pitchdocs
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.
Best use case
pitchdocs is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using pitchdocs should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
How pitchdocs Compares
| Feature / Agent | pitchdocs | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
Related Skills
pitchdocs-suite
One-command generation and audit of the full public repository documentation set — README, CHANGELOG, ROADMAP, CONTRIBUTING, CODE_OF_CONDUCT, SECURITY, issue templates, PR template, and discussion templates. Use when setting up a new repo or auditing an existing one.
visual-standards
Visual formatting standards for repository documentation — emoji heading prefixes, horizontal rules, TOC anchors, callouts, screenshots (device dimensions, HTML patterns, captions, shadows), and image optimisation. Load when generating READMEs with visual elements or working with screenshots.
user-guides
Generates task-oriented user guides and how-to documentation for a repository. Creates docs/guides/ with step-by-step instructions for common workflows, integrations, and advanced usage. Links guides into README.md and CONTRIBUTING.md. Use when a project needs user-facing how-to documentation beyond the README quickstart.
roadmap
Generates ROADMAP.md from project milestones, issues, and boards (GitHub, GitLab, or Bitbucket). Structures content with mission statement, current milestone progress, upcoming milestones, and community involvement section. Use when creating or updating a project roadmap.
public-readme
Generates READMEs with the Daytona/Banesullivan marketing framework — hero section, benefit-driven features, quickstart, comparison tables, and compelling CTAs. Produces docs that sell as well as they inform. Use when creating or overhauling a project README.
platform-profiles
Platform-specific equivalents for GitLab and Bitbucket when generating repository documentation. Lookup tables for file paths, badges, Markdown rendering, CI/CD, and CLI tools. Load this skill when working on non-GitHub repos or generating cross-platform docs.
package-registry
Documentation guidance for projects published to npm and PyPI package registries. Covers metadata fields that affect registry pages, README cross-renderer compatibility, trusted publishing, provenance badges, and audit checks. Use when a project has package.json or pyproject.toml and is published publicly.
llms-txt
Generates llms.txt and llms-full.txt files following the llmstxt.org specification. Provides LLM-friendly content curation for AI coding assistants (Cursor, Windsurf, Claude Code) and AI search engines. Use when generating or updating llms.txt for a repository.
launch-artifacts
Transforms README and CHANGELOG into platform-specific launch content — Dev.to articles, Hacker News posts, Reddit posts, Twitter/X threads, and awesome list submission PRs. Keeps promotion tethered to code artifacts, not generic marketing. Use when launching or announcing a project release.
geo-optimisation
Generative Engine Optimisation (GEO) patterns for documentation that surfaces correctly in AI-generated answers — citation capsules, crisp definitions, atomic sections, comparison tables, statistics, and semantic scaffolding. Load when optimising docs for AI citation (ChatGPT, Perplexity, Google AI Overviews, Claude).
feature-benefits
Systematic codebase scanning for features and evidence-based feature-to-benefit translation. Extracts what a project does from its code and translates it into what users gain — generates features and benefits sections, "Why [Project]?" content, and feature audit reports. Use when writing a features table for a README, extracting features from code, auditing feature coverage, or answering "why should someone use this project?".
docs-verify
Validates documentation quality and freshness — checks for broken links, stale content, llms.txt sync, image issues, heading hierarchy, and badge URLs. Runs locally or in CI. Use to catch documentation decay before it reaches users.