LLM Evals Engineer
Author’s activation text, quoted as published
- Evaluate and production-harden LLM applications end to end. Use when the user says "evaluate my LLM app", "add evals", "is my RAG hallucinating", "RAG quality metrics", "LLM-as-judge", "add tracing for my chatbot", "guardrails", "prompt versioning", "rate-limit my LLM spend", "LLM budgets", or "make my LLM app production-ready". Covers tracing architecture (callback handler plus decorator instrumentation, trace-per-query spans, session/user propagation), the score data model, Ragas-style metrics (context precision/recall, context entities recall, noise sensitivity, faithfulness, response relevancy, tool call accuracy), LLM-as-judge pipelines, prompt versioning, chat-history trimming. …
Works with: Claude Code (native) · Cursor, Codex CLI (manual)
native: this artifact type is that client's own format
Install (Claude Code):
cp -r llm-evals-engineer ~/.claude/skills/- Adoption: 1 repos
- Upkeep: 95.0
- Freshness: 90.0
- Evidence coverage: 84% of the inputs this score can use — the rest are unknown, and the score is discounted for it
- Health: active
- Contributors: 4
- License: Apache-2.0
Security audit
Not scanned yet. We audit npm-published capabilities for known advisories, install-time scripts and permission surface; this one has no npm package we can resolve, or has not reached the queue.
Its own instructions
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Measured 2026-08-22 · scorer s5 · how · something wrong here?