embedding-analysis
embedding-analysis is a Fine-tuning & training data skill for AI agents, published by mkurman in the zorai repository on GitHub. Skillspace rates embedding-analysis F (45/100) and the safety scan rated it risky; the rating is based on static analysis only.
Risky. This skill has patterns associated with unsafe behaviour. Read the source carefully before running it.
Compute and analyze embeddings for dataset quality, distribution comparison, semantic deduplication, diversity measurement, and similarity-based filtering. Covers sentence-transformers, embedding space diagnostics, and 2025-2026 literature techniques (NeMo Curator semantic dedup, embedding similarity metrics for data selection, DataRater-style quality scoring).
- Grade
- F
- Quality
- 45 / 100
- Safety
- Risky
- GitHub stars
- 320
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since.
Our review
- Architecture
- 96 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- High-risk patterns
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 1 collections
What the scanner flagged
- dynamic code execution (eval/exec)
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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