Agent skill · Databases

embedding

Standalone embedding service for semantic search. Runs as persistent FastAPI server for millisecond-latency embeddings. Supports model swapping via env vars. Use when you need vectors for any database (ArangoDB, Pinecone, etc).

majiayu000github.com/majiayu000GitHub ↗
claude-codecan modify filesMIT
Install
npx skills add majiayu000/claude-skill-registry --skill embedding --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

Facts
Files in the skill folder: 2
SKILL.md size: 4 KB
Bundled scripts: none
Allowed tools: BashWebFetch
Path: skills/ai-ml/embedding/SKILL.md
Open the folder on GitHub →
Where it comes from
Stars: 534
Language: HTML

Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.

From the SKILL.md

# Embedding Skill Standalone embedding service for semantic search across any database. ## Architecture ``` ┌─────────────────────────────────────────┐ │ embedding service (:8602) │ │ Model: EMBEDDING_MODEL env var │ │ Device: auto (CPU/GPU) │ └───────────────────┬─────────────────────┘ │ ┌───────────────┼───────────────┐ ▼ ▼ ▼ memory edge-verifier your-project skill searches ArangoDB/etc ``` ## Quick Start ```bash # Start the service (first run loads model ~5-10s) ./run.sh serve # Embed text (CLI) ./run.sh embed --text "your query here" # Embed via HTTP (after service is running) curl -X POST http://127.0.0.1:8602/embed -H "Content-Type: application/json" \ -d '{"text": "your query here"}' ``` ## Commands | Command | Description | | --------------------------------- | ------------------------------------------- | | `./run.sh serve` | Start persistent FastAPI server | | `./run.sh embed --text "..."` | Embed single text (uses service if running) | | `./run.sh embed --file input.txt` | Embed file contents | | `./run.sh info` | Show model, device, service status | ## Configuration | Variable | Default | Description | | ----------------------- | ----------------------- | --------------

What's inside
Steps it walks through
  1. Architecture
  2. Quick Start
  3. Commands
  4. Configuration
  5. Swapping Models
  6. API Endpoints
  7. POST /embed
  8. POST /embed/batch
  9. GET /info
  10. Integration Examples
  11. ArangoDB Semantic Search
  12. From Memory Skill
  13. Cold Start
Ships with 1 file
  • metadata.json
Commands it runs
Start the service (first run loads model ~5-10s)
Embed text (CLI)
Embed via HTTP (after service is running)
curl -X POST http://127.0.0.1:8602/embed -H "Content-Type: application/json" \
Use a different model for this project
export EMBEDDING_MODEL="nomic-ai/nomic-embed-text-v1"
Or for GPU-accelerated
export EMBEDDING_MODEL="intfloat/e5-large-v2"
export EMBEDDING_DEVICE="cuda"
export EMBEDDING_SERVICE_URL="http://127.0.0.1:8602"
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About this skill
What does the embedding skill do?

Standalone embedding service for semantic search. Runs as persistent FastAPI server for millisecond-latency embeddings. Supports model swapping via env vars. Use when you need vectors for any database (ArangoDB, Pinecone, etc).

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill embedding --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.

Where does this skill come from?

From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.

Is a popular skill a good skill?

Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.

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