Open-Source Tool Measures AI Coding Agent Autonomy with Local Data Analysis
What Codelens-AI Does
Codelens-AI is an open-source CLI tool that reads your local Claude Code session files and correlates them with git history. Instead of just tracking cost, it analyzes how the AI agent actually works by calculating autonomy metrics based on your usage patterns.
Key Metrics and Sample Results
The creator ran the tool on 30 days of personal usage and shared these results:
- Autopilot Ratio: 7.4x — For every message sent to Claude, the agent takes 7 actions
- Self-Heal Score: 1% — Out of 6,281 bash commands, only 50 were tests or lints
- Toolbelt Coverage: 81% — The agent uses most available tools (grep, read, write, bash, search)
- Commit Velocity: 114 steps/commit — It takes 114 tool calls to produce one commit
- Overall Autonomy Score: C (36/100)
Practical Impact and Usage
These metrics revealed that while the agent works hard (7.4x Autopilot Ratio), it rarely verifies its own work (1% Self-Heal Score). This insight prompted the creator to change their prompting strategy — they now explicitly tell Claude to run tests after every edit, which increased their Self-Heal Score from 1% to approximately 15% within a few days.
Setup and Data Privacy
The tool requires zero setup: npx claude-roi. All data stays local — it parses your ~/.claude/projects/ JSONL files plus git log. There's no cloud component and no telemetry.
Development Status and Community
The tool is actively seeking feature suggestions, issues, and PRs — particularly around the scoring formula and adding support for Cursor/Codex sessions. The creator is curious what scores other people get and whether others are running this tool.
GitHub: github.com/Akshat2634/Codelens-AI
Website: https://codelensai-dev.vercel.app/
📖 Read the full source: r/ClaudeAI
👀 See Also
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Qure is a desktop application from JetBrains (currently in closed beta) that generates end-to-end web test code from recordings made in its built-in browser. Instead of describing test flows in text for AI agents, developers record their manual QA scenarios by interacting with their product, and the AI produces working test code that matches their existing codebase.