Baseline validation and generation workflows are in place.
RepoDocs AI now validates lifecycle metadata, exports documentation into multiple publishing formats, and runs repository-native automation for documentation generation, analytics, and knowledge-graph indexing.
Validation
Quality gates
Frontmatter validation for templates and examples
Repository structure validation for required assets
Quality checks for unresolved placeholders in example and generated docs
OpenAPI schema-aware validation for example endpoint payloads and parameters
OpenAPI request-body validation against request schemas
Version-compatibility validation for API documents and generated examples
Deprecation and migration validation against OpenAPI lifecycle metadata
Markdown linting through npm validation
Automatic regeneration workflow when OpenAPI specs change on `main`
Generation
OpenAPI to Markdown
Local generator script for API overview and endpoint docs
Example OpenAPI spec in the repository
Manual GitHub Action for artifact generation
Automatic regeneration workflow for spec changes
Generated output folder suitable for review before publishing
MkDocs starter pack for docs-site adoption
Docusaurus starter pack for richer docs-site adoption
GitBook and Next.js starter packs for additional publishing models
Confluence, Google Docs, Notion, and PDF export pipelines
Documentation-agent, analytics, and knowledge-graph automation jobs
Hosted control plane with authenticated HTTP endpoints for automation runs
Queued execution so multiple automation requests can be accepted safely
Container packaging for hosted control-plane deployment
Next Automation Step
Move from repository automation to hosted orchestration.
The strongest next improvement is turning these repository-native jobs into a long-running service layer with live integrations and additional export targets.