databricks-mlflow-evaluation
databricks-mlflow-evaluation is an LLM evaluation & observability skill for AI agents, published by databricks in the databricks-agent-skills repository on GitHub. Skillspace rates databricks-mlflow-evaluation D (67/100) and it passed the safety scan; the rating is based on static analysis only.
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
- Grade
- D
- Quality
- 67 / 100
- Safety
- Passed
- GitHub stars
- 209
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 94 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- Low-risk patterns
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 3 other repositories, in 1 collections
- Scope
- Vendor-specific: built around one product. MLflow 3 GenAI evaluation APIs on Databricks
What the scanner flagged
- mentions secrets in text
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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