ai-eval-design-and-iteration
Develop "quizzes" (evals) to measure model performance on specific tasks. Use these benchmarks to guide fine-tuning, determine product UX patterns, and track performance improvements over time. Use this when launching a new AI feature, switching between model versions, or optimizing for high-stakes accuracy.
npx skills add majiayu000/claude-skill-registry --skill ai-eval-design-and-iteration --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# AI Eval Design and Iteration In traditional software, inputs and outputs are defined. In AI, inputs and outputs are fuzzy. Evals (evaluations) are the "unit tests" for AI products. They allow you to move from "vibes-based" development to metric-driven iteration. By building a rigorous "quiz" for your model, you can determine exactly how capable your product is and where it requires human-in-the-loop scaffolding. ## The Eval Workflow ### 1. Identify "Hero Use Cases" Don't start with generic benchmarks (like MMLU). Instead, define the specific "hero" scenarios your product must master. - Identify the 10–20 most common or high-value queries users will give your model. - For each query, define what a "Perfect/Gold" answer looks like. - Include edge cases where you expect the model to struggle (e.g., complex reasoning or specific formatting). ### 2. Design the "Quiz" (The Eval) Create a set of tests to gauge how well the model knows the subject material. - **Input:** The specific prompt or instruction. - **Reference:** The "Gold" standard answer or a set of criteria (e.g., "Must mention X," "Must not exceed 200 words"). - **Scoring Mechanism:** Use a more powerful model (like O1 or GP
- The Eval Workflow
- 1. Identify "Hero Use Cases"
- 2. Design the "Quiz" (The Eval)
- 3. Apply the "Hill Climbing" Process
- 4. Determine UX Based on Accuracy Thresholds
- Examples
- Common Pitfalls
What does the ai-eval-design-and-iteration skill do?
Develop "quizzes" (evals) to measure model performance on specific tasks. Use these benchmarks to guide fine-tuning, determine product UX patterns, and track performance improvements over time. Use this when launching a new AI feature, switching between model versions, or optimizing for high-stakes accuracy.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ai-eval-design-and-iteration --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.