ai-evals▌
refoundai/lenny-skills · updated Apr 8, 2026
MDX-style export adds YAML metadata + attribution linking explainx.ai and this canonical listing URL.
Systematic evaluation framework for AI products using practitioner-driven methodologies.
- ›Guides users through understanding what \"good\" looks like, designing rubrics and test cases, and implementing scoring criteria aligned with actual user needs
- ›Emphasizes manual review and error analysis as prerequisites to building meaningful evals, with structured workflows for clustering failure patterns
- ›Flags common pitfalls including vague criteria, LLM-as-judge without validation, and Liker
AI Evals
Help the user create systematic evaluations for AI products using insights from AI practitioners.
How to Help
When the user asks for help with AI evals:
- Understand what they're evaluating - Ask what AI feature or model they're testing and what "good" looks like
- Help design the eval approach - Suggest rubrics, test cases, and measurement methods
- Guide implementation - Help them think through edge cases, scoring criteria, and iteration cycles
- Connect to product requirements - Ensure evals align with actual user needs, not just technical metrics
Core Principles
Evals are the new PRD
Brendan Foody: "If the model is the product, then the eval is the product requirement document." Evals define what success looks like in AI products—they're not optional quality checks, they're core specifications.
Evals are a core product skill
Hamel Husain & Shreya Shankar: "Both the chief product officers of Anthropic and OpenAI shared that evals are becoming the most important new skill for product builders." This isn't just for ML engineers—product people need to master this.
The workflow matters
Building good evals involves error analysis, open coding (writing down what's wrong), clustering failure patterns, and creating rubrics. It's a systematic process, not a one-time test.
Questions to Help Users
- "What does 'good' look like for this AI output?"
- "What are the most common failure modes you've seen?"
- "How will you know if the model got better or worse?"
- "Are you measuring what users actually care about?"
- "Have you manually reviewed enough outputs to understand failure patterns?"
Common Mistakes to Flag
- Skipping manual review - You can't write good evals without first understanding failure patterns through manual trace analysis
- Using vague criteria - "The output should be good" isn't an eval; you need specific, measurable criteria
- LLM-as-judge without validation - If using an LLM to judge, you must validate that judge against human experts
- Likert scales over binary - Force Pass/Fail decisions; 1-5 scales produce meaningless averages
Deep Dive
For all 2 insights from 2 guests, see references/guest-insights.md
Related Skills
- Building with LLMs
- AI Product Strategy
- Evaluating New Technology
How to use ai-evals on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add ai-evals
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches ai-evals from GitHub repository refoundai/lenny-skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate ai-evals. Access the skill through slash commands (e.g., /ai-evals) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ 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.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★63 reviews- ★★★★★Ira Nasser· Dec 20, 2024
Keeps context tight: ai-evals is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Jin Bhatia· Dec 16, 2024
We added ai-evals from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ira Tandon· Nov 11, 2024
ai-evals is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Min Martinez· Nov 7, 2024
ai-evals fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Min Rahman· Oct 26, 2024
ai-evals has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Noah Tandon· Oct 2, 2024
Solid pick for teams standardizing on skills: ai-evals is focused, and the summary matches what you get after install.
- ★★★★★Mateo Reddy· Sep 25, 2024
Registry listing for ai-evals matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Zaid Thomas· Sep 25, 2024
Keeps context tight: ai-evals is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Omar Agarwal· Sep 9, 2024
Useful defaults in ai-evals — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Noah Thompson· Sep 9, 2024
ai-evals has been reliable in day-to-day use. Documentation quality is above average for community skills.
showing 1-10 of 63