cxm-neural-memory（CXM 神经记忆）

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.

🔒 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.

🛠️ 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.