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+---
+name: chroma
+description: This skill provides access to a RAG-like context enhancer that uses Chroma locally.
+---
+
+# Chroma
+
+Whenever the user asks to "use chroma", you should perform a search on Chroma, which runs locally via a Python script.
+The working directory you must use is located in `~/code/chroma` and the entrypoint is `main.py`. The script **MUST** be invoked using `uv`.
+
+You have access to these commands:
+
+- `$ uv run main.py lc` -> Lists the existing collections.
+- `$ uv run main.py q <collection> <query>` -> Performs a query. Be sure to quote the `<query>` if this is composed by multiple words.
+
+Then use the response from Chroma to enhance the context and give the user a refined response.
+
+## A note on file sources
+
+The Chroma response returns the metadatas for the chunks it finds. Among these metadatas, there is `file_name`, which refers to the original file that was chunked and imported. **DO NOT ATTEMPT** to find or fetch these files. They most likely do not exist in the filesystem. You **SHOULD ALWAYS** however cite correctly from which files (**ONLY** from Chroma's metadatas) the information is coming.
+
+## Example use case
+
+**START**
+
+User query:
+> Search in chroma information about lovecraft's Dunwich horror.
+
+Step 1: Get the available collections with `uv run main.py lc`. The output is:
+
+```
+lovecraft
+documents
+```
+
+Most likely our information is in the `lovecraft` collection. We will use that for the query.
+
+Step 2: Query using `uv run main.py q lovecraft <query>`. The query *is up to you*, create one keeping into account that this is a raw query on a vector DB. Be concise, extract keywords, avoid noise.
+
+Step 3: Get the results, enhance the context, and respond to the user.
+
+**END**
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