emem-find-similar
emem-find-similar is a Geospatial & mapping skill for AI agents, published by Vortx-AI in the emem repository on GitHub. Skillspace rates emem-find-similar D (64/100) and it passed the safety scan; the rating is based on static analysis only.
Given a place name or cell64, return the top-K most similar places on Earth by cosine similarity over the 128-D Tessera foundation embedding. Use when the user asks for analogues, look-alikes, or counterparts ("find cities like Bangalore", "where else looks like the Sundarbans", "show me places with a similar urban canopy to Singapore"). Returns cell64s with scores, lat/lng, and cached place labels.
- Grade
- D
- Quality
- 64 / 100
- Safety
- Passed
- GitHub stars
- 45
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
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- Low-risk patterns
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- over-broad allowed-tools ('*')
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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