llm-evaluation
About
The llm-evaluation skill enables developers to implement comprehensive testing for LLM applications using automated metrics, human feedback, and benchmarking. It is used to systematically measure performance, compare models and prompts, and detect regressions before deployment. This helps establish baselines, validate improvements, and build confidence in production AI systems.
Quick Install
Claude Code
Recommendednpx skills add camoneart/claude-code -a claude-code/plugin add https://github.com/camoneart/claude-codegit clone https://github.com/camoneart/claude-code.git ~/.claude/skills/llm-evaluationCopy and paste this command in Claude Code to install this skill
GitHub Repository
Related Skills
evaluating-llms-harness
TestingThis Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.
cloudflare-cron-triggers
TestingThis skill provides comprehensive knowledge for implementing Cloudflare Cron Triggers to schedule Workers using cron expressions. It covers setting up periodic tasks, maintenance jobs, and automated workflows while handling common issues like invalid cron expressions and timezone problems. Developers can use it for configuring scheduled handlers, testing cron triggers, and integrating with Workflows and Green Compute.
webapp-testing
TestingThis Claude Skill provides a Playwright-based toolkit for testing local web applications through Python scripts. It enables frontend verification, UI debugging, screenshot capture, and log viewing while managing server lifecycles. Use it for browser automation tasks but run scripts directly rather than reading their source code to avoid context pollution.
finishing-a-development-branch
TestingThis skill helps developers complete finished work by verifying tests pass and then presenting structured integration options. It guides the workflow for merging, creating PRs, or cleaning up branches after implementation is done. Use it when your code is ready and tested to systematically finalize the development process.
