/ai-style-impressions

name: ai-style-impressions
description: "Transparent, itemized impressions of AI-generated WRITING STYLE — the repo's ONLY non-integrity track. Two passes: a deterministic defensive-hedge density screen (tools/check_ai_style.py, AIS-DEFENSIVE-HEDGE) plus a fresh cross-model GROSS-cases-only semantic pass over the 13 AIS-* style tells (broken narrative arc, LLM phrase tics, jargon-stuffing, invented codenames, clause/formula walls, gratuitous pseudocode, bullet overuse, bold-module spam, restatement loops, focus drift, single-style figures, appendix dumping). Every finding is named, LOCATED, span-anchored to the evidence ledger (claims.json), carries not_integrity_finding:true + false_positive_risk:high + an fp_case, and gets ZERO verdict weight: the adjudicator forces it to info, excludes it from overall_verdict, and renders it in a SEPARATE report section. NOT an AI-text classifier — no scores, no \"this is AI-written\", no authorship probability; a paper can be CLEAN_GIVEN_EVIDENCE and still list many. Emits ai-style-impressions.findings.json; computes NO verdict; if a tell is actually substantive it routes to the integrity auditor. Triggers: \"AI style\", \"vibe check\", \"writing fingerprint\", \"AI 文风\", \"vibe paper\"."
argument-hint: [paper-dir | claims.json]
allowed-tools: Bash(*), Read, Write, mcp__codex__codex

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 to `info`, excluded from > `overall_verdict`, and rendered in a **separate** report section, > *"AI Writing-Style Impressions — NOT integrity findings · ZERO verdict weight"*. **A > paper can be `CLEAN_GIVEN_EVIDENCE` and 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 an `fp_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`, or `CronCreate`.** 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.

These signals are **real** in the sense that reviewers react to them — but they are **not evidence of misconduct, and not evidence of authorship.** Honest LLM-assisted writing produces phrase tics; non-native English produces awkward transitions; a house style produces bold module names and consistent figures; a careful author hedges in the Limitations. So this skill's contract is narrow and permanent:

• it emits **only** the 13 `AIS-*` style patterns, each as an **impression**;
• it tags **every** finding `not_integrity_finding: true` + `false_positive_risk: high`

and attaches an `fp_case` (the legitimate, not-necessarily-AI explanation);

• it **never** says a paper is "AI-generated", never assigns a probability or a score,

and **never** moves the verdict;

• **silence is the common, correct output** — most papers should produce few or zero

AIS impressions.

It exists so a reviewer's *style* reactions become **named, located, checkable impressions** instead of an unfalsifiable "vibe" — and so that whenever a style tell is actually a **substantive** problem, this skill **hands it to the right integrity auditor** rather than smuggling it in under a style label.

Core doctrine (the non-negotiables)

**Ledger-anchored, span-verified, ZERO verdict weight, NOT an authorship classifier, reviewer ≠ adjudicator.** Two passes feed the pipeline:

1. a **deterministic** pass (no model) — the one objectively computable style signal: a conservative **defensive-hedge density screen** (`AIS-DEFENSIVE-HEDGE`), which fires only on a genuine pattern (≥4 distinct strong-template hedge sentences across ≥2 non-excluded sections **and** ≥25% of all scope sentences). It flags the **recurrence of the hedge SHAPE**, never who wrote it (`tools/check_ai_style.py`); 2. a **fresh cross-model GROSS-cases-only** semantic pass — the 13 judgment-call style tells, each span-anchored, `not_integrity_finding: true`, `false_positive_risk: high`, with an `fp_case`. (`AIS-DEFENSIVE-HEDGE` is **dual**: deterministic for the pervasive case above + this semantic pass for the