CodeCompanion Memory
Searches previous chat conversations using semantic search to recall past solutions, decisions, and context.
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Key features
- Retrieves past solutions, code patterns, and architectural decisions.
- Performs semantic search on chat history using natural language queries.
- Integrates with a local ChromaDB vector database for private, efficient lookups.
- Filters results by relevance and allows adjusting the number of results shown.
- Provides conversation previews or full summaries for detailed context.
Use cases
- Finding a solution to a recurring bug or error that was solved in a previous session.
- Recalling a specific architectural choice or database schema discussed weeks ago.
- Reusing a code pattern or implementation from a prior conversation.
FAQ
When should I use this skill?
Use this skill whenever context from past conversations would be helpful. This includes situations where you're facing a recurring problem, need to remember a past decision, or want to build upon a solution you've previously discussed with Claude.
How does this skill improve my workflow?
It saves you time by preventing you from re-solving old problems. By providing instant access to your project's history and past decisions, it ensures consistency and turns your chat logs into a searchable, personal knowledge base.
What capabilities does it provide?
CodeCompanion Memory features natural language semantic search, retrieval of past solutions and code snippets, relevance-based filtering, and seamless integration with a private, local ChromaDB vector database for secure and efficient lookups.
Is my conversation data kept private?
Yes, your data remains private. The skill integrates with a local ChromaDB instance running on your machine. Your chat history is indexed and searched locally, ensuring your conversations are never sent to an external service.
What does the CodeCompanion Memory skill do?
This skill gives Claude a long-term memory by allowing it to perform semantic searches on your previous chat conversations. It helps recall past solutions, code patterns, and architectural decisions using natural language queries.
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