PitchDocs — AI Documentation Plugin

Description

--- name: pitchdocs description: Generate marketing-quality repository documentation from codebase analysis. Scans 10 signal categories, extracts features with file-level evidence, and produces README, CHANGELOG, ROADMAP, and 15+ more docs. Zero runtime dependencies. For AI context file management, see ContextDocs. version: "2.1.0" author: Little Bear Apps tags: - documentation - readme - changelog - marketing - quality-scoring - claude-code-plugin --- # PitchDocs — AI Documentation Plugin ## Overview PitchDocs is a pure Markdown Claude Code plugin that scans any codebase and generates professional, marketing-ready repository documentation. Every feature claim traces to an actual file path — no hallucinated marketing copy. 15 skills, 16 slash commands (14 active + 2 stubs), 4 agents (3 pipeline + 1 per-project freshness checker), 1 auto-loaded rule + 2 installable rules, 1 opt-in hook. 100% Markdown, zero runtime dependencies, MIT licensed. ## When to Use - Starting a new open-source project and need professional docs fast - Overhauling an existing README that undersells your project - Preparing for a public launch or Product Hunt submission - Auditing documentation completeness across 20+ files - Creating changelogs, roadmaps, or user guides from existing code and git history ## Instructions 1. Install the plugin: ``` /plugin marketplace add littlebearapps/lba-plugins /plugin install pitchdocs@lba-plugins ``` 2. Navigate to any project repository 3. Run commands: - `/pitchdocs:readme` — Generate a marketing-quality README - `/pitchdocs:docs-audit` — Audit documentation completeness (20+ file checklist) - `/pitchdocs:features` — Extract features with file-level evidence - `/pitchdocs:changelog` — Generate CHANGELOG from git history - `/pitchdocs:ai-context` — Stub: redirects to ContextDocs - `/pitchdocs:llms-txt` — Generate llms.txt for AI discoverability - `/pitchdocs:docs-verify` — Quality scoring (0-100) with link checking - `/pitchdocs:roadmap` — Generate ROADMAP from GitHub milestones - `/pitchdocs:user-guide` — Generate task-oriented user guides - `/pitchdocs:launch` — Generate launch and promotion content - `/pitchdocs:doc-refresh` — Refresh all docs after version bumps - `/pitchdocs:platform` — Detect hosting platform feature support - `/pitchdocs:visual-standards` — Visual formatting standards for docs - `/pitchdocs:geo` — GEO optimisation for AI citation readiness - `/pitchdocs:activate` — Install per-project rules, agent, and hook - `/pitchdocs:context-guard` — Stub: redirects to ContextDocs ## Output Format Each command produces Markdown files written directly to the repository. The orchestration agent follows a 4-step workflow: 1. **Discover** — Scan codebase across 10 signal categories 2. **Extract** — Identify features with file-level evidence, classify by tier (Hero/Core/Supporting) 3. **Write** — Generate documentation with benefit-driven language and GEO-optimised structure 4. **Validate** — Check quality against the 4-question test and doc standards ## Examples **Feature extraction output:** ``` Hero Feature: Evidence-based feature extraction Evidence: .claude/skills/feature-benefits/SKILL.md Benefit: Every feature claim traces to actual code — no hallucinated marketing copy Category: Confidence gained ``` **README generation produces:** - Hero section with one-liner + badges - "Why [Project]?" with problem/solution table - Quick start with Time to Hello World target - Features with emoji+bold+em-dash bullets - Comparison table vs alternatives - Documentation links and contributing CTA ## Notes - Works with 9 AI tools: Claude Code, OpenCode, Codex CLI, Cursor, Windsurf, Cline, Gemini CLI, Aider, Goose - Cross-platform: GitHub, GitLab, and Bitbucket - GEO-optimised for AI citation (ChatGPT, Perplexity, Google AI Overviews) - Content filter mitigation built in for CODE_OF_CONDUCT, LICENSE, and SECURITY files - All knowledge stored as structured YAML+Markdown — no JavaScript, no Python, no build step

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