denn-gubsky/ai-dev-effectiveness
8 stars · Last commit 2026-05-13
Measure AI co-programming effectiveness on any git repo. Detects Claude/Copilot/Cursor/Codex signatures and triangulates productivity multipliers via top-down roles, bottom-up formula, and an optional Claude Code subagent that reads diffs.
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# ai-dev-effectiveness  > Measure how much AI co-programming actually accelerates software delivery — on any git repo. `ai-dev-effectiveness` reads your git history, detects which commits were co-authored by AI coding agents (Claude, Copilot, Cursor, Codex, Aider, …), and produces an interactive HTML report (and a structured JSON sidecar) comparing your real delivery against a hypothetical traditional team. It triangulates three independent estimators — top-down specialist roles, bottom-up per-commit formulas, and an AI judge that reads each diff — so the productivity multipliers are credible, not just plausible. ## Quickstart ```bash # Install (macOS): brew install pipx && pipx ensurepath pipx install git+https://github.com/denn-gubsky/ai-dev-effectiveness # Pick a folder DEDICATED to running analyses — NOT one of your project repos. mkdir -p ~/dev-effectiveness && cd ~/dev-effectiveness # One-time: install the bundled subagents (effort-judge + roles-architect). ai-dev-effectiveness init-judge