Review data science analyses for methodology, reproducibility, and code quality.
npx skills add https://github.com/edwinhu/workflows --skill ds-review
Please help me install this Agent Skill. Skill: ds-review Source: https://github.com/edwinhu/workflows/tree/main/lib/skills/ds-review Command: npx skills add https://github.com/edwinhu/workflows --skill ds-review
This Skill rigorously reviews data analysis methodologies, code quality, and reproducibility to ensure the integrity and reliability of research-grade findings.
Initiate a parallel review of the current data analysis project.
High-intent search queries and answers about installing and using this skill.
To audit data science analysis for reproducibility, this Skill spawns parallel reviewers to validate methodology, code quality, and adherence to SPEC.md and PLAN.md, ensuring research-grade integrity for publication.
A multi-agent code review uses specialized agents to evaluate methodology, reproducibility, and code quality simultaneously. It merges findings into a consolidated report to ensure data analysis meets high-stakes decision-making standards.
You ensure data analysis code meets publication standards by running a comprehensive audit that validates reproducibility and methodology against research-grade benchmarks, reconciling multi-agent findings into actionable feedback.
Yes, you need CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS enabled to execute the parallel review process, which spawns the specialized methodology, reproducibility, and code quality reviewers required for the audit.
Yes, you can automatically validate analysis code against SPEC.md and PLAN.md. The review process checks adherence to these specifications to guarantee the methodology meets research-grade publication requirements.
Use a reconciliation protocol when multiple specialized reviewers complete their audits. It merges and prioritizes parallel findings into a consolidated, actionable report for high-stakes decision-making or publication.
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