# Reproducibility Checklist Audit

> Assess paper completeness against ML Reproducibility Checklist

- Skill: `yogsoth-ai/reproducibility-checklist-audit` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add yogsoth-ai/reproducibility-checklist-audit`
- Raw SKILL.md: https://api.skillmd.com/api/skills/yogsoth-ai/reproducibility-checklist-audit/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Security
- Author: yogsoth-ai (https://skillmd.com/u/yogsoth-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/yogsoth-ai/reproducibility-checklist-audit

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# Reproducibility Checklist Audit


## Purpose

Evaluate a paper against the standard ML Reproducibility Checklist (as used by NeurIPS, ICML, ICLR). Produces a structured assessment of what information is present, what is missing, and an overall reproducibility score.

## Input Schema

| Field | Type | Description |
|-------|------|-------------|
| paper_content | string | Full paper text (markdown format) |

## Output Schema

```json
{
  "paper_title": "string",
  "checklist": {
    "model_architecture": {"present": true, "details": "string"},
    "training_procedure": {"present": true, "details": "string"},
    "hyperparameters": {"present": true, "details": "string"},
    "hyperparameter_search": {"present": false, "details": "string"},
    "datasets": {"present": true, "details": "string"},
    "data_preprocessing": {"present": true, "details": "string"},
    "evaluation_metrics": {"present": true, "details": "string"},
    "error_bars_or_confidence": {"present": false, "details": "string"},
    "number_of_runs": {"present": false, "details": "string"},
    "compute_resources": {"present": false, "details": "string"},
    "code_availability": {"present": true, "details": "string"},
    "random_seeds": {"present": false, "details": "string"}
  },
  "overall_score": 0.0,
  "critical_gaps": ["string"],
  "reproducibility_risk": "low|medium|high|critical"
}
```