What is the primary purpose of Compound Learning?

It turns each development cycle into accumulated project knowledge by maintaining a CLAUDE.md file that Claude reads at the start of every session to understand your specific codebase requirements.

How do I add new learnings to my project?

You can use the /ct:compound command to append new, dated, and actionable insights to your project's memory file following the recommended specific and actionable format.

How does it handle conflicting information?

The skill employs a contradiction resolution pattern where old entries are struck through or updated with new dates and context to reflect the evolving state of the project.

What happens if CLAUDE.md becomes too large?

The skill provides guidelines for splitting documentation into topic-specific files within a .claude/docs/ directory while keeping the main CLAUDE.md as a high-level index.

How does this help prevent recurring bugs?

By documenting a 'Recurring Bugs' section with specific error messages and their corresponding fixes, the AI can proactively recognize and avoid known pitfalls in your codebase.

Compound Learning & Project Memory

Name: Compound Learning & Project Memory
Author: tbdng

bytbdng

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Aprendizaje y Documentación

Manages and evolves project-specific knowledge in CLAUDE.md to improve AI performance over time.

Compound Learning is a specialized skill for Claude Code designed to build a persistent project memory by documenting technical patterns, successes, and failures in a standardized CLAUDE.md file. By capturing dated, actionable, and specific learnings after each development cycle, it ensures that Claude becomes increasingly context-aware and effective within a specific codebase. This system helps prevent recurring bugs, preserves architectural decisions, and maintains a high-signal documentation layer that scales as your project grows.

Características Principales

01Automated project memory updates via the /ct:compound command

02Scalable architecture for splitting large documentation into topic-specific files

03Standardized entry formatting for dated, specific, and actionable insights

04Structured organization for effective patterns, failed approaches, and recurring bugs

050 GitHub stars

06Intelligent maintenance including deduplication and contradiction resolution

Casos de Uso

01Recording complex library configurations to prevent future integration regressions

02Tracking failed technical experiments to avoid repeating unproductive development paths

03Documenting architectural patterns and coding standards for team-wide consistency

Características Principales

01Automated project memory updates via the /ct:compound command

02Scalable architecture for splitting large documentation into topic-specific files

03Standardized entry formatting for dated, specific, and actionable insights

04Structured organization for effective patterns, failed approaches, and recurring bugs

050 GitHub stars

06Intelligent maintenance including deduplication and contradiction resolution

Casos de Uso

01Recording complex library configurations to prevent future integration regressions

02Tracking failed technical experiments to avoid repeating unproductive development paths

03Documenting architectural patterns and coding standards for team-wide consistency