, 507 lines differ
How it starts
The opening of the file, as written. The whole thing — 946 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Eval-Design Forensics — does the evaluation measure what the paper claims?
Audit evaluation-design and reporting validity for: $ARGUMENTS (requires
claims.json from /evidence-ledger). Emit span-anchored
eval-design-forensics.findings.json. This skill computes no verdict.
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It is verdict-bearing input — it proposes the findings the deterministic adjudicator turns into the report. Re-firing it on a wall-clock timer adds no signal: its output changes only when the paper / ledger changes (or a repo arrives, raising the observability level), not with the clock. Schedule the external wait that precedes it — ledger built (or artifacts released → L2) → audit once. (Mirrors ARIS's external-cadence doctrine.)
Adapted from the ML-evaluation-methodology literature — the leakage taxonomy of Kapoor & Narayanan (2023), the LLM-as-judge validity work (MT-Bench self-enhancement, self-preference, position bias), and the "Show Your Work" / reproducibility-checklist reporting norms — reframed to audit a third party's evaluation. A favourite autoresearch shortcut is to report a number that is arithmetically self-consistent (family A), runs real code against a real ground truth (family D), and still does not measure what it claims: the protocol leaks, the load-bearing metric is a conflicted/unvalidated LLM judge, or the reporting quietly drops a declared condition. This skill is the constraint that asks "is this a valid measurement of the claim?", pointed at a submission, and it stays honest — leakage and under-reporting are usually honest methodological errors, so every finding is a discrepancy to clarify, never an accusation.
Why this exists
An optimizing pipeline (or rushed human) treats the evaluation as a number to make go up, not a measurement to keep valid. The repeatable failure modes — distinct from "is the number real?" (family D) — are:
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 · 946 lines · 458 tokens per session scan A 8c20d1e39a5e
eval-design-forensics is a skill published in the GitHub repository wanshuiyin/Anti-Autoresearch (171 stars, last pushed 9d ago), licensed MIT. It adds 458 tokens to every session and 16,764 once invoked, about $0.0018 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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