chroma-local
Installation
SKILL.md
Instructions
Determine these before writing code. Prefer discovering them from the repo and the user request. Ask only when the choice materially changes the implementation.
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Runtime shape
- Are they connecting to a running local server, embedding Chroma into tests, or setting up local development from scratch?
- Decide whether they need
chroma run, a Docker or service command,HttpClientorChromaClient, or PythonEphemeralClient.
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Persistence
- Persistent local data: choose an intentional data path.
- Disposable test data: use defaults or a temp directory.
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Embedding model
- Reuse the app's existing embedding provider when possible.
- Otherwise default to
@chroma-core/default-embedin TypeScript or the standard local default in Python. - If the user explicitly wants OpenAI embeddings in TypeScript, install and use
@chroma-core/openai.
-
Indexed data shape
- Determine what is being indexed, how it should be chunked, and what metadata is needed for filtering and updates.
Related skills
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chroma
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113chroma-cloud
Provides expertise on Chroma Cloud integration for semantic search and hybrid search applications. Use when the user is working with Chroma Cloud, CloudClient, managed collections, Schema(), Search(), hybrid search, or Chroma Cloud CLI workflows.
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