gh:compound

To document solved problems and build a team knowledge base, this Skill coordinates multiple subagents to extract solutions and context, generating structured YAML-frontmatter documents in docs/solutions for future discovery and reuse.

The multi-agent workflow extracts solutions by orchestrating parallel subagents for context analysis, solution extraction, related docs finding, and session history review, assembling validated YAML-frontmatter documents categorized by problem type.

Yes, you can enrich problem solving documentation by integrating optional context from memory and prior sessions, which reduces duplication and adds valuable historical context to the newly created knowledge artifacts.

The best way to categorize bug fixes and knowledge tracks is by validating YAML frontmatter against a schema and assigning the problem_type metadata, sorting documents into category-based directories under docs/solutions.

To run the gh:compound knowledge management workflow, you need the gale-task, gale-memory, and gh dependencies installed to support subagent coordination, context integration, and document assembly.

A multi-agent approach surpasses manual documentation by automating context analysis and solution extraction in parallel, ensuring structured schema validation and comprehensive knowledge artifacts without manual formatting overhead.