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Chroma

Checked
davila7·vlatest·ai-ml·MIT

Chroma generates creative color palettes based on a given word or phrase, boosting design inspiration and visual harmony.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill chroma -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

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No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned davila7/claude-code-templates at 79182c51fb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/rag-chroma/SKILL.md
Scanned
2 files, 408 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
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About this skill

What it does

Chroma is an open-source embedding database designed for building LLM (Large Language Model) applications that require memory. It allows you to store documents and their associated embeddings, along with metadata, enabling semantic search over those documents. Chroma facilitates the creation of Retrieval-Augmented Generation (RAG) pipelines by providing a local or self-hosted vector database solution.

When to use it

  • Building RAG applications where an LLM needs access to external knowledge.
  • When you require a locally hosted and managed vector database.
  • For prototyping LLM application components in notebooks.
  • Performing semantic search over collections of documents.
  • Storing embeddings alongside associated metadata for filtering and context.

Key capabilities

  • Collection Creation: Create named collections to organize your data.
  • Document Addition: Add documents, their corresponding embeddings (either auto-generated or custom), and associated metadata.
  • Semantic Search/Querying: Perform similarity searches using query texts and filter results based on metadata.
  • Metadata Filtering: Filter search results based on complex criteria defined within the where clause.
  • Custom Embedding Functions: Integrate with embedding models like OpenAI's text-embedding-3-small.

Example prompts

  • "Create a Chroma collection named 'my_collection'."
  • "Add two documents to the 'my_collection' along with metadata indicating their source."
  • "Perform a similarity search on 'my_collection' for documents related to 'machine learning tutorial', returning the top 5 results."

Tips & gotchas

  • You’ll need to install Chroma using pip install chromadb or npm install chromadb.
  • To use custom embeddings, you may need an API key (e.g., for OpenAI).
  • Chroma is designed as a self-hosted solution; consider alternatives like Pinecone if managed cloud scaling is required.