Free alternatives to llm-evaluation
50 free AI Skills apps to use instead, best rated first.
Top picks
- W
webapp-testing
LaunchNative Python Playwright scripts for testing local web applications with server lifecycle management. Includes with_server.py helper to automatically start and manage single or multiple servers before running automation scripts. Supports re
- C
content-research-writer
LaunchAssists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on
- O
openhue
Launchopenhue is an agent skill from the OpenClaw project, listed on skills.sh. It lets an agent control Philips Hue lights and scenes through a Hue Bridge with the OpenHue CLI: turn lights on and off, dim rooms, set scenes such as movie mode, an
- B
batch-grill-me
Launchbatch-grill-me is an agent skill from Matt Pocock's skills collection, listed on skills.sh. It interviews you relentlessly about a plan until you share an understanding. It maps decisions as a tree and asks every question it can answer now
- I
implement
Launchimplement is an agent skill from Matt Pocock's skills collection, listed on skills.sh. It implements work from a spec or tickets: it fetches the ticket, works test-first at agreed seams, runs type checks and tests as it goes, reviews the re
- F
frontend-design
LaunchDistinctive, production-grade frontend interfaces that reject generic AI aesthetics through intentional design choices. Guides aesthetic direction selection (brutalist, maximalist, retro-futuristic, luxury, organic, etc.) before implementat
More AI Skills apps
brainstorming
Transform rough ideas into fully-formed designs through structured questioning
finishing-a-development-branch
Guides completion of development work by presenting clear options
test-driven-development
Use when implementing any feature or bugfix, before writing implementation code
About llm-evaluation
llm-evaluation
Implement comprehensive evaluation strategies for LLM applications
llm-evaluation is an agent skill from the wshobson/agents collection for designing evaluation strategies for LLM applications. It combines automated metrics, human feedback and benchmarking to measure how well an application performs. Automated metrics are fast, repeatable and scalable, because they compute scores from outputs without a person reading each one. Human evaluation covers the qualities that metrics miss, and A/B testing shows how two versions of a prompt or model behave with real users. The skill begins with the core evaluation types and then covers how to build a test set, choose metrics that fit the task, and set baselines so later changes can be compared against where the system started. It applies when measuring LLM performance systematically, comparing models or prompts, detecting regressions before deployment, validating that a prompt change actually helped, building confidence in production systems, establishing baselines and tracking progress over time, and debugging unexpected model behavior. The guidance is meant to make model quality a measured quantity rather than a matter of impressions. The team still chooses the metrics and the test cases, because the right measures depend on what the application is for. The skill is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 12.4K installs and passing results from three security audits at the time of the scan.
- Price
- Free
- Made by
- wshobson
- Category
- AI Skills
- Website
- github.com/wshobson/agents/llm-evaluation