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 proof-derivation-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/proof-derivation-forensics)<a href="https://agentmods.dev/skills/wanshuiyin/anti-autoresearch/proof-derivation-forensics"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/proof-derivation-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/proof-derivation-forensics"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/proof-derivation-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 234 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 342 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 | $0.00222 | $0.13881 |
| Opus 5.5 | $0.00089 | $0.05552 |
| Sonnet 5.5 | $0.00044 | $0.02776 |
| Haiku 4.5 | $0.00022 | $0.01388 |
Grade A, and why
proof-derivation-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:
- proof-derivation-forensics — 98% identical, 392 lines differ
How it starts
The opening of the file, as written. The whole thing — 822 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proof & Derivation Forensics — does the written proof hold?
Audit family G (proof & derivation integrity) for: $ARGUMENTS (requires
claims.json from /evidence-ledger). A fresh cross-model reviewer reads each
theorem/proof and proposes span-anchored findings; this skill writes
proof-derivation-forensics.findings.json. The deterministic adjudicator — not this
skill — computes the 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, not with the clock. Schedule the external wait that precedes it — ledger built → audit once. (Mirrors ARIS's external-cadence doctrine.)
Broken math is the single most-cited "obviously machine-written" tell in real reviews ("过不去的步骤用文字糊弄", "车轱辘话复述当证明", "关键公式符号用反"). Unlike the surface signals of family F, family-G flaws are substantive and can be critical: a theorem whose proof is circular, skips a load-bearing obligation, or takes an invalid step does not support its claim. And — crucially — proof validity is decidable from the written proof: we never need the code or results, so family G is verdict-bearing at L1 (the LaTeX source) and can still reach HARD_FLAGS with no repo — but needs that source, because PDF-extracted math is unreliable; at an L0 (PDF-only) run a family-G flaw surfaces as
infoonly. Adapted from ARISproof-checker(per-obligation ledger + 20-category taxonomy + counterexample red team) andformula-derivation(identity/proposition/approximation/interpretation step typing), reframed from "fix my own proof" to "audit a third party's proof, detect-only." There is no fixing here and no authorship verdict — only "the step shown does not hold," with the exact line quoted.
Why this exists
An autoresearch pipeline (or a rushed human) writes a theorem statement, then a proof, then an abstract that advertises the theorem — in separate passes, never reconciled at the level of the argument. The result is a proof that does not establish its own 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.
- 1mo ago First seen · 822 lines · 222 tokens per session scan A ab47d5d0483c
proof-derivation-forensics is a skill published in the GitHub repository wanshuiyin/Anti-Autoresearch (172 stars, last pushed 10d ago), licensed MIT. It adds 222 tokens to every session and 13,881 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 2026-08-30.
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