hamelsmu

hamelsmu / evals-skills

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Skills for AI Evals to compliment the course: AI Evals For Engineers & PMs

maven.comparlance-labsevals?promoCode=evals-info-url
82
12
100% credibility
Found Mar 03, 2026 at 83 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

A set of guides for AI coding assistants to audit, analyze, and improve tests for language model projects.

How It Works

1
🔍 Discover helpful guides

You hear about simple tools that teach your AI helper how to check and improve AI projects while learning about testing AI.

2
📚 Get started if new

If you're just beginning, pick the main checking tool to review your AI test setup.

3
🔗 Connect the tools

Link these smart guides to your AI assistant with a quick setup, making it ready to assist.

4
✅ Run the review

Tell your AI helper to examine your project, spotting common issues and suggesting fixes.

5
📊 Review the findings

Get a clear report highlighting problems in order of importance, with tips on next steps.

6
🛠️ Fix and enhance

Use the recommended guides to correct issues, create better test examples, or build review tools.

🎉 Achieve strong results

Your AI projects now have reliable checks that catch mistakes and perform excellently.

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AI-Generated Review

What is evals-skills?

Evals-skills is a set of Claude skills evals plugins for AI coding agents like those in Claude Code or VSCode extensions, designed to audit and fix LLM evaluation pipelines. It tackles common pitfalls in building evals—think poor synthetic data or uncalibrated judges—by providing ready-to-run tools that guide agents through diagnostics, error analysis, and RAG evaluation. Install via simple bash-like plugin commands in Claude Code, then invoke skills like /evals-skills:eval-audit to get prioritized reports, complementing the AI Evals course for engineers and PMs.

Why is it gaining traction?

In a sea of generic GitHub skills creators and Copilot extensions, evals-skills stands out with battle-tested checks from 50+ companies, like generating diverse synthetic data or validating LLM judges against human labels. Developers hook into it fast: one audit skill spawns subagents for deep dives and suggests fixes using other skills, saving hours on eval debugging. Its tight integration with Anthropic's Claude skills ecosystem and GitHub agent workflows makes it a practical boost over ad-hoc prompting.

Who should use this?

AI engineers knee-deep in eval pipelines who use Claude Code or similar skills GitHub agents for RAG testing and judge calibration. PMs reviewing LLM traces for production evals, especially those supplementing the evals course. Teams blending GitHub skills sh with VSCode or Pages for custom annotation interfaces.

Verdict

Grab it if you're already in the Claude skills GitHub claude code flow—82 stars and 1.0% credibility score signal early-stage maturity with solid docs but unproven scale. Worth a test run for evals teams; extend it with custom skills for your stack.

(198 words)

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