repository_analyst_skill

This skill analyzes repository history to reveal code evolution patterns and actionable refactoring insights with data-driven recommendations.
  • Ruby

5

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

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npx veilstrat add skill viamin/aidp --skill repository_analyst

  • SKILL.md4.1 KB

Overview

This skill is Repository Analyst, an expert in version control analysis and code evolution patterns. It inspects git history to surface hotspots, ownership, churn, and coupling so teams can make prioritized refactoring decisions. The output is evidence-driven and designed for immediate action by engineering and product leads.

How this skill works

The skill parses commit logs, authorship, and file change histories using tools like the ruby-maat gem and plain git analysis. It computes churn, temporal coupling, file age, and ownership metrics, then ranks files and modules by risk and maintenance cost. Results are presented as concise metrics, prioritized recommendations, and exportable raw data for further analysis.

When to use it

  • Before planning major refactors or architectural changes
  • When diagnosing recurring bugs or high maintenance areas
  • During onboarding to map code ownership and knowledge gaps
  • To evaluate team delivery patterns and branching strategies
  • When assessing technical debt or preparing for releases

Best practices

  • Run repository analysis on a representative time window (e.g., 6–12 months) to avoid short-term noise
  • Correlate churn and bug reports before prioritizing fixes to avoid chasing false positives
  • Combine temporal coupling with module boundaries to reveal architectural violations
  • Share executive summaries with non-technical stakeholders and attach raw metrics for engineers
  • Flag data quality issues (rebases, squashed merges) and document assumptions made during analysis

Example use cases

  • Identify top 20% of files that cause 80% of maintenance effort to focus refactoring
  • Detect single-person ownership areas and recommend pair programming or documentation targets
  • Map files frequently changed together to inform modularization or API extraction
  • Produce CSV exports of authorship and churn for capacity planning and knowledge transfer
  • Assess stabilization trends after a release to decide when to freeze features

FAQ

Access to the git repository (clone or read access) and an analysis time window. Optional: issue tracker links to correlate bugs with commits.

How do you prioritize suggested fixes?

Recommendations are prioritized by estimated impact (churn × bug correlation × criticality) and by effort (rough LOC/complexity estimates), with explicit assumptions and confidence levels.

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