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 | $0.00123 | $0.05121 |
| Sonnet 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 13d 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, 47 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.
- 13d 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 (153 stars, last pushed 3d ago), licensed MIT. It adds 246 tokens to every session and 10,241 once invoked, about $0.0012 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
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.
replay
Audit a past pruning or approval decision by creating a counterfactual branch from a saved snapshot without mutating the live graph. Use when the user asks what would have happened under another decision, disputes a paused branch, or wants to inspect an earlier checkpoint.
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…
debug-sop
Diagnose errors, failed tests, crashes, hangs, regressions, suspicious outputs, and unexpected experimental results through reproducible hypothesis-driven debugging. Use whenever a script or system behaves incorrectly, even if the user only says it is broken or pastes an error. Respect diagnosis-only requests…
writeup-sop
Produce or revise research reports, result-bearing Markdown, paper sections, and manuscripts without overstating evidence. Use whenever writing text that reports experimental metrics, statistical conclusions, hypothesis rankings, theorem claims, or research findings. Do not trigger for ordinary README edits…
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.