openai-knowledge_skill

Overview

This skill helps you answer questions about the OpenAI API and platform by fetching authoritative, up-to-date documentation. It prefers using the OpenAI Developer Documentation MCP server to return exact doc text, schemas, examples, and limits rather than guessing. Use this skill when you need precise endpoint parameters, response schemas, rate limits, or platform feature details.

How this skill works

First, the skill checks whether the openaiDeveloperDocs MCP server tools are available and uses them when present. It performs a guided two-step workflow: search the docs to find the best page, then fetch the exact markdown or OpenAPI spec for accurate quoting. If MCP tools are not configured, the skill provides clear setup instructions and asks you to restart the Codex session so the tools can load.

When to use it

• You need exact endpoint parameters, schemas, or request/response examples for the Responses, Realtime, or other OpenAI APIs.
• You must confirm current rate limits, auth behavior, or model availability rather than relying on memory.
• You want to quote official developer docs or reproduce exact examples and edge-case guidance.
• You are implementing multi-agent workflows that depend on platform features (tools, streaming, MCP).
• You need authoritative guidance for troubleshooting integration or SDK usage.

Best practices

• Prefer the MCP server tools; search first, then fetch specific pages or anchors for precise text.
• When requesting schemas or endpoints, use the OpenAPI spec and endpoint list functions to avoid inventing fields or defaults.
• If MCP is unavailable, follow the provided CLI or config-file setup steps and restart Codex before asking for docs.
• Ask for the specific endpoint, model name, or feature and any relevant anchors to reduce fetch scope.
• Treat fetched markdown as the source of truth and paraphrase or quote it accurately.

Example use cases

• Verify the exact request body and response schema for the Responses API before implementing client code.
• Confirm streaming and realtime API behavior, example frames, and limits for a low-latency agent.
• Check current rate limits, model capabilities, or policy notes for production deployment planning.
• Fetch the OpenAPI spec to generate client code or validate request parameters automatically.
• Guide a user to enable the openaiDeveloperDocs MCP server when their environment lacks it.