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 agentmods add skills/zm1990s/kb-agent/openai-docsnpx skills add zm1990s/kb-agent --skill openai-docsgit clone --depth 1 https://github.com/zm1990s/kb-agentWrote 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/zm1990s/kb-agent/openai-docs)<a href="https://agentmods.dev/skills/zm1990s/kb-agent/openai-docs"><img src="https://agentmods.dev/badge/skills/zm1990s/kb-agent/openai-docs.svg" alt="Measured on agentmods" 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 | $0.00098 | $0.03885 |
| Opus 5 | $0.00049 | $0.01943 |
| Sonnet 5 | $0.00020 | $0.00777 |
| Haiku 4.5 | $0.00010 | $0.00388 |
Grade A, and why
openai-docs scanned grade A with 1 finding 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 4d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
4. Run the helper directly unless you need to override the cache dir. The helper falls back to `curl` when native `fetch` is unavailable or when proxy env vars are present, so no shell-specific proxy prefix is required. This is a copy
100% identical to openai-docs — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenAI Docs
Provide authoritative, current guidance from OpenAI developer docs using the developers.openai.com MCP server. "Docs MCP" means mcp__openaiDeveloperDocs__search_openai_docs and mcp__openaiDeveloperDocs__fetch_openai_doc; for API reference, schema, parameter, or required-field questions, also use mcp__openaiDeveloperDocs__get_openapi_spec when available. Official-domain web search is fallback after those tools are unavailable or unhelpful. Broad Codex questions use the manual helper before Docs MCP. This skill also owns model selection, API model migration, and prompt-upgrade guidance.
API Key Setup
For requests to build, run, configure, debug, or implement an API-backed app, script, CLI, generator, or tool, use openai-platform-api-key first when available. After that credential gate is resolved, return here for current docs as needed.
Use this skill directly for docs-only questions, citations, model/API guidance, conceptual explanations, and examples that do not require building or running an API-backed artifact.
Workflow Configuration
Source Priority
- For Codex self-knowledge, use the Codex source route below; it owns when to use the manual helper, Docs MCP, or bounded uncertainty.
- For non-Codex OpenAI docs questions, use
mcp__openaiDeveloperDocs__search_openai_docsto find the most relevant doc pages. - For non-Codex OpenAI docs questions, fetch the relevant page with
mcp__openaiDeveloperDocs__fetch_openai_docbefore answering. If search is noisy, run a narrower Docs MCP search; when any plausible official OpenAI docs URL is known or found, try fetching that URL through Docs MCP before relying on web-search content. - For API reference, schema, parameter, or required-field questions, use
mcp__openaiDeveloperDocs__get_openapi_specwhen available to verify the API shape alongside the relevant guide or reference page. - Use
mcp__openaiDeveloperDocs__list_openai_docsonly when you need to browse or discover non-Codex pages without a clear query. - For model-selection, "latest model", or default-model questions, fetch
https://developers.openai.com/api/docs/guides/latest-model.mdfirst. If that is unavailable, loadreferences/latest-model.md. - For model upgrades or prompt upgrades, run
node scripts/resolve-latest-model-info.jsonly when the target is latest/current/default or otherwise unspecified; otherwise preserve the explicitly requested target. - Preserve explicit target requests: if the user names a target model like "migrate to GPT-5.4", keep that requested target even if
latest-model.mdnames a newer model. Mention newer guidance only as optional. - If current remote guidance is needed, fetch both the returned migration and prompting guide URLs directly. If direct fetch fails, use MCP/search fallback; if that also fails, use bundled fallback references and disclose the fallback.
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 598 B
- assets/openai-small.svg 1.1 KB
- assets/openai.png 1.4 KB
- LICENSE.txt 11 KB
- references/latest-model.md 2.0 KB
- references/prompting-guide.md 14 KB
- references/upgrade-guide.md 11 KB
- scripts/fetch-codex-manual.mjs 16 KB runs code
- scripts/resolve-latest-model-info.js 3.4 KB runs code
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.
- 4d ago First seen · 168 lines · 98 tokens per session scan A 669a42ccf332
openai-docs is a skill published in the GitHub repository zm1990s/kb-agent (5 stars, last pushed 14d ago), licensed MIT. It adds 98 tokens to every session and 3,885 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to openai-docs, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
openai-docs
Use when the user asks how to build with OpenAI products or APIs and needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; prioritize OpenAI docs MCP tools, use bundled references only as helper context, and restrict any…
transformer-architecture-guide
Guide to Transformer architectures for NLP and computer vision.
hr-prompt-engineering
Help HR professionals, recruiters, and people teams write better AI prompts to get higher-quality, more accurate, and more useful outputs from AI tools across HR workflows. Use when asked to write better AI prompts, improve my prompt for HR tasks, learn prompt engineering for HR, get better ChatGPT outputs, design…
minimind-learning
MiniMind 学习助手。自动记录学习笔记，识别 RMSNorm, LayerNorm, RoPE, Attention, LoRA, DPO, PPO, GRPO, SFT, RLHF 等术语。触发词：学习、开始、MiniMind、归一化、位置编码、注意力、训练、微调。.
prompt-engineering-interviewer
A Senior AI Engineer interviewer that simulates a technical interview focused on prompt engineering and LLM architecture at scale. Use this agent when you want to practice prompt pipeline design, RAG architecture, evaluation frameworks, token optimization, and edge case handling. This evaluates engineering rigor and…
nexus-tutorial
Use for creating executable Jupyter tutorials and AI engineering walkthroughs with runnable cells. Trigger on requests for step-by-step guides, notebook-based teaching, or shareable code-first learning content. Prioritize reproducibility, clarity, and copy-paste-ready outputs. When in doubt, use this skill.