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 presentation-signalsgit 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/presentation-signals)<a href="https://agentmods.dev/skills/wanshuiyin/anti-autoresearch/presentation-signals"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/presentation-signals/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/presentation-signals"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/presentation-signals.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 69 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 | $0.00246 | $0.10241 |
| Opus 5.5 | $0.00098 | $0.04096 |
| Sonnet 5.5 | $0.00049 | $0.02048 |
| Haiku 4.5 | $0.00025 | $0.01024 |
Grade A, and why
presentation-signals 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:
- presentation-signals — 95% identical, 240 lines differ
How it starts
The opening of the file, as written. The whole thing — 648 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Presentation Signals — the surface tells (auxiliary, never a verdict)
Run surface signal checks for: $ARGUMENTS (requires claims.json from
/evidence-ledger). Emit span-anchored presentation-signals.findings.json. This
skill computes no verdict.
⚠️ This skill is deliberately weak by design. A polished paper can be fraudulent and a rough paper can be honest, so surface signals must never drive a verdict. Everything here is emitted under skill
presentation-signals, which the adjudicator caps atminor(SURFACE_ONLY_SKILLS+SURFACE_PATTERNSintools/adjudicate_findings.py) — at mostSOFT_FLAGS, neverHARD_FLAGS. This is not an AI-text classifier; for authorship detection use a dedicated tool (Pangram / GPTZero / Binoculars). Our only job is to add "combine with the substantive findings and look closer" context. Seereferences/hack-pattern-taxonomy.md§F.
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It is verdict-bearing input — it proposes the surface 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 → check once. (Mirrors ARIS's external-cadence doctrine.)
Why this exists
Real reviewers notice surface tells before they read a single number — and they say so out loud: "两张表一模一样" (two tables are identical), "图还是大模型生成的" (the figure is LLM-generated), "就这还没写满9页" (couldn't even fill 9 pages), "堆砌名词吗" (just stuffing jargon?), "本文不是什么什么，而是什么什么…论文应该直接表达 做了什么" (stop hedging "this paper is not X but rather Y" — just say what you did), "摘要写的像实验分析,读不到引言" (the abstract reads like an experiment log; the introduction is unreadable). An autoresearch pipeline (or a rushed human) produces exactly these artifacts: a table copy-pasted and never updated, an oversized float to pad the page limit, a decorative generated illustration in place of a real results plot, paragraphs of generic LLM boilerplate, draft text so densely over-hedged that every sentence defends against an objection, and an abstract that dumps experiment notes instead of telling a background → contribution → evidence story.
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 · 648 lines · 246 tokens per session scan A aac2d7e2ba71
presentation-signals is a skill published in the GitHub repository wanshuiyin/Anti-Autoresearch (172 stars, last pushed 11d ago), licensed MIT. It adds 246 tokens to every session and 10,241 once invoked, about $0.0010 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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