Model Evaluation Suite Builder
$2.99OfficialBuild comprehensive model evaluation: benchmark selection, statistical significance, human evaluation protocols, and safety testing.
datamodel-evaluationbenchmarkssafety-testingstatistical-significance· by SkillingMain
What you get
- ✓9-step procedure
- ✓6 pitfalls to avoid
- ✓Installs into 6 tools
- Version
- v1 →
- Last updated
- today
- Length
- 3 min read
- Requires
- Best with a strong model (Claude Sonnet 4)
Works in: Claude Code, Codex, Cline, opencode, OpenClaw, Hermes · Handles multi-file projects
Preview
When to use
Use this skill when you need defensible evidence that a model is good — for a release decision, a model card, a procurement choice, or a research result. It covers benchmark selection, robust statistics, human evaluation, and safety/red-team testing for both traditional ML and LLMs. Reach for it whenever "it scored well on our internal set" is not enough.
Inputs to gather
- Model type and intended use cases
- Stakeholders and decisions the evaluation must inform (ship/no-ship, procurement, regulatory)
- Existing benchmarks in the domain and their known limitations
- Compute and human-eval budget
- Safety and policy requirements (toxicity, bias, security)
- Languages, mod
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