Contributor Codebase Analyzer

Read every commit. Grow every engineer.

Created by Anivar Aravind

DORA metrics tell you how fast you're moving. They don't tell you where you're going.

Dashboards full of PR volume, commit counts, and "impact scores" actively punish your best architects — the ones simplifying complexity rather than adding to it.

This Agent Skill values brevity over volume and design patterns over code churn. It brings the rigor of manual code review to agentic workflows — reading every commit diff, not a sample.

The Problem

Engineering reviews rely on vanity metrics and manager impressions. These miss what matters: an engineer who reduces a payment processor from 2,000 lines to 400 looks like "low output" on a dashboard. An engineer repeatedly shipping debug code to production looks like "high output."

No dashboard shows you the difference. Reading the code does.

The Solution

This skill doesn't count lines. It reads the diffs.

• Reads every commit diff for each contributor, quarterly
• Calculates an accuracy baseline — how often code ships without needing fixes
• Detects design improvements and anti-patterns from actual changes
• Maps codebase structure, cross-repo dependencies, and technical debt
• Generates growth reviews with specific commit evidence

No scores. No rankings. Accuracy rates surface where to look deeper — not verdicts.

All analysis saves incrementally — built for agentic context limits. Interrupt and resume without losing progress.

Getting Started

1. Prerequisites

• git (required)
• gh — GitHub CLI (required for GitHub repos)
• glab — GitLab CLI (required for GitLab repos)
• jq and bc (optional, for structured output and calculations)

2. Install

npx skills add anivar/contributor-codebase-analyzer -g

This auto-detects your AI agents (Claude Code, Cursor, Gemini CLI, GitHub Copilot, and others) and installs the skill to all of them.

Manual install:

git clone https://github.com/anivar/contributor-codebase-analyzer.git
ln -s "$(pwd)/contributor-codebase-analyzer" ~/.agents/skills/contributor-codebase-analyzer

3. Onboard

Navigate to your project and run:

./scripts/checkpoint.sh onboard

This auto-detects your platform (GitHub/GitLab), repo, and org. No manual configuration needed.

4. Use

"Analyze github.com/alice-dev for 2025 annual review in repo org/repo"

"Compare github.com/alice-dev and github.com/bob-eng for 2025. Who should be promoted?"

"Analyze the codebase structure of this repo"

"Map dependencies across all repos in our org"

"Run a governance analysis: tech portfolio, debt registry, security posture"

What You Get

Contributor Reviews

• Accuracy rate: 100% - (fix-related commits / total commits) — a baseline that surfaces where to look deeper
• Anti-pattern detection: debug code shipped, empty catch blocks, hook bypass, mega-commits
• Strength identification: defensive programming, offline-first design, code reduction, feature gating
• Quarter-by-quarter breakdown: growth trajectory, complexity trends, domain breadth
• Development assessment: readiness signals, growth areas, and next-level plan
• Multi-engineer comparison: complementary strengths with contextual evidence

Codebase Reports

• Repository structure: module map, entry points, architecture patterns, dependencies
• Cross-repo relationships: shared libraries, internal packages, dependency graph
• Enterprise governance: technology portfolio, technical debt registry, security posture

Periodic Checkpoints

All work saves to $PROJECT/.cca/ in append-only JSONL format. Resume from any phase — never re-analyze what's already been processed.

Platform Support

Works with both GitHub and GitLab (including nested subgroups). Platform is auto-detected from your git remote URL.

How It Works

The skill follows a 7-phase process for contributor analysis:

1. Identity Discovery — finds all git email variants automatically
2. Metrics — commits, PRs/MRs, reviews, lines changed
3. Read ALL Diffs — quarterly parallel agents with batch sizing to prevent failures
4. Bug Introduction — self-reverts, crash-fixes, same-day fixes, hook bypass
5. Code Quality — anti-patterns and strengths from actual diffs
6. Report Generation — structured markdown with growth assessment and development plan
7. Comparison — multi-engineer strengths comparison with evidence

Batch sizing is enforced from hard limits discovered in production:

Project Structure

├── SKILL.md              # Agent entry point and routing
├── AGENTS.md             # Full compiled guide for agents
├── assets/
│   └── logo.svg          # Project logo
├── references/           # Progressive disclosure by topic
│   ├── onboarding.md
│   ├── contributor-analysis.md
│   ├── accuracy-analysis.md
│   ├── code-quality-catalog.md
│   ├── qualitative-judgment.md
│   ├── report-templates.md
│   ├── codebase-analysis.md
│   └── periodic-saving.md
├── scripts/
│   └── checkpoint.sh     # Save/resume/status/ratelimit helper
└── LICENSE

Design Decisions

Why every commit, not sampling? Sampling misses the story. An engineer's best work might be a 12-line fix that prevents a payment double-charge. Sampling skips it. Reading every diff is what experienced code reviewers do — this skill encodes that expertise into a repeatable process.

Why an Agent Skill? Analyzing a year of commits doesn't fit in a single AI session or context window. Agent Skills are self-contained expertise that save checkpoints, resume across sessions, and never re-read a diff already understood. This skill automates the reading — the thinking is still yours.

Why baselines, not scores? Numbers like commit counts, lines changed, or accuracy rates aren't evaluations — they're baselines that surface where to look. A low accuracy rate doesn't mean a bad engineer — it often means they own the riskiest module in the system. Numbers open the door. Reading the code walks through it.

Why constructive framing? Strong engineering cultures grow people — they don't grade them. Every label, every comparison, every recommendation is framed for growth: "Developing" not "Below Expectations," "Growth Areas" not "Blockers," strengths before concerns. The fairness checks aren't afterthoughts — they're load-bearing.

Why both GitHub and GitLab? Enterprise teams don't live on one platform. Auto-detection from git remote -v means zero configuration — the skill adapts to whatever the team uses.

Constructive Use

This tool reads code to grow engineers, not judge them.

Do:

• Use findings to start development conversations, not deliver verdicts
• Share reports WITH the engineer, not just about them
• Recognize strengths before discussing growth areas
• Consider repository and project context
• Consider team context: deadlines, on-call, team changes, unfamiliar codebases

Don't:

• Analyze commits in isolation — diffs without project context are noise
• Use metrics to justify termination without human context
• Compare engineers as a ranking exercise — compare for complementary strengths
• Treat anti-patterns as character flaws — they're often process or tooling gaps
• Share individual accuracy rates publicly or competitively
• Run analysis without the engineer's awareness

Fairness checks built in:

• Same time window and scope for all contributors
• Fix-related commits analyzed for root cause (inherited bug vs introduced)
• Low-activity periods flagged for context (leave, onboarding, cross-team work)
• "Peak then decline" flagged as "support needed," not penalized
• All labels are growth-oriented: "Developing" not "Below Expectations"

Ecosystem — Skills by Anivar Aravind

Testing Skills

Library & Framework Skills

Author

Anivar Aravind — Building AI agent skills for engineering excellence.

License

MIT