Guide for ingesting the latest OpenAI TypeSpec specification into the openai-dotnet SDK. Use this when asked to update or ingest OpenAI API specs, copy base TypeSpec files from upstream, fix client TSP compile errors, or run code generation for new API areas.
npx skills add https://github.com/openai/openai-dotnet --skill ingesting-spec
This skill describes how to ingest the latest OpenAI TypeSpec specification (from the upstream microsoft/openai-openapi-pr repository) into the openai-dotnet SDK, area by area.
The process involves:
This skill is split across multiple files for easier navigation:
| Document | Description |
|----------|-------------|
| steps.md | Step-by-step process — the full 9-step workflow from copying spec to post-generation verification |
| file-locations.md | Key file locations — quick reference for all upstream and local paths, area mappings |
| patterns-and-gotchas.md | Common patterns & gotchas — lessons learned, pitfalls, and conventions to follow |
| checklist.md | Checklist — a task-by-task checklist for tracking progress during an ingestion |
| references.md | Reference PRs — detailed notes on past ingestion PRs with lessons learned |
Areas that can be ingested independently:
administration · assistants · audio · batch · chat · containers · conversations · embeddings · evals · files · fine-tuning · graders · images · models · moderations · realtime · responses · runs · threads · vector-stores · videos
@@clientLocation for every operation in the client TSP (the latest spec no longer uses interface blocks)specification/client/ instead[CodeGenType] stubs in src/Custom/{Area}/Internal/GeneratorStubs.cs for any renamed types./scripts/Invoke-CodeGen.ps1 to generate code (warnings are OK. Errors are not), then dotnet build to verify (Should be no warnings or errors)Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
Next.js best practices - file conventions, RSC boundaries, data patterns, async APIs, metadata, error handling, route handlers, image/font optimization, bundling
Use when starting feature work that needs isolation from current workspace or before executing implementation plans - creates isolated git worktrees with smart directory selection and safety verification
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