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 ai-style-impressionsgit 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/ai-style-impressions)<a href="https://agentmods.dev/skills/wanshuiyin/anti-autoresearch/ai-style-impressions"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/ai-style-impressions/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/ai-style-impressions"><img src="https://agentmods.dev/badge/skills/wanshuiyin/anti-autoresearch/ai-style-impressions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00284 | $0.13319 |
| Opus 5 | $0.00142 | $0.06660 |
| Sonnet 5 | $0.00057 | $0.02664 |
| Haiku 4.5 | $0.00028 | $0.01332 |
Grade A, and why
ai-style-impressions 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:
- ai-style-impressions — 100% identical, 27 lines differ
How it starts
The opening of the file, as written. The whole thing — 774 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Writing-Style Impressions — itemized, located, ZERO verdict weight (NOT integrity findings)
Surface AI writing-style impressions for: $ARGUMENTS (requires claims.json
from /evidence-ledger). Emit span-anchored ai-style-impressions.findings.json.
Every finding here is an impression with ZERO verdict weight — this skill
proposes no integrity finding and computes no verdict.
⚠️ This is the repo's ONLY non-integrity output, and it is non-integrity by construction. AIS findings are transparent, itemized impressions of AI-generated writing style. The adjudicator (
tools/adjudicate_findings.py) gives every AIS finding ZERO verdict weight — it is forced toinfo, excluded fromoverall_verdict, and rendered in a separate report section, "AI Writing-Style Impressions — NOT integrity findings · ZERO verdict weight". A paper can beCLEAN_GIVEN_EVIDENCEand still list many AIS impressions. These are not factual/integrity inconsistencies and imply no authorship probability. We are not an opaque AI-text classifier: no scores, never "this is AI-written" / "likely AI-generated". Every finding is a named, located, itemized observation with anfp_case. For authorship detection use a dedicated tool (Pangram / GPTZero / Binoculars) — that is out of scope here, by design.
🔒 Do not wrap this skill in
/loop,/schedule, orCronCreate. It is report-input — it proposes the impressions the deterministic adjudicator renders in the zero-weight AIS section. 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 say the quiet part out loud about style: "这一看就是大模型写的味儿" (this reads like an LLM wrote it), "满篇 'it is worth noting' / '值得注意的是'" (every paragraph hedges with "it is worth noting"), "通篇 not only…but also、一堆 however/ therefore" (chains of however/therefore/moreover), "堆术语，论证是空的" (term-stuffing with no argument under it), "实验叫 'Experiment Set Gamma'，从没定义" (an undefined internal codename used as if defined), "图都是一个味儿的 AI 生成图" (the figures share one generated visual grammar), "附录像把跑的 trace 一股脑倒进去" (the appendix reads like a dumped run trace). An autoresearch pipeline (or a rushed human leaning on an assistant) produces exactly these style artifacts.
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 · 774 lines · 284 tokens per session scan A 0a4bca45895b
ai-style-impressions is a skill published in the GitHub repository wanshuiyin/Anti-Autoresearch (153 stars, last pushed 3d ago), licensed MIT. It adds 284 tokens to every session and 13,319 once invoked, about $0.0014 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.
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