rlm

Recursive Language Model patterns for Claude Code enable handling massive contexts (10M+ tokens) by treating them as external variables. Ideal for operations teams managing large datasets, it integrates with Claude Code to analyze extensive files without direct tool calls.

1. **Prepare Your Dataset**: Save your large file (e.g., JSON, CSV, or text) in a location accessible to Claude Code. Ensure it’s structured for analysis (e.g., rows/columns for tabular data). 2. **Load the Dataset**: Use the rlm command in Claude Code to treat the file as an external variable. Example: ``` rlm --file-path /path/to/large_dataset.json --context-size 10M ``` 3. **Define Your Analysis**: Craft a prompt that specifies the patterns or insights you need. Use placeholders like [SPECIFIC_PATTERNS] to guide the AI. Example: ``` Analyze the dataset for [ANOMALIES] in [TRANSACTION_COLUMN] and highlight [TRENDS] over [TIME_PERIOD]. ``` 4. **Review and Refine**: Examine the AI’s output for accuracy. If the results are too broad, refine your prompt by narrowing the focus (e.g., "Focus only on transactions >$500"). 5. **Export Insights**: Save the AI’s recommendations or summaries to a new file for further action. Use: ``` rlm --export /path/to/output.json ``` **Tip**: For best results, pre-filter your dataset to reduce noise before using rlm. Combine rlm with Claude Code’s built-in tools (e.g., `grep`, `jq`) for initial data cleaning.