Claude Skills · Presentations

Presentation Signals

wanshuiyin/Anti-Autoresearch

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,...

★ 149 Synced 3 hours ago View SKILL.md

At a glance

Presentations Manual install Python Actively maintained

This skill is for Presentations and helps you spot duplicate or near-identical tables, flag leftover template strings in slides, and detect ai-generated or placeholder figures.

Install git clone --depth 1 https://github.com/wanshuiyin/Anti-Autoresearch cp -r Anti-Autoresearch/skills/presentation-signals ~/.claude/skills/presentation-signals
Can use BashReadWriteMCP

Setup, runtime and requirements describe wanshuiyin/Anti-Autoresearch, the repo this skill ships in.

Quality CheckSlidesartifactsTemplate DetectionPresentation Review

Also in wanshuiyin/Anti-Autoresearch

View the repo
Adversarial Case Builder Code Review & Testing

Synthesize the single strongest EVIDENCE-BOUND reviewer case to reject a paper, built ONLY from the evidence ledger (claims.json) + the othe...

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Anti Autoresearch Presentations

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Audit whether a paper's EVALUATION DESIGN actually measures what it claims and whether its reporting is complete — the validity layer family...

Evidence Ledger Documents & Spreadsheets

Build the deterministic evidence ledger (artifact_manifest.json + claims.json) that every other Anti-Autoresearch auditor reads. One pass in...

Experiment Forensics Code Review & Testing

Audit experiment integrity against the evidence ledger. At L2 (repo + result files present) a fresh cross-model reviewer reads the eval code...

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