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Eval Driven Development

Category: Science & Research  ·  Sub-category: math-stats  ·  Last updated:
Use when reasoning about building language-model-integrated systems by writing evaluations before and alongside the system: the statistical (not binary) nature of LLM evals, the five primitives (dataset, evaluation function, aggregation, iteration loop, regression budget), the judgment-mechanism taxonomy (programmatic, model-graded, human-graded, preference comparison), the difference between system-specific evals and canonical benchmarks (MMLU, HumanEval, BIG-bench, GAIA), how evals drive prompt/model/scaffolding/tooling changes, why Goodhart's Law means higher eval scores are not always improvements, and the offline-eval-vs-production-telemetry distinction. Do NOT use for deterministic unit testing (use testing-strategy), production monitoring (use evaluation or error-tracking), general-software TDD (use testing-strategy), or the construction of individual eval rubrics and task sets (use agent-eval-design — it owns construction; this skill owns the iteration discipline).

What this skill does

Eval Driven Development is a community-contributed Claude Code skill in the math-stats sub-category. It ships as a SKILL.md file that Claude Code auto-discovers under ~/.claude/skills/eval-driven-development/ and loads when your prompt matches the skill's trigger.

When to invoke it: Use when reasoning about building language-model-integrated systems by writing evaluations before and alongside the system: the statistical (not binary) nature of LLM evals, the five primitives (dataset, evaluation function, aggregation, iteration loop, regression budget), the judgment-mechanism taxonomy (programmatic, model-graded, human-graded, preference comparison), the difference between system-specific evals and canonical benchmarks (MMLU, HumanEval, BIG-bench, GAIA), how evals drive prompt/model/scaffolding/tooling changes, why Goodhart's Law means higher eval scores are not always improvements, and the offline-eval-vs-production-telemetry distinction. Do NOT use for deterministic unit testing (use testing-strategy), production monitoring (use evaluation or error-tracking), general-software TDD (use testing-strategy), or the construction of individual eval rubrics and task sets (use agent-eval-design — it owns construction; this skill owns the iteration discipline).

Who uses this skill

The Eval Driven Development Claude Code skill is built for researchers, data scientists, academics, and analysts working with complex data and scientific literature. It's part of ClaudSkills (also referred to as Claude Skills or Claude Code Skills) — the open community-curated registry of 69,000+ SKILL.md files for Anthropic's Claude Code agent and the wider Claude ecosystem (Claude API, Claude Agent SDK).

How to install

Free

Manual install (2 steps)

mkdir -p ~/.claude/skills/eval-driven-development
curl -L https://claudskills.com/skills/eval-driven-development/SKILL.md \
  -o ~/.claude/skills/eval-driven-development/SKILL.md

Or just download SKILL.md directly and drop it into ~/.claude/skills/eval-driven-development/. Claude Code auto-discovers it on next session.

Skills live at ~/.claude/skills/eval-driven-development/SKILL.md on macOS/Linux, or %USERPROFILE%\.claude\skills\eval-driven-development\SKILL.md on Windows. See the full install guide for step-by-step instructions.

Pro

One-click install via the desktop app

The ClaudSkills desktop app installs any skill directly into ~/.claude/skills/ with one click — no terminal required. Pro starts at $9/mo or $149 lifetime.

Pro

For the full experience including quality scoring and one-click install features for each skill — upgrade to Pro.

Attribution & license

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