Agent skill
openai-knowledge
Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.
Install this agent skill to your Project
npx add-skill https://github.com/openai/openai-agents-python/tree/main/.agents/skills/openai-knowledge
SKILL.md
OpenAI Knowledge
Overview
Use the OpenAI Developer Documentation MCP server to search and fetch exact docs (markdown), then base your answer on that text instead of guessing.
Workflow
1) Check whether the Docs MCP server is available
If the mcp__openaiDeveloperDocs__* tools are available, use them.
If you are unsure, run codex mcp list and check for openaiDeveloperDocs.
2) Use MCP tools to pull exact docs
- Search first, then fetch the specific page or pages.
mcp__openaiDeveloperDocs__search_openai_docs→ pick the best URL.mcp__openaiDeveloperDocs__fetch_openai_doc→ retrieve the exact markdown (optionally with ananchor).
- When you need endpoint schemas or parameters, use:
mcp__openaiDeveloperDocs__get_openapi_specmcp__openaiDeveloperDocs__list_api_endpoints
Base your answer on the fetched text and quote or paraphrase it precisely. Do not invent flags, field names, defaults, or limits.
3) If MCP is not configured, guide setup (do not change config unless asked)
Provide one of these setup options, then ask the user to restart the Codex session so the tools load:
- CLI:
codex mcp add openaiDeveloperDocs --url https://developers.openai.com/mcp
- Config file (
~/.codex/config.toml):- Add:
toml
[mcp_servers.openaiDeveloperDocs] url = "https://developers.openai.com/mcp"
- Add:
Also point to: https://developers.openai.com/resources/docs-mcp#quickstart
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
final-release-review
Perform a release-readiness review by locating the previous release tag from remote tags and auditing the diff (e.g., v1.2.3...<commit>) for breaking changes, regressions, improvement opportunities, and risks before releasing openai-agents-python.
examples-auto-run
Run python examples in auto mode with logging, rerun helpers, and background control.
implementation-strategy
Decide how to implement runtime and API changes in openai-agents-python before editing code. Use when a task changes exported APIs, runtime behavior, serialized state, tests, or docs and you need to choose the compatibility boundary, whether shims or migrations are warranted, and when unreleased interfaces can be rewritten directly.
docs-sync
Analyze main branch implementation and configuration to find missing, incorrect, or outdated documentation in docs/. Use when asked to audit doc coverage, sync docs with code, or propose doc updates/structure changes. Only update English docs under docs/** and never touch translated docs under docs/ja, docs/ko, or docs/zh. Provide a report and ask for approval before editing docs.
runtime-behavior-probe
Plan and execute runtime-behavior investigations with temporary probe scripts, validation matrices, state controls, and findings-first reports. Use only when the user explicitly invokes this skill to verify actual runtime behavior beyond normal code-level checks, especially to uncover edge cases, undocumented behavior, or common failure modes in local or live integrations. A baseline smoke check is fine as an entry point, but do not stop at happy-path confirmation.
test-coverage-improver
Improve test coverage in the OpenAI Agents Python repository: run `make coverage`, inspect coverage artifacts, identify low-coverage files, propose high-impact tests, and confirm with the user before writing tests.
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