Docs Organizationv1.1.0

A solo developer or small team is starting a greenfield project and needs to establish a documentation structure that won't become unmanageable as the project grows. They use this skill to pick the right size template and create an initial CLAUDE.md that stays within recommended line limits while giving AI coding agents the context they need.

An engineering team's CLAUDE.md or AGENTS.md has grown past 500 lines because every developer kept appending notes, burning tokens on every AI conversation. The team uses this skill to audit, deduplicate, and split the file into a lean root file plus well-organized docs/ subdirectories.

A platform team manages an umbrella workspace holding multiple independent git repos and needs to clarify which documentation belongs at the workspace level versus inside each repo. The skill helps them enforce cross-repo-only docs at the umbrella level and place repo-specific guides inside their respective repositories.

A developer inherits a messy project root littered with screenshots, debug logs, chat exports, and temp markdown files, making the actual documentation impossible to find. They apply the Step 0 noise audit to relocate or delete clutter into archive folders before reorganizing the real documentation structure.

A startup scaling from five to twenty engineers needs a predictable place for design specs, runbooks, ADRs, and implementation plans so new hires can find answers without grepping. The skill provides the Large Project template with frozen plans/design folders and a docs/README.md map.

Individual developers maintaining open source repositories who need clear contributor-facing documentation that separates human-readable READMEs from AI agent context files. Good doc organization reduces maintainer burden and improves contributor onboarding quality.

Consulting firms that onboard client codebases repeatedly and need a repeatable documentation organization methodology to apply across engagements. Standardized doc structures reduce ramp-up time for each new client project and improve deliverable quality.

Companies building AI coding assistants or agent platforms where context window efficiency directly impacts product cost and output quality. Internal adoption of doc organization best practices reduces token spend and improves agent response accuracy for customers.