result-provenance-review
CommunityEnsure reproducibility and accurate recording of computational results.
Data & Analytics#data integrity#provenance tracking#result validation#computational reproducibility#result assurance
Authorstephendor
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill helps to confirm that computational results are reproducible, correctly recorded, and trustworthy for downstream use.
Core Features & Use Cases
- Provenance Assurance: Verifies the reproducibility of results, including caching, seeds, output paths, and no-overwrite behavior.
- Result Validation: Ensures that results match their recorded parameters and seeds.
- Use Case: When reviewing or producing TDL computational result files, this skill helps to ensure that results are reliable for further analysis or reporting.
Quick Start
Run the result-provenance-review skill on the latest result file to confirm its reproducibility and accuracy.
Dependency Matrix
Required Modules
None requiredComponents
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: result-provenance-review Download link: https://github.com/stephendor/TDL/archive/main.zip#result-provenance-review Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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