Use for the "Statistical Caution" step in a NeurIPS paper workflow when you need paragraph output for the Experiments stage from verified inputs. Best for when refining or drafting the experiments portion of a neurips-style paper; when you need a reusable artifact rather than a one-off paragraph rewrite; not for inventing unsupported experiments, citations, proofs, or contributions.
Original id: 07_09_statistical_caution_skill
Section: Experiments
Layer: section
Category: experiments
Produce a reusable statistical caution module for Experiments within a NeurIPS paper workflow, with explicit boundaries, evidence discipline, and handoff-ready structure.
Required inputs:
paper_blueprint (required): Verified paper-level question, answer, storyline, contributions, and target sections.contribution_list (required): Stable list of contributions already accepted by the authors as in-scope and evidence-backed.claim_evidence_map (required): Claim-to-evidence alignment table used to prevent unsupported statements and novelty inflation.experiment_evidence (required): Trusted tables, figures, logs, or manually verified summaries of experimental outcomes and caveats.Optional inputs:
terminology_registry (optional): Canonical terms, symbols, and disallowed variants for consistency control.current_draft_context (optional): Existing local draft text or outline to revise, compress, or audit.venue_constraints (optional): Page limit, formatting rules, length pressure, or author preference on tone.paragraphobjective_interpretation, main_output, risk_flags, handoff_notestechnical / Englishreferences/spec.yaml.Core hard rules:
Section-specific checks:
Revision policy:
Reasoning focus:
Self-checklist:
Upstream skills:
00_01_paper_blueprint_skill00_03_contribution_extractor_skill00_04_claim_evidence_alignment_skill00_06_terminology_notation_registry_skill05_11_method_draft_skill07_08_result_interpretation_skillDownstream handoff:
07_10_generalization_analysis_skill