How does this integrate with QA workflows?

It records QA pass rates, specific issues found, and links to screenshots for every test cycle, allowing agents to verify fixes against historical data.

How does this skill persist data across sessions?

The skill utilizes Docker MCP memory to store structured project state, ensuring that information remains available to agents even after a session is restarted.

What is the benefit of tracking architectural decisions?

Tracking decisions (ADRs) prevents AI agents from suggesting alternatives that have already been rejected and ensures new code aligns with previously established technical patterns.

Can this skill track multiple microservices simultaneously?

Yes, it is specifically designed to manage inventories for multiple services, including their respective API ports, Docker IPs, and database schemas.

Project Memory Tracking

Name: Project Memory Tracking
Author: usmanali4073

byusmanali4073

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Productivity & Workflow

Tracks project context, services, and decisions using Docker MCP memory for persistent agent context across development sessions.

About

Project Memory Tracking is a specialized skill for Claude Code designed to provide a persistent knowledge base for complex development workflows. By leveraging Docker MCP memory, it stores and retrieves crucial project metadata—including microservice inventories, API endpoints, database schemas, and architectural decisions—ensuring that AI agents maintain full context without needing to re-analyze the codebase. This skill facilitates seamless transitions between tasks such as feature implementation, QA testing, and issue resolution by maintaining a consistent, queryable record of the current project state and technical history.

Key Features

1 GitHub stars
Persistent storage of service inventories and API endpoint mappings
Comprehensive QA cycle results, screenshot logs, and issue tracking
Docker network configuration and container IP management
Detailed tracking of implemented features and UI components
Historical record of architectural decisions and technical rationale

Use Cases

Maintaining a living architectural record of microservice dependencies
Restoring full project context for agents at the start of a new session
Coordinating multi-agent workflows by sharing task and state data

About

Key Features

1 GitHub stars
Persistent storage of service inventories and API endpoint mappings
Comprehensive QA cycle results, screenshot logs, and issue tracking
Docker network configuration and container IP management
Detailed tracking of implemented features and UI components
Historical record of architectural decisions and technical rationale

Use Cases

Maintaining a living architectural record of microservice dependencies
Restoring full project context for agents at the start of a new session
Coordinating multi-agent workflows by sharing task and state data