# Researchstack Artifact Audit

> Reproducibility and evidence-audit skill for research projects. Use when Codex should trace figures and claims back to scripts, configs, datasets, seeds, hardware assumptions, and logs; find missing provenance; or prepare the code and experiment pipeline for submission, open-sourcing, or artifact evaluation.

- Skill: `lqf0624/researchstack-artifact-audit` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add lqf0624/researchstack-artifact-audit`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lqf0624/researchstack-artifact-audit/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: lqf0624 (https://skillmd.com/u/lqf0624)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/lqf0624/researchstack-artifact-audit

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# Artifact Audit

Audit the paper like an artifact evaluator.

Read [../references/evidence-contracts.md](../references/evidence-contracts.md) and preserve the claim-promotion states during the audit.

Check for each reported result:

- where the number came from,
- which code path produced it,
- which config or seed was used,
- whether the environment is documented,
- whether reruns are expected to match exactly or statistically.
- whether the timed and semantic boundary matches the real deployment lifecycle.

Produce:

1. Provenance table mapping claim -> experiment -> script/config -> output artifact.
2. Reproducibility gaps.
3. Environment assumptions.
4. Highest-risk silent failure modes.
5. Claim-promotion downgrades where provenance or controls are insufficient.

Be especially strict about:

- cached outputs with unclear freshness,
- figures hand-edited after generation,
- hidden dataset filtering,
- mismatched train/test or warmup/steady-state conditions,
- benchmark harnesses that favor the proposed method,
- copied numbers that no longer match the current codebase.
- required planning, packing, synchronization, waiting, or assembly work excluded from headline timing.