Systematic evaluation of LLM applications using automated metrics, human feedback, and statistical testing.
Automate repetitive workflows and reduce manual effort
Example: Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Learn new skills, understand complex topics, get expert guidance
Example: Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Enhance output quality through reviews, suggestions, and refinements
Example: Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Time estimate: 15-45 minutes depending on use case complexity
Prerequisites
Submit your Claude Code skill and start earning
epx add wshobson/agents --skill llm-evaluationCompatible with
Supported agents
Covers three evaluation approaches: automated metrics (BLEU, ROUGE, BERTScore, accuracy, precision/recall), human evaluation across dimensions like accuracy and coherence, and LLM-as-Judge for pointwise, pairwise, and reference-based scoring
Includes implementations for text generation, classification, and retrieval (RAG) evaluation with ready-to-use metric functions and custom me
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionllm-evaluationExecute the skills CLI command in your project's root directory to begin installation:
epx add wshobson/agents --skill llm-evaluationFetches llm-evaluation from wshobson/agents and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
.cursor/skills/llm-evaluation
Restart Cursor to activate llm-evaluation. Access via /llm-evaluationin your agent's command palette.
Learn more:View source on GitHubSkills CLI docsAbout CursorWhat are agent skills?
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
mobile-android-design
wshobson/agents · Frontend · same repo
python-performance-optimization
wshobson/agents · Backend · same repo
unity-ecs-patterns
wshobson/agents · Productivity · same repo
rust-async-patterns
wshobson/agents · Backend · same repo
llm-council
am-will/codex-skills · AI/ML · tag: llm
karpathy-guidelines
unknown/karpathy-guidelines · code · tag: llm
Registry listing for llm-evaluation matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: llm-evaluation is focused, and the summary matches what you get after install.
We added llm-evaluation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
llm-evaluation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
llm-evaluation reduced setup friction for our internal harness; good balance of opinion and flexibility.
llm-evaluation has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in llm-evaluation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
llm-evaluation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
I recommend llm-evaluation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Useful defaults in llm-evaluation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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