Validate data science specification documents for completeness and readiness.
npx skills add https://github.com/edwinhu/workflows --skill ds-spec-reviewer
Please help me install this Agent Skill. Skill: ds-spec-reviewer Source: https://github.com/edwinhu/workflows/tree/main/lib/skills/ds-spec-reviewer Command: npx skills add https://github.com/edwinhu/workflows --skill ds-spec-reviewer
This skill prevents data science projects from starting with incomplete or flawed specifications, saving significant time and resources by catching issues early in the development lifecycle.
SPEC.md for completeness and adherence to best practices.SPEC.md document clearly defines all necessary data sources, the precise questions to be answered, and the expected deliverables, preventing costly rework later.Use the ds-spec-reviewer skill to verify the completeness of the .claude/SPEC.md file.
High-intent search queries and answers about installing and using this skill.
You can validate a data science specification by running an automated reviewer against your SPEC.md to identify gaps in data sources, analysis objectives, output formats, and success criteria before planning begins.
Specification review is evaluating a SPEC.md document against a predefined checklist to ensure all critical components like data sources and success criteria are addressed, preventing downstream issues and costly rework.
Data science projects require spec validation to catch incomplete specifications early, saving significant time and resources by preventing costly rework and downstream issues during the development lifecycle.
Check your SPEC.md for gaps by using an automated reviewer subagent that evaluates the document against a predefined checklist, specifically flagging missing data sources, objectives, output formats, and success criteria.
Yes, you can use an automated spec reviewer to ensure data science readiness by verifying the completeness of your SPEC.md file and facilitating an iterative feedback loop for spec refinement.
Automated specification review evaluates a SPEC.md strictly against a predefined checklist, meaning it identifies structural gaps in data sources and objectives but does not assess the technical feasibility of the analysis itself.
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