Agent skill · Databases

vector-database

Work with vector databases for RAG, embeddings, and semantic search using ChromaDB or similar. Use when building knowledge bases for PSI Engine or AI-powered search.

majiayu000github.com/majiayu000GitHub ↗
claude-codeMIT
Install
npx skills add majiayu000/claude-skill-registry --skill vector-database-lovedragonball-power-ranger-toolkit --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
Path: skills/ai-ml/vector-database-lovedragonball-power-ranger-toolkit/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

# 🧮 Vector Database Skill ## ChromaDB Setup ### Installation ```bash pip install chromadb ``` ### Initialize ```python import chromadb # Persistent storage client = chromadb.PersistentClient(path="./chroma_db") # In-memory (testing) client = chromadb.Client() # Get or create collection collection = client.get_or_create_collection( name="knowledge_base", metadata={"hnsw:space": "cosine"} ) ``` --- ## CRUD Operations ### Add Documents ```python collection.add( documents=["How to fix null pointer exception in Python"], metadatas=[{ "source": "agent_1", "category": "debugging", "language": "python", "date": "2026-01-14" }], ids=["doc_001"] ) ``` ### Query (Semantic Search) ```python results = collection.query( query_texts=["null reference error"], n_results=5, where={"category": "debugging"}, include=["documents", "metadatas", "distances"] ) # Results for i, doc in enumerate(results['documents'][0]): print(f"Score: {results['distances'][0][i]}") print(f"Doc: {doc}") ``` ### Update ```python collection.update( ids=["doc_001"], documents=["Updated solution for null pointer"], metadatas=[{"updated": True}] ) ``` ### Delete ```python collection.delete(ids=["doc_001"]) # or collection.dele

What's inside
Steps it walks through
  1. ChromaDB Setup
  2. Installation
  3. Initialize
  4. CRUD Operations
  5. Add Documents
  6. Query (Semantic Search)
  7. Update
  8. Delete
  9. RAG Pattern
  10. Custom Embeddings
  11. Filtering
  12. PSI Engine Integration
Ships with 1 file
  • metadata.json
Commands it runs
pip install chromadb
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About this skill
What does the vector-database skill do?

Work with vector databases for RAG, embeddings, and semantic search using ChromaDB or similar. Use when building knowledge bases for PSI Engine or AI-powered search.

How do I install it?

Run `npx skills add majiayu000/claude-skill-registry --skill vector-database-lovedragonball-power-ranger-toolkit --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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