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fidgetcodingfidgetcoding/fidgetflo/.agents/skills/embeddings/SKILL.md

embeddings

Skill1 starsChanged 7 months agoMITView on GitHub

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.

7 other projects use a version of this, 5 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. 2 are common, 0 are distinctive, and 0 are very specific to this project.

  • 2

    Common

    Most of the 7 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Common:

    Use HNSW for large pattern databases

    line 66
  2. Common:

    Use hyperbolic for hierarchical relationships

    line 68

“Common” is measured: kept by at least 6 in 10 of the 7 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Embeddings Skill

Purpose

Vector embeddings for semantic search and pattern matching with HNSW indexing.

Features

FeatureDescription
sql.jsCross-platform SQLite persistent cache (WASM)
HNSW150x-12,500x faster search
HyperbolicPoincare ball model for hierarchical data
NormalizationL2, L1, min-max, z-score
ChunkingConfigurable overlap and size
75x fasterWith agentic-flow ONNX integration

Commands

Initialize Embeddings

npx fidgetflo embeddings init --backend sqlite

Embed Text

npx fidgetflo embeddings embed --text "authentication patterns"

Batch Embed

npx fidgetflo embeddings batch --file documents.json

Semantic Search

npx fidgetflo embeddings search --query "security best practices" --top-k 5

Memory Integration

# Store with embeddings
npx fidgetflo memory store --key "pattern-1" --value "description" --embed

# Search with embeddings
npx fidgetflo memory search --query "related patterns" --semantic

Quantization

TypeMemory ReductionSpeed
Int83.92xFast
Int47.84xFaster
Binary32xFastest

Best Practices

  1. Use HNSW for large pattern databases
  2. Enable quantization for memory efficiency
  3. Use hyperbolic for hierarchical relationships
  4. Normalize embeddings for consistency

Download now (ZIP)

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