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πŸ¦€ ClawHub

CXM: Neural Memory for Agents

by @joeavaib

Use this skill when you need to understand the architecture of a codebase, perform semantic searches across files, map dependencies before refactoring, or in...

Versionv1.0.3
Downloads611
TERMINAL
clawhub install cxm-neural-memory

πŸ“– About This Skill


name: cxm-neural-memory description: Use this skill when you need to understand the architecture of a codebase, perform semantic searches across files, map dependencies before refactoring, or ingest non-code documentation into your context memory. It leverages the CXM (ContextMachine) tool to prevent context collapse.

CXM Neural Memory Skill

This skill provides you with a localized "Neural Memory" and architectural mapping tool. It allows you to find code semantically and map dependencies using bundled AST-parsing tools.

πŸ”’ Security & Transparency (Disclosure)

To ensure safe and transparent operation, be aware of the following behaviors:

  • Local Indexing: This skill performs recursive file reads within the project to build a local vector index (FAISS) stored in ~/.cxm.
  • Resource Footprint: Initial indexing is CPU-intensive. Runtime RAM usage ranges from ~300MB (Mini-BERT) to ~1GB (MPNet).
  • Network Access: On the very first execution, this skill will download a pre-trained model (~80MB to ~400MB) from the HuggingFace Hub. No project data is ever uploaded.
  • File Modification: The tool can patch files. It strictly respects the allowed_write_paths and mode (e.g., ask_first) defined in the project's .cxm.yaml.
  • πŸ› οΈ Local Engine Usage

    You are already bundled with the CXM source code. All commands must be executed via the local src/cli.py script.

    Crucial Instruction: Always use the --agent-mode flag to receive strict, parseable JSON.

    Core Capabilities & Usage

    1. Semantic Search (Vibe Searching)

    Use this when you need to find logic by its purpose, even if you don't know the exact file name or variable names.

    Command:

    python src/cli.py --agent-mode harvest --semantic "your natural language query"
    

    Interpretation: The JSON output contains a results array with path, content, and start_line/end_line for precise targeting.

    2. Dependency Graphing (Architectural Mapping)

    Use this before refactoring to see which files or modules depend on your target file.

    Command:

    python src/cli.py --agent-mode map path/to/file.py
    

    Interpretation: The JSON output includes an edges list and a hotspots array showing the most heavily used modules in the project.

    3. Architecture Ingestion

    Force CXM to index non-code files like README.md, docker-compose.yml, or package.json to understand the system's infrastructure.

    Command:

    python src/cli.py --agent-mode ingest .
    

    Workflow for Complex Refactoring

    1. Locate: Use semantic search to find the relevant code sections. 2. Map: Run map on the identified files to see the blast radius. 3. Execute: Apply your changes knowing the full architectural context.