Context-Scribe is a "Persistent Secretary" daemon designed to eliminate "Agent Amnesia." It monitors local AI session logs in the background, extracts long-term behavioral rules and project constraints, and professionally persists them to a centralized Memory Bank via the Model Context Protocol (MCP).

• 🔄 Zero-Touch Sync: Automatically extracts and persists rules natively from chat logs, entirely out of band.
• 📂 Project-Aware Routing: Intelligently detects the active project context and routes rules to either global/global_rules.md or [project-name]/rules.md.
• ⚖️ Strength Hierarchy: Enforces a "Project > Global" hierarchy. Project-specific rules always take precedence over global ones.
• 🧠 Intelligent Enhancement: Does not just copy-paste; the system professionalizes slang (e.g., "spongebob typing" -> "alternating uppercase") and adds concrete code examples.
• 🏗️ Semantic Categorization: Automatically organizes your Memory Bank into logical Markdown sections like # Style, # Architecture, and # Personal.
• 🛡️ Conflict Resolution: Implements "New-Trumps-Old" logic to autonomously resolve contradictions within the same scope.
• ⚡ Ultra-Fast Evaluation: Uses optimized, extension-free headless calls to gemini-2.5-flash-lite for near-instant extraction.

Context-Scribe is designed to be invisible. You do not need to use special "Rule" prefixes or commands. Simply interact with your AI agent as you normally would:

• "I prefer using single quotes for Python strings."
• "My name is Joao and I'm a senior engineer."
• "For this specific project, let's use Tailwind for styling."

The Evaluator will autonomously detect that these are long-term preferences, professionalize them, and commit them to your Memory Bank in the background.

Context-Scribe operates on an Observer-Evaluator-Bridge pattern. See the full architecture diagram and workflow details for more information.

1. Observer: A robust watchdog powered log monitor with manual periodic scanning ensuring zero missed events across various Gemini log formats.
2. Evaluator: Headless evaluation powered by your local gemini CLI (no extra API keys required).
3. Bridge: Direct MCP integration syncing data into @allpepper/memory-bank-mcp.

• Python 3.10+
• Gemini CLI: The daemon uses your local gemini CLI installation for headless evaluation.
• Memory Bank MCP: Your AI tool must have the @allpepper/memory-bank-mcp server configured.

1. Clone and Enter:

   git clone https://github.com/Joaolfelicio/context-scribe.git
   cd context-scribe

2. Environment & Dependencies:

   python3 -m venv .venv
   source .venv/bin/activate
   pip install -e ".[test]"

Start the daemon using the default Memory Bank location (~/.memory-bank):

context-scribe --tool gemini

If your AI tool's MCP server is configured to use a non-default root directory, you must pass that path to Context-Scribe:

context-scribe --tool gemini --bank-path "/path/to/your/custom-bank"

We maintain a high standard of quality with a hard requirement of at least 80% code coverage.

pytest --cov=context_scribe tests/

1. Start the daemon: context-scribe --tool gemini

2. Simulate an interaction:

   echo '[{"role": "user", "content": "Rule: Always use single quotes for Python strings."}]' > ~/.gemini/tmp/test_rule.json

3. Verify Result: Check your configured Memory Bank folder (e.g., ~/.memory-bank/global/global_rules.md). You should see the rule professionally enhanced and categorized.