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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.

Install with Tessl CLI

npx tessl i github:ruvnet/claude-flow --skill embeddings
What are skills?

76

1.60x

Does it follow best practices?

Evaluation74%

1.60x

Agent success when using this skill

Validation for skill structure

SKILL.md
Review
Evals

Evaluation results

52%

1%

Developer Documentation Search System

Semantic search initialization and querying

Criteria
Without context
With context

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%

Without context: $0.6297 · 4m 36s · 35 turns · 74 in / 6,049 out tokens

With context: $0.9970 · 5m 42s · 53 turns · 52 in / 10,606 out tokens

70%

Architecture Pattern Knowledge Store

Memory integration with embeddings

Criteria
Without context
With context

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%

Without context: $0.7674 · 5m 57s · 41 turns · 46 in / 8,670 out tokens

With context: $0.5248 · 4m 11s · 35 turns · 67 in / 5,870 out tokens

100%

83%

Memory-Efficient Document Embedding Pipeline for Edge Deployment

Batch embedding with quantization

Criteria
Without context
With context

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%

Without context: $0.6554 · 4m 11s · 25 turns · 1,316 in / 12,550 out tokens

With context: $0.2632 · 1m 15s · 16 turns · 262 in / 3,652 out tokens

Evaluated
Agent
Claude Code
Model
Unknown

Table of Contents

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