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See AI-generated vs human-written code on GitHub PRs Agent Blame shows you which lines of code in a GitHub Pull Request were written by AI, so you can focus your code reviews where it matters. HOW IT WORKS • Orange gutter marks highlight AI-generated lines • Hover over marks to see prompt details (agent, model, and prompt text) • Unmarked lines are human-written (clean, distraction-free UI) • File badges show AI percentage per file • PR summary shows total AI vs human code stats REPOSITORY ANALYTICS • Full analytics dashboard in GitHub Insights → Agent Blame • Track AI vs human code trends over time • See tool breakdown (Cursor, Claude Code, OpenCode) • Model usage statistics • Per-contributor AI usage • Commit:Prompt efficiency ratio • Filter by time period (24h, 3 days, week, month, all time) FEATURES • Visual markers directly on GitHub PR diff views • Works in both unified and split diff modes • Supports light and dark themes • Zero performance impact on page load REQUIREMENTS • A GitHub Personal Access Token (with repo scope) • The agentblame CLI installed in your repositories to capture AI attribution GETTING STARTED 1. Install the extension 2. Click the extension icon 3. Add your GitHub token 4. Visit any PR with agentblame attribution data 5. Check Insights → Agent Blame for repository analytics PRIVACY • Your GitHub token is stored locally, never sent to external servers • No analytics, tracking, or data collection • Open source: github.com/mesa-dot-dev/agentblame
Feb 7, 2026
rating_count is the Chrome Web Store ratings count, not a written-review count.
Media assets
Screenshots and videos on the listing.
Has promo video
Whether the listing includes at least one video.
Languages
Declared language locales.
Developer website
Listing exposes a developer website URL.
Contact email
Listing exposes a contact email.
Keyword in name
Case-insensitive substring match in the name.
Keyword in description
Case-insensitive substring match in the description.
Keyword occurrences in description
Count of case-insensitive occurrences in the description.
Category user-count percentile
Share of same-category extensions with fewer users (null if unknown).
These are transparent listing completeness / keyword signals, not a prediction of Chrome Web Store search ranking.