presentation-signals

# 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 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 > `references/hack-pattern-taxonomy.md` §F. > 🔒 **Do not wrap this skill in `/loop`, `/schedule`, or `CronCreate`.** 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...