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).
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryScience & researchStars0
About this skill
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).
Quick start
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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).
About this listing
Category
Science & research
Reported license
Not specified
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI