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presentation-signals

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Checkable-ish surface presentation signals a reviewer notices first — duplicate/near-identical tables, leftover pipeline/template strings, too-few or LLM-looking figures, and page-padding. AUXILIARY ONLY and weak by design: a deterministic pass (tools/check_presentation.py — dup-table + pipeline-artifact) plus a fresh cross-model GROSS-cases-only semantic pass (thin-float, LLM-figure, page-padding), every above-info finding span-anchored to the evidence ledger (claims.json). The adjudicator CAPS everything at minor (SURFACE_ONLY_SKILLS + SURFACE_PATTERNS) — these contribute at most SOFT_FLAGS, never a HARD verdict — default false_positive_risk:high. NOTE: the pure AI writing-STYLE impressions (AI-flavor prose, defensive 'not-X-but-Y' hedging, narrative-arc, jargon-stuffing, invented codenames) MOVED to the zero-verdict-weight AIS track — for those use skills/ai-style-impressions, NOT this. Emits presentation-signals.findings.json; NEVER computes the verdict. Triggers: "presentation signals", "surface check", "duplicate tables", "排版信号".

skills/presentation-signals/SKILL.md

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Last checked
Nov 11, 2026

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181
Repository last updated
Nov 6, 2026
License
MIT

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# 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**.

> ⚠<U+FE0F> **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 at `minor`** (`SURFACE_ONLY_SKILLS` + `SURFACE_PATTERNS` in
> `tools/adjudicate_findings.py`) — at most `SOFT_FLAGS`, **never** `HARD_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. See
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