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 consistency-auditgit 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/consistency-audit)<a href="https://agentmods.dev/skills/wanshuiyin/anti-autoresearch/consistency-audit"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/consistency-audit/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/consistency-audit"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/consistency-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, 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 255 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 | $0.00133 | $0.10604 |
| Opus 5.5 | $0.00053 | $0.04242 |
| Sonnet 5.5 | $0.00027 | $0.02121 |
| Haiku 4.5 | $0.00013 | $0.01060 |
Grade A, and why
consistency-audit 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:
- consistency-audit — 97% identical, 279 lines differ
How it starts
The opening of the file, as written. The whole thing — 666 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Consistency Audit — the paper vs itself
Audit intra-paper self-consistency for: $ARGUMENTS (requires claims.json
from /evidence-ledger). Emit span-anchored consistency-audit.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.)
The flagship instrument. Internal contradiction is the single most defensible thing you can check on an unknown submission: it needs no external GT, runs at L0 (PDF-only), and is exactly where machine-generated papers crack — they hallucinate local coherence. Recall scales with the ledger: a PDF-text (L0) ledger extracts only number/scope spans, so the table/caption/method-drift checks gain teeth at L1 (LaTeX), where tables and captions are actually extracted. Adapted from ARIS
paper-claim-audit, reframed from "paper vs result files" to "paper vs itself." There is no external ground truth in this skill.
Why this exists
An autoresearch pipeline (or rushed human) writes the abstract, the tables, the method section, and the appendix in separate passes and never reconciles them. The result is a paper that disagrees with itself:
- abstract quotes 85.3% accuracy; the best row of its own Table 2 is 84.7%;
- "improves by 16%" when 73.1 → 78.0 is +6.7% relative / +4.9 points;
- "mean over 5 seeds" where the number is the single best seed, and N=3 in the table;
- method section says "no test-time labels"; the experimental-setup paragraph loads gold labels for calibration;
- "comprehensive evaluation across diverse benchmarks" on two datasets, one domain.
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 · 666 lines · 133 tokens per session scan A fb16bc9ad238
consistency-audit is a skill published in the GitHub repository wanshuiyin/Anti-Autoresearch (172 stars, last pushed 11d ago), licensed MIT. It adds 133 tokens to every session and 10,604 once invoked, about $0.0005 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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