SuperAPI AI Directory

reproducibility-audit

Data & analytics

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Updated 10-16 06:21

About this skill

Reproducibility Audit

Assume the result is not reproducible until the missing pieces are enumerated.

Workflow

  1. Identify the target result to reproduce.
  2. Check for the required ingredients:
    • code
    • commit/version pinning
    • data access
    • config files
    • preprocessing steps
    • hardware assumptions
    • seed handling

references/good-enough-baseline.md expands this into a concrete presence/absence checklist (data provenance, pinned dependencies, smoke test, version identity, determinism) grounded in Good Enough Practices in Scientific Computing (Wilson et al., 2017) — a missing item there is a finding, not a minor annoyance. 3. Attempt to map paper claims to executable steps. 4. Record ambiguities and blockers explicitly. 5. If running code, log exact commands and outputs. 6. Classify status:

  • reproduced
  • partially reproduced
  • blocked
  • inconsistent with paper
  1. Recommend the smallest fixes needed for reproducibility.

Rules

  • Do not say reproduced unless you ran and checked it.
  • Missing config details are findings, not minor annoyances.
  • Separate code availability from result reproducibility.

Output shape

Return:

  • Target result
  • Available artifacts
  • Missing artifacts
  • Reproduction status
  • Commands / evidence
  • Recommended fixes