How does Compound Docs detect when a problem is solved?

The skill monitors the conversation for confirmation phrases like 'that worked' or 'it's fixed,' and can also be manually invoked using the /doc-fix command.

Does it document every minor fix or typo?

No, the skill is configured to skip trivial syntax errors and focus on non-obvious solutions or complex debugging efforts that provide long-term value.

Where are the documented solutions stored?

Solutions are stored as Markdown files within a structured directory at docs/solutions/, organized into subcategories based on the problem type.

What is the 'Required Reading' feature?

It allows users to promote specific critical solutions to a pattern file that Claude reviews before generating new code, preventing the repetition of foundational mistakes.

Can I link related issues together?

Yes, the skill includes a cross-referencing step that searches for similar historical issues and adds links between the new documentation and existing records.

Compound Documentation Manager

Name: Compound Documentation Manager
Author: jjeremycai

byjjeremycai

Learning & Documentation

Automates the capture and categorization of solved coding problems into a searchable institutional knowledge base.

About

Compound Docs is a specialized skill designed to transform transient troubleshooting sessions into permanent institutional knowledge. It automatically detects when a technical problem has been resolved, gathers critical context—including root causes, environment details, and specific code fixes—and generates structured Markdown documentation. By utilizing validated YAML frontmatter and standardized categorization, it builds a searchable repository that prevents the recurrence of past bugs and allows users to promote non-obvious solutions to 'Required Reading' for future AI sessions.

Key Features

Automatic trigger detection for solved problems and verification phrases
Standardized categorization and file naming for organized knowledge storage
Post-capture decision menu for promoting solutions to critical patterns
Cross-referencing logic to link related historical issues and patterns
Validated YAML frontmatter for consistent metadata and high searchability
0 GitHub stars

Use Cases

Creating 'Required Reading' lists to improve AI accuracy in future coding sessions
Building a searchable technical debt and troubleshooting repository for complex modules
Standardizing documentation for non-obvious bug fixes across a development team

About

Key Features

Automatic trigger detection for solved problems and verification phrases
Standardized categorization and file naming for organized knowledge storage
Post-capture decision menu for promoting solutions to critical patterns
Cross-referencing logic to link related historical issues and patterns
Validated YAML frontmatter for consistent metadata and high searchability
0 GitHub stars

Use Cases

Creating 'Required Reading' lists to improve AI accuracy in future coding sessions
Building a searchable technical debt and troubleshooting repository for complex modules
Standardizing documentation for non-obvious bug fixes across a development team