chromadb-embeddings
Store and query vector embeddings using ChromaDB at {{CHROMADB_HOST}}:{{CHROMADB_PORT}}.
Best use case
chromadb-embeddings is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Store and query vector embeddings using ChromaDB at {{CHROMADB_HOST}}:{{CHROMADB_PORT}}.
Teams using chromadb-embeddings should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/chromadb-embeddings/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How chromadb-embeddings Compares
| Feature / Agent | chromadb-embeddings | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Store and query vector embeddings using ChromaDB at {{CHROMADB_HOST}}:{{CHROMADB_PORT}}.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# ChromaDB Embeddings
ChromaDB is available at `http://{{CHROMADB_HOST}}:{{CHROMADB_PORT}}` within the Docker network.
## Create a Collection
```bash
curl -X POST "http://{{CHROMADB_HOST}}:{{CHROMADB_PORT}}/api/v1/collections" \
-H "Content-Type: application/json" \
-d '{"name": "my_collection", "metadata": {"hnsw:space": "cosine"}}'
```
## Add Documents
```bash
curl -X POST "http://{{CHROMADB_HOST}}:{{CHROMADB_PORT}}/api/v1/collections/{collection_id}/add" \
-H "Content-Type: application/json" \
-d '{"ids": ["doc1"], "documents": ["Hello world"], "metadatas": [{"source": "test"}]}'
```
## Query Similar Documents
```bash
curl -X POST "http://{{CHROMADB_HOST}}:{{CHROMADB_PORT}}/api/v1/collections/{collection_id}/query" \
-H "Content-Type: application/json" \
-d '{"query_texts": ["greeting"], "n_results": 5}'
```
## Tips for AI Agents
- ChromaDB can auto-generate embeddings if configured with an embedding function.
- Use metadata filters to narrow search results.
- Prefer cosine distance for normalized embeddings.Related Skills
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