embeddings
Vector embeddings with HNSW indexing, sql.js persistence, and hyperbolic support. 75x faster with agentic-flow integration. Use when: semantic search, pattern matching, similarity queries, knowledge retrieval. Skip when: exact text matching, simple lookups, no semantic understanding needed.
66
56%
Does it follow best practices?
Impact
74%
1.60xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./.agents/skills/embeddings/SKILL.mdEvaluation results
52%
1%Developer Documentation Search System
Semantic search initialization and querying
Init command used
58%
83%
SQLite backend specified
100%
100%
Embed or batch command used
25%
0%
Search command used
91%
16%
Search uses --query flag
100%
60%
Search uses --top-k flag
0%
0%
HNSW indexing mentioned
0%
100%
Normalization mentioned
0%
0%
Correct CLI tool
77%
88%
70%
Architecture Pattern Knowledge Store
Memory integration with embeddings
Memory store command used
100%
100%
Store uses --embed flag
0%
0%
Store uses --key flag
100%
100%
Store uses --value flag
100%
100%
Memory search command used
100%
100%
Search uses --semantic flag
0%
0%
Search uses --query flag
100%
100%
Correct CLI tool
100%
100%
100%
83%Memory-Efficient Document Embedding Pipeline for Edge Deployment
Batch embedding with quantization
Init with sqlite backend
0%
100%
Batch command used
0%
100%
Batch uses --file flag
0%
100%
Quantization applied
46%
100%
Quantization justified
60%
100%
Hyperbolic embedding chosen
0%
100%
Search command used
0%
100%
Does not use per-item embed loop
50%
100%
Correct CLI tool
0%
100%
- Repository
- ruvnet/ruflo
- Commit
398f7c2
- Evaluated
- Agent
- Claude Code
- Model
- Claude Sonnet 4.6
Table of Contents
Is this your skill?
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.