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 | $0.00111 | $0.06940 |
| Sonnet 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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- proof-derivation-forensics — 98% identical, 47 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.
- 12d 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 (153 stars, last pushed 3d ago), licensed MIT. It adds 222 tokens to every session and 13,881 once invoked, about $0.0011 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.
Other skills, from other repositories
research-sop
Run an end-to-end, auditable research workflow from question framing through literature, competing hypotheses, experiment selection, implementation, verification, and claim handoff. Use whenever the user asks to investigate, compare, test, validate, or establish an empirical research claim, including when they do not…
preregister
Lock a confirmatory falsification target and its fixed multiple-comparison family before observing the confirmatory result. Use before promoting an exploratory finding to a main claim or whenever several related hypotheses need Bonferroni control. Records metric, threshold, family id/size, correction, and seed budget.
prove-sop
Build and audit a statistical or mathematical proof from proposition capture through skeleton selection, diagnosis, correction, optional empirical checking, and optional Lean reinsurance. Use when the user asks to prove or rigorously derive a proposition, a graph proposition lacks a verified proof, or a reviewer…
proof-checker
A rigorous proof-checking and repair skill for LaTeX mathematics. LaTeX is a text format commonly used to write mathematical documents.
paper-illustration
A tool for creating academic-paper illustrations such as architecture diagrams and method figures with Gemini image generation and Claude-guided revisions.
paper-navigator
Find and read academic papers: disambiguate queries, discover papers (search, citation traversal, recommendations, arXiv monitoring, trending, GitHub search), evaluate (TLDR, citations, code, SOTA), and read with structured analysis (3-level strategy). Use when: finding papers, reading a paper, related work, citation…