Where is the documentation stored?

Documentation is stored within your repository in a structured directory format, typically under docs/solutions/ categorized by the specific problem type.

What is the Compound Docs skill?

It is a specialized capability for Claude Code that captures technical solutions immediately after a problem is solved, organizing them into a structured and searchable documentation system.

Should I use this for simple typo fixes?

No, the skill is designed for non-trivial problems, tricky debugging sessions, and non-obvious solutions that would benefit future development sessions.

Why does it use YAML frontmatter?

YAML frontmatter provides standardized metadata, allowing for fast lookups, consistent categorization, and the ability to programmatically search through documentation based on problem type or severity.

How does the skill detect when to document a problem?

It automatically triggers when it detects confirmation phrases like 'it's fixed' or 'working now,' but can also be manually invoked using the /doc-fix command.

Compound Documentation

Name: Compound Documentation
Author: RBozydar

byRBozydar

Learning & Documentation

Captures and organizes solved technical problems into a searchable, YAML-validated knowledge base to preserve institutional intelligence.

About

The Compound Docs skill automates the creation of high-quality technical documentation by extracting context from successful problem-solving sessions. It guides the AI through a structured 7-step process—from detecting a fix to validating metadata against a schema—ensuring that complex debugging insights and root causes are preserved in a categorized markdown format. By enforcing strict frontmatter standards and cross-referencing similar issues, it transforms ephemeral chat history into a permanent, searchable repository of solutions that prevents developers from solving the same problem twice.

Key Features

Categorized file organization based on problem types and symptoms
Automated detection of successful resolutions via natural language triggers
Strict YAML frontmatter validation for consistent metadata and searchability
Automated cross-referencing and critical pattern detection for recurring issues
Post-capture decision menu for promoting solutions to 'Required Reading' or new skills
0 GitHub stars

Use Cases

Building a team-wide 'Required Reading' repository for architectural anti-patterns
Documenting complex debugging sessions to prevent future session memory loss
Standardizing root cause analysis (RCA) capture within a project repository

About

Key Features

Categorized file organization based on problem types and symptoms
Automated detection of successful resolutions via natural language triggers
Strict YAML frontmatter validation for consistent metadata and searchability
Automated cross-referencing and critical pattern detection for recurring issues
Post-capture decision menu for promoting solutions to 'Required Reading' or new skills
0 GitHub stars

Use Cases

Building a team-wide 'Required Reading' repository for architectural anti-patterns
Documenting complex debugging sessions to prevent future session memory loss
Standardizing root cause analysis (RCA) capture within a project repository