OpenAI Developers for Claude Code is a Claude Code plugin bundle that connects OpenAI developer documentation and workflow guidance to Claude Code. It helps users build AI applications, agents, and ChatGPT Apps from Claude Code, including setup, troubleshooting, Agents SDK development, and app submission. The catalogue entries are the plugin's skills, plugin metadata, and documentation MCP integration.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add openai/openai-developers-for-claude --skill openai-docsgit clone --depth 1 https://github.com/openai/openai-developers-for-claudeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/openai/openai-developers-for-claude/openai-docs)<a href="https://agentmods.dev/skills/openai/openai-developers-for-claude/openai-docs"><img src="https://agentmods.dev/badge/skills/openai/openai-developers-for-claude/openai-docs/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/openai/openai-developers-for-claude/openai-docs"><img src="https://agentmods.dev/badge/skills/openai/openai-developers-for-claude/openai-docs.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00054 | $0.01315 |
| Opus 5.5 | $0.00022 | $0.00526 |
| Sonnet 5.5 | $0.00011 | $0.00263 |
| Haiku 4.5 | $0.00005 | $0.00131 |
Grade A, and why
openai-docs scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenAI Docs
Use the plugin-provided OpenAI Docs MCP server at https://developers.openai.com/mcp first for current OpenAI developer guidance. This skill also owns model selection, API model migration, and prompt-upgrade guidance.
API Key Setup
For requests that require a live OpenAI API call, use openai-platform-api-key first when available. Missing credentials block only the live call. They do not block requested source or configuration edits, documentation retrieval, or offline, mocked, static, fixture-based, or syntax validation.
Use this skill directly for docs-only questions, citations, model/API guidance, and examples that do not require a live API call.
For model migration or prompting-guidance requests, retrieve the relevant current guidance before any live-call credential check. Continue with requested source edits and offline validation when no API key is available; skip only validation that actually sends a live API request, and say that it was skipped.
First Action for Model Requests
Before inspecting a repository or checking API credentials, classify the model target:
- Latest/current/newest/recommended/default/flagship/unspecified target: fetch
https://developers.openai.com/api/docs/guides/latest-model.mdthrough Docs MCP. Read itslatestModelInfoblock and follow the exactmigrationGuideandpromptingGuideURLs it provides. Do not infer those URLs or the current model. - Explicitly named target: preserve that model and fetch
https://developers.openai.com/api/docs/guides/latest-model?model=<requested-model>through Docs MCP. For example, use?model=gpt-5.6or?model=gpt-5.3-codex. Do not substitute the current model or guidance for a different model.
If a requested page is unavailable, contains only a title or no substantive body, retry the exact URL once through Docs MCP, then use a focused Docs MCP search for the same official guide. If the guide still cannot be retrieved, search only official OpenAI domains such as developers.openai.com and platform.openai.com. If that also fails, state what could not be verified and return bounded uncertainty. Never use bundled or remembered model facts as a fallback.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 66 lines · 54 tokens per session scan A d1c694c11875
openai-docs is a skill published in the GitHub repository openai/openai-developers-for-claude (38 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 54 tokens to every session and 1,315 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file…