Analyze repository structure, dependencies, and documentation needs

Recommend the best static site generator based on project analysis and user preferences

Generate configuration files for the selected static site generator

Create Diataxis-compliant documentation structure

Generate Playwright E2E test setup for documentation site (containers + CI/CD)

Set up deployment workflow for GitHub Pages or Vercel with deployment tracking and preference learning. Use the 'target' parameter to choose one deployment platform per project (github-pages or vercel). If you switch targets, the tool will warn you to remove the old workflow file.

[DEPRECATED — use deploy_site instead] Set up GitHub Pages deployment workflow. This alias will be removed in v1.1.0.

Verify and troubleshoot GitHub Pages deployment

Intelligently populate Diataxis documentation with project-specific content

Intelligently analyze and update existing documentation using memory insights and code comparison

Validate the accuracy, completeness, and compliance of generated Diataxis documentation

Validate general content quality: broken links, code syntax, references, and basic accuracy

Analyze repository and existing documentation to identify missing content and gaps

Test documentation build and local server before deploying to GitHub Pages

Evaluate README files for community health, accessibility, and onboarding effectiveness

Analyze README files against best practices checklist and generate templates for improvement

Comprehensive link checking for documentation deployment with external, internal, and anchor link validation

Generate standardized README templates for different project types with best practices

Validate README files against community best practices checklist with detailed scoring

Comprehensive README analysis with length assessment, structure evaluation, and optimization opportunities

Optimize README content by restructuring, condensing, and extracting detailed sections to separate documentation

Manage user preferences for documentation generation and SSG recommendations

Analyze deployment patterns and generate insights from historical deployment data

List files and directories within allowed roots. Use this to discover files without requiring full absolute paths from the user.

Automatically synchronize documentation with code changes using AST-based drift detection (Phase 3)

Watch code changes and trigger documentation drift detection in near real-time.

Record the host's judgment ('actionable' | 'noise' | 'deferred') for a previously-detected drift. Outcomes feed back into priority-scoring weights via the knowledge graph (ADR-012 Phase 4, Issue #114).

Generate context-aware documentation using AST analysis and knowledge graph insights (Phase 3)

Scan documentation directory for staleness markers and identify files needing updates based on configurable time thresholds (minutes, hours, days)

Validate documentation freshness, initialize metadata for files without it, and update timestamps based on code changes

Generate, validate, and manage sitemap.xml as the source of truth for documentation links. Sitemap.xml is used for SEO, search engine submission, and deployment tracking.

Generate a comprehensive LLM context reference file documenting all tools, memory system, and workflows for easy @ reference

Detect, classify, and clean up artifacts generated by AI coding agents (e.g., TODO.md, PLAN.md, agent markers, temporary files). Supports scan, clean, and archive operations with configurable patterns.

Aggregate cross-project signals from the Knowledge Graph and surface community-level insights: deployment success rates per SSG, common technology stacks, frequent drift sources, and project health distribution. All data is anonymized — no raw project paths are exposed.

Recall memories about a project or topic

Get intelligent analysis with patterns, predictions, and recommendations

Get enhanced recommendations using learning and knowledge graph

Get comprehensive learning and knowledge graph statistics

Query the knowledge graph for relationships and paths

Perform contextual memory retrieval with intelligent ranking

Manage multi-agent memory sharing and collaboration

Get insights and patterns from memory

Find similar projects from memory

Export memories to JSON or CSV

Clean up old memories

Intelligent memory pruning and optimization

Analyze temporal patterns and trends in memory data

Generate visual representations of memory data

Advanced memory export with multiple formats and options

Advanced memory import with validation and conflict resolution

Create and execute migration plans between different memory systems

Get comprehensive optimization metrics and recommendations