llm-evaluation

github:wshobson/agents · first committed 2025-10-17 · source · badge

Implement comprehensive evaluation strategies for LLM applications using automated metrics, human feedback, and benchmarking. Use when testing LLM performance, measuring AI application quality, or establishing evaluation frameworks.

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Listed, not yet scored. This skill has no public model test yet. It is in the scoring queue; submitted skills go first, then the most-installed.

Version history

7 commits to this file on GitHub (newest first).

2026-05-22 · commit be57c0b · feat: multi-harness plugin marketplace (Codex, Cursor, OpenCode, Gemini) (#541)
2026-03-07 · commit 47a5dbc · fix(skills): remove phantom resource references and fix CoC links (#447)
2026-02-19 · commit 0865571 · chore: update model references to Claude 4.6 and GPT-5.2
2026-01-19 · commit 5684887 · style: format all files with prettier
2026-01-19 · commit 8be0e8a · feat(llm-application-dev): modernize to LangGraph and latest models v2.0.0
2025-11-17 · commit 1305e48 · Replace GPT and Claude models to latest, better and cheaper models (#118)
2025-10-17 · commit 65e5cb0 · feat: add Agent Skills and restructure documentation

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