mlflow-evaluation
MLflow 3 GenAI evaluation for agent development. Use when (1) writing mlflow.genai.evaluate() code, (2) creating @scorer functions, (3) building evaluation datasets from traces, (4) using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), (5) analyzing traces for latency/errors/architecture, (6) optimizing agent context/prompts/token usage, (7) debugging evaluation failures. Covers the full eval workflow: trace analysis -> dataset building -> scorer creation -> evaluation execution.
npx skills add majiayu000/claude-skill-registry --skill mlflow-evaluation-datasciencemonkey-claude-code-cli-bric-2 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# MLflow 3 GenAI Evaluation ## Before Writing Any Code 1. **Read GOTCHAS.md** - 15+ common mistakes that cause failures 2. **Read CRITICAL-interfaces.md** - Exact API signatures and data schemas ## End-to-End Workflows Follow these workflows based on your goal. Each step indicates which reference files to read. ### Workflow 1: First-Time Evaluation Setup For users new to MLflow GenAI evaluation or setting up evaluation for a new agent. | Step | Action | Reference Files | |------|--------|-----------------| | 1 | Understand what to evaluate | `user-journeys.md` (Journey 0: Strategy) | | 2 | Learn API patterns | `GOTCHAS.md` + `CRITICAL-interfaces.md` | | 3 | Build initial dataset | `patterns-datasets.md` (Patterns 1-4) | | 4 | Choose/create scorers | `patterns-scorers.md` + `CRITICAL-interfaces.md` (built-in list) | | 5 | Run evaluation | `patterns-evaluation.md` (Patterns 1-3) | ### Workflow 2: Production Trace -> Evaluation Dataset For building evaluation datasets from production traces. | Step | Action | Reference Files | |------|--------|-----------------| | 1 | Search and filter traces | `patterns-trace-analysis.md` (MCP tools section) | | 2 | Analyze trace quality | `patterns-
- Before Writing Any Code
- End-to-End Workflows
- Workflow 1: First-Time Evaluation Setup
- Workflow 2: Production Trace -> Evaluation Dataset
- Workflow 3: Performance Optimization
- Workflow 4: Regression Detection
- Workflow 5: Custom Scorer Development
- Reference Files Quick Lookup
- Critical API Facts
What does the mlflow-evaluation skill do?
MLflow 3 GenAI evaluation for agent development. Use when (1) writing mlflow.genai.evaluate() code, (2) creating @scorer functions, (3) building evaluation datasets from traces, (4) using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), (5) analyzing traces for latency/errors/architecture, (6) optimizing agent context/prompts/token usage, (7) debugging evaluation failures. Covers the full eval workflow: trace analysis -> dataset building -> scorer creation -> evaluation execution.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill mlflow-evaluation-datasciencemonkey-claude-code-cli-bric-2 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.