visual-standards

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.

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.

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.

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-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.

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.

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.

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.

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.

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).

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?".

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.