claude-cognitive

🥈Silver

@GMaN1911

Claude Cognitive provides persistent context and multi-instance coordination for Claude Code. It enables operations teams to maintain working memory across multiple instances, improving productivity and token optimization. Integrates with Python-based workflows and supports Claude agents.

446360Updated 1w ago

Intermediate30min to implementautomation

Saves ~180 min per use

Quick InstallView Source

git clone https://github.com/GMaN1911/claude-cognitive.git

Works with:

Claude

Overview

About This Skill

https://github.com/GMaN1911/claude-cognitive

How to Use

1. Identify your specific workflow and the type of data that needs persistent context. 2. Set up a Claude Cognitive instance through the Python SDK or API. 3. Configure the context window size based on your data complexity (start with 4096-8192 tokens). 4. Implement token optimization by compressing text data before storage (see example code). 5. Test with multiple instances to verify context persistence and synchronization. Tip: Start with a smaller context window and increase as needed to balance performance and memory retention.

Use Cases

Automatically maintain context across multiple Claude Code instances during long development sessions.

Reduce token usage by intelligently managing file context based on usage patterns.

Coordinate efforts between different instances to avoid duplicate work on shared codebases.

Quickly inject relevant code context based on recent interactions, improving initial response accuracy.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/GMaN1911/claude-cognitive

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I'm setting up a Claude Cognitive instance to manage [SPECIFIC_WORKFLOW]. I need help configuring the persistent context to maintain [TYPE_OF_DATA] across multiple sessions. Also, provide guidance on optimizing token usage for this use case. Include any relevant Python code snippets for integration.

Example Output

Based on your request to manage customer support ticket escalations, I've configured your Claude Cognitive instance with the following parameters:

1. Persistent Context Settings:
   - Context Window: 8192 tokens
   - Memory Retention: 7-day rolling window
   - Data Structure: JSON-based ticket objects with priority flags

2. Multi-Instance Coordination:
   - Instance Count: 3 (for load balancing)
   - Synchronization Interval: 5-minute syncs
   - Conflict Resolution: Last-write-wins with audit trail

3. Token Optimization:
   ```python
   def optimize_tokens(ticket_data):
       # Compress ticket descriptions using this algorithm
       compressed = compress_description(ticket_data['description'])
       ticket_data['description'] = compressed
       return ticket_data
   ```

4. Integration Code:
   ```python
   from claude_cognitive import CognitiveManager
   manager = CognitiveManager(api_key='your_key')
   manager.add_workflow('ticket_escalation', context_settings)
   ```

This configuration will maintain ticket context across instances while optimizing token usage by compressing text data. The system will automatically sync ticket statuses every 5 minutes.