llm-evaluation
Implement comprehensive evaluation strategies for LLM applications
by wshobsonFree
Description
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.…
Highlights
- Automated metrics for fast, repeatable scoring
- Human evaluation and feedback
- A/B testing of prompts and models
- Baselines and regression detection
- Benchmarking guidance
- Installs with the skills CLI
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llm-evaluation FAQ
What does this skill help measure?
The performance of an LLM application, using automated metrics, human feedback, benchmarks and A/B tests.
Why set a baseline?
So later prompt or model changes can be compared against where the system started, and regressions are caught before deployment.
Are automated metrics enough?
The skill pairs them with human evaluation, because metrics miss some qualities.
Can it compare two prompts or models?
Yes. Comparing models or prompts is one of the uses it covers, including A/B testing.
How do I install it?
Run npx skills add https://github.com/wshobson/agents --skill llm-evaluation.
What security audits does it have?
skills.sh lists three audits: Gen Agent Trust Hub Pass, Socket Pass and Snyk Pass.
What are the best llm-evaluation alternatives?
Popular alternatives to llm-evaluation include phoenix-evals, bats-testing-patterns and e2e-testing-patterns. See all llm-evaluation alternatives