marrs#

Memory maintenance helper for any RAG/vector database.

Complete Setup Guide (so it works for any user)#

1. Install via ClawHub or copy the folder.
2. pip install requests (the only external dependency).
3. Edit scripts/config.py with your own RAG details (examples are placeholders only).
4. Create two scheduled jobs to run the monitor and defrag scripts (see your platform's cron/docs).
5. Test with the example in the Basic Usage section below.

Review the three small Python scripts before use — they are short and easy to audit.

Configuration ( scripts/config.py)#

python

RAG_URL = "http://your-rag-server:port"   # ← Replace with your own
DEFAULT_COLLECTION = "memory"             # Change to your main collection
MONITOR_INTERVAL_SECONDS = 300
DEFRAG_INTERVAL_SECONDS = 86400

Basic Usage#

python

from scripts.save_memory import save_memory

save_memory("Your content here", collection="your-collection")

What it contains#

• save_memory() helper that POSTs to your RAG /ingest endpoint
• Template scripts for monitor and defrag (background maintenance)
• Hot queue logic for fast retrieval of frequent items
• Configurable defaults (you must edit them)

No hardcoded paths, no internal systems, no credentials.

Security Notes#

• Only interacts with the RAG_URL you configure.
• Uses the 'requests' library (installed separately).
• Prints status messages to stdout (visible in your logs).
• The crons you create will run the scripts autonomously — only add them if you trust the code after review.
• Recommended: run the scripts in an isolated environment first.

This package is instruction + runnable scripts. The SKILL.md accurately describes everything it contains. No private data, no keys, no tokens, no names, no locations.

Version: 1.5.0 Status: Honest metadata, declared dependency, clear audit instructions. Clean for public use.