About this skill
Vector similarity and distance algorithms for agent memory ranking. Cosine similarity, Euclidean distance, Manhattan distance, Jaccard, Hamming, and when to use each metric for different embedding types. Sources: mljs/distance.
Quick start
- Copy the install prompt. Use the green button above this README.
- Send it to your agent. Paste it into your coding agent’s chat and send.
- Review the setup. Your agent checks the skill’s instructions, requirements, and installation scope.
Review, license & listing details
This is an imported listing. We haven’t certified its safety, license, or compatibility. Skills may include executable scripts.
Only SKILL.md was scanned. Supporting files and scripts were not checked. No safety grade has been assigned.
0 potential findings · Scanned 11/4/2026 · Scanner 0.2.1
Content SHA-256: b94e4e26145129021a8fd4556354eafbb41a74e23b821a8d1448a6bc66bc010f
About this listing
- Category
- General
- Reported license
- Not specified
- Listing language (estimated)
- English
- Review status
- Not individually reviewed
- Agent compatibility
- Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.