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Stage ml: ML venue lens (default NeurIPS) scoring per reference for technical fit: canonicality, baseline relevance, recency for SOTA claims; writes ml_* columns.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | cite-guard-ml |
| description | Stage ml: ML venue lens (default NeurIPS) scoring per reference for technical fit: canonicality, baseline relevance, recency for SOTA claims; writes ml_* columns. |
| license | MIT |
| metadata | {"version":"1.0.0","category":"research-qa","tags":["ml","neurips","icml","iclr"]} |
Per reference:
Default profile: neurips (override via CLI).
Stage audit: bibliography hygiene checks per reference and writes audit_quality/audit_confidence/audit_remediation per row.
Stage ground: evaluates whether each reference supports the claims it is cited for; fetches evidence preferring md/html/tex/rtf/txt/pdf; writes ground_* columns and produces grounding reports.
Initialize the reference QA pipeline by creating out/audit_references.csv with one row per BibTeX entry and required stage columns.
Stage resolve: resolve each reference against OpenAlex/Crossref/arXiv/DBLP; detect hallucinations and metadata mismatches; writes resolve_* columns and refs.corrected.bib.
Final stage: compute reference_quality_score by weighted QUALITY×CONFIDENCE across audit/resolve/ground/venue/ml; rank and assign review_priority; includes default + venue-specific blocker rules.
Stage venue: policy venue lens (governance/ethics/policy) scoring per reference based on authority/genre appropriateness for policy claims; writes venue_* columns.
