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 evidence-ledgergit 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/evidence-ledger)<a href="https://agentmods.dev/skills/wanshuiyin/anti-autoresearch/evidence-ledger"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/evidence-ledger/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/evidence-ledger"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/evidence-ledger.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 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 62 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 427 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 Rogue Agent · line 618 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
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 | $0.00221 | $0.11987 |
| Opus 5.5 | $0.00088 | $0.04795 |
| Sonnet 5.5 | $0.00044 | $0.02397 |
| Haiku 4.5 | $0.00022 | $0.01199 |
Grade A, and why
evidence-ledger scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
( curl -fsSL "https://arxiv.org/e-print/$ARG" -o "$PAPER_DIR/src.tar" \ Copies of this mod
1 near-identical copy found in the catalogue:
- evidence-ledger — 98% identical, 219 lines differ
How it starts
The opening of the file, as written. The whole thing — 638 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evidence Ledger — the spine every auditor reads
Infrastructure skill, not an auditor. It produces the only structure the auditor skills are allowed to reason over, so they don't each re-read the PDF and hallucinate a different table and a different list of numbers. It emits no findings and no verdict — only
artifact_manifest.json+claims.json. Seereferences/integrity-forensics-contract.md§"The pipeline" (stages [1]–[2]).
Build the ledger for: $ARGUMENTS
🔁 Not verdict-bearing — but not a polling skill either. The deterministic backbone (Steps 1–2) is a pure function of the hashed sources: same source bytes → byte-identical
claims.json. Re-run it only when the sources change, never on a wall-clock timer. The only non-deterministic part is the optional enrichment pass (Step 3), which is additive and skippable. Do not wrap this skill in/loop//schedule/CronCreate; there is no verdict to re-fire and no external event to wait on.
Why this exists
Five language-model auditors each independently parsing a PDF = five different hallucinated tables and five different number lists, none reproducible — and the obvious dismissal, "an LLM grading another LLM's paper is just slop." The structural answer is one deterministic pass that turns the paper into:
artifact_manifest.json— what was observable; this fixes the observability level L, the ceiling on every downstream finding's severity, andclaims.json— a list of span-anchored, hashed, checkable claims (schemas/claims.schema.json).
Every downstream finding must cite a claim_id from this ledger and quote a verbatim
span of it. No ledger claim → no finding (the single most important integrity
rule of the repo, enforced again by tools/adjudicate_findings.py). That is what
makes the difference between "a model said so" and "here is the exact sentence, its
file, and its content hash" (DESIGN.md §2).
Role in the pipeline (what this skill does and does NOT do)
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 · 638 lines · 221 tokens per session scan A 1d1b58bf2ca1
evidence-ledger is a skill published in the GitHub repository wanshuiyin/Anti-Autoresearch (172 stars, last pushed 11d ago), licensed MIT. It adds 221 tokens to every session and 11,987 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
codex-autoresearch
Triage improvement work and run or resume accepted measured loops in a local project. Architecture, documentation, UX, product study, open research, taste, and one-shot fixes stay direct unless the user explicitly requests repeated measurement with a complete experiment contract.
auto-paper-improvement-loop
An automated loop for improving a generated research paper through repeated review, editing, and recompilation. Recompiling rebuilds the paper so changes can be checked in the final document.
proof-checker
A rigorous proof-checking and repair skill for LaTeX mathematics. LaTeX is a text format commonly used to write mathematical documents.
citation-audit
Zero-context verification that every bibliographic entry in the paper is real, correctly attributed, and used in a context the cited paper actually supports. Uses a fresh cross-model reviewer with web/DBLP/arXiv lookup to catch hallucinated authors, wrong years, fabricated venues, version mismatches, and wrong-context…
grant-proposal
A grant-writing workflow that turns research ideas and related literature into structured funding proposals for named national and international grant formats.
paper-illustration
A tool for creating academic-paper illustrations such as architecture diagrams and method figures with Gemini image generation and Claude-guided revisions.