Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add wenhaochai/claude-plugins/plugin install 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/wenhaochai/claude-plugins/proof-derivation-forensics)<a href="https://agentmods.dev/skills/wenhaochai/claude-plugins/proof-derivation-forensics"><img src="https://agentmods.dev/badge/skills/wenhaochai/claude-plugins/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/wenhaochai/claude-plugins/proof-derivation-forensics"><img src="https://agentmods.dev/badge/skills/wenhaochai/claude-plugins/proof-derivation-forensics.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 | $0.00084 | $0.07754 |
| Opus 5.5 | $0.00034 | $0.03102 |
| Sonnet 5.5 | $0.00017 | $0.01551 |
| Haiku 4.5 | $0.00008 | $0.00775 |
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 24d 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.
This is a copy
98% identical to proof-derivation-forensics — 392 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 470 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: $ARGUMENTS (requires
claims.json from /evidence-ledger). A fresh cross-model reviewer reads each
theorem/proof and proposes span-anchored findings; this skill writes
proof-derivation-forensics.findings.json. The deterministic adjudicator — not this
skill — computes the verdict.
🔒 Run once per input change; never wrap in
/loop,/scheduleorCronCreate(${CLAUDE_PLUGIN_ROOT}/support/references/run-cadence.md). Re-run only when the paper or the ledger changes.
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.
Read reference/rationale.md for why family G exists, an example of each flaw, and the honest recall bound (why a pure-symbol step covered by no ledger claim stays at info).
Core principle
Ledger-anchored, span-verified, decide-from-the-written-proof, reviewer≠adjudicator.
There is no deterministic tool for family G (no arithmetic backbone like
consistency-audit's check_numeric_consistency.py) — proof validity is a semantic
judgment. So this skill runs exactly one substantive pass:
What ships with it
4 files 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.
- 24d ago Changed · -347 lines 6253ef9ba1fa
- 1mo ago Changed · -138 tokens per session 3da7fdacf66d
- 1mo ago Changed 2c43d328a118
- 1mo ago Changed 185ebf338133
- 1mo ago First seen · 817 lines · 222 tokens per session scan A 922998e6dbd8
proof-derivation-forensics is a skill published in the GitHub repository wenhaochai/claude-plugins (18 stars, last pushed yesterday), licensed MIT. It adds 84 tokens to every session and 7,754 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to proof-derivation-forensics, differing in 392 lines, and is treated as a copy.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Performs bounded, local exploratory analysis of explicitly supported scientific files. Supports redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Builds and analyzes phylogenetic trees using MAFFT multiple sequence alignment, IQ-TREE maximum likelihood with ModelFinder and branch support, and FastTree approximate inference. Uses ETE3 for tree summaries and visualization. Applies to homologous nucleotide or protein sequences, microbial gene trees, protein…
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
presenting-conference-talks
Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.