manuscript-provenance

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Computational provenance audit verifying every number, table, and figure in a manuscript derives from code, not manual entry. Triggers on: "check provenance", "verify reproducibility", "audit my pipeline", "are my numbers from code", "provenance audit". Companion to manuscript-review (prose audit).

AI & Automation 313 stars 46 forks Updated 4 days ago MIT

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Quality Score: 93/100

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Skill Content

# Manuscript Provenance Audit **Pipeline position:** Phase 2a (grounding audit). Runs in parallel with `manuscript-typography`. Depends on: content settled after Phase 1 fixes. Produces macro manifest consumed by `manuscript-review` Pass 13 (Cross-Element Coherence). ## Purpose Verify that a manuscript is a faithful rendering of computational outputs. Every number, table, figure, category label, ordering, and threshold in the document must trace to a specific script, config file, or pipeline output. Manual data entry in a manuscript is a reproducibility defect. This skill produces a provenance map — a structured report linking each manuscript artifact to its generating code — and flags every break in the chain. Companion skill: `manuscript-review` audits the document as prose (structure, argumentation, citations). This skill audits whether the document content is computationally grounded. Run both for complete pre-publication coverage. ## Boundary Agreement with manuscript-review | Concern | manuscript-review | This skill (manuscript-provenance) | | ------------------------- | ----------------------------------------------------------- | -------------------------------------------------------------- | | Reproducibility | Does the paper describe enough to reproduce? (§6) | Does the code actually produce what the paper claims? (§1, §7) | | Figures/Tables ...

Details

Author
Mathews-Tom
Repository
Mathews-Tom/armory
Created
5 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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