Wscats

Wscats / who-is-actor

Public

把摸鱼的同事揪出来🤪

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15
4
89% credibility
Found Mar 17, 2026 at 15 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A skill that analyzes code project change history to create AI-generated profiles of developers' habits, efficiency, style, and engagement.

How It Works

1
👀 Discover the tool

You hear about a clever way to understand developers' work styles by reviewing their past code changes in a project.

2
📁 Pick your project

Choose the folder on your computer that holds the code project you want to explore.

3
👥 Choose who and when

Select specific team members or a time period to focus the insights just right.

4
🚀 Start the magic

Hit go, and it safely scans the change history to build smart profiles without any hassle.

5
⏳ Watch it work

In moments, it gathers patterns on habits, speed, and style using helpful AI thinking.

📊 Get your insights

Enjoy a clear report with ratings, comparisons, and tips to better appreciate each developer's strengths.

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Star Growth

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AI-Generated Review

What is who-is-actor?

This YAML-defined skill for AI runtimes analyzes Git commit history in a repo to profile developers—think "who is github actor" behind the commits, revealing habits like commit frequency, work hours, code churn, bug fixes, and an engagement index. Unlike casual searches for "who is actor ben foster" or "who is actor of thanos," it crunches data from git log using only standard Unix tools (git, awk, grep) with zero package installs, then feeds aggregated metrics to AI for a Markdown report on efficiency, style, and team dynamics. Point it at a repo path, filter by authors or dates, and get insights on slackers or stars without touching emails or secrets.

Why is it gaining traction?

Zero-dependency setup means instant use on any Unix-like machine—no npm or pip hell—plus strict read-only git commands and auto-redaction keep it secure and private. The hook is the fun, data-backed "engagement index" for spotting patterns like weekend warriors versus clock-watchers, with dry-run previews and cross-dev comparisons that beat manual git greps. Developers dig the AI commentary turning raw stats into actionable portraits, scoped for collab not HR spying.

Who should use this?

Team leads auditing open-source contributors or internal repos to benchmark productivity. Dev managers reviewing "who is actor of harry potter"-level commits from juniors versus seniors. OSS maintainers profiling forks before merges, especially with date ranges for sprint retros.

Verdict

Early stage at 15 stars and 0.9% credibility score, with solid docs but no tests visible—test on non-prod repos first. Worth a spin for git-heavy teams needing quick profiling without bloat.

(198 words)

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