diff --git a/chroma/SKILL.md b/chroma/SKILL.md
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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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diff --git a/chromy/SKILL.md b/chromy/SKILL.md
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+---
+name: chromy
+description: This skill provides access to a RAG-like context enhancer that uses Chromadb locally.
+---
+
+# Chromy
+
+Whenever the user asks to "use chromy", you should invoke `chromy`, which is a cli tool to perform RAG search.
+The tool should be available in the `$PATH` as `chromy`.
+
+You have access to these commands:
+
+- `$ chromy lc` -> Lists the existing collections.
+- `$ chromy q <collection> <query>` -> Performs a query. Be sure to quote the `<query>` if this is composed by multiple words.
+
+Then use the response from Chromy to enhance the context and give the user a refined response.
+
+## A note on file sources
+
+The Chromy 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 Chromy's metadatas) the information is coming.
+
+## Example use case
+
+**START**
+
+User query:
+
+> Search in Chromy information about lovecraft's Dunwich horror.
+
+Step 1: Get the available collections with `chromy 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 `chromy 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**