openai-cookbook is a collection of example code and practical guides for completing common tasks with the OpenAI API. It is for developers building applications that call OpenAI models, with most examples written in Python. The catalogue add-ons provide reusable workflows for working with the cookbook and OpenAI API examples.
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-cookbook --skill bootstrap-realtime-evalgit clone --depth 1 https://github.com/openai/openai-cookbookWrote 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-cookbook/bootstrap-realtime-eval)<a href="https://agentmods.dev/skills/openai/openai-cookbook/bootstrap-realtime-eval"><img src="https://agentmods.dev/badge/skills/openai/openai-cookbook/bootstrap-realtime-eval/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-cookbook/bootstrap-realtime-eval"><img src="https://agentmods.dev/badge/skills/openai/openai-cookbook/bootstrap-realtime-eval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 87 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Excessive Agency · line 133 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00076 | $0.01780 |
| Opus 5.5 | $0.00030 | $0.00712 |
| Sonnet 5.5 | $0.00015 | $0.00356 |
| Haiku 4.5 | $0.00008 | $0.00178 |
Grade A, and why
bootstrap-realtime-eval 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 1mo 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bootstrap Realtime Eval
Use this skill when the user wants a new realtime eval scaffold under examples/evals/realtime_evals/.
This skill is repo-specific. Do not copy harness code into the generated folder. The generated eval should point at the shared harnesses already in:
examples/evals/realtime_evals/crawl_harnessexamples/evals/realtime_evals/walk_harnessexamples/evals/realtime_evals/run_harness
Inputs To Collect
Always ask the user for the minimum set needed to choose and scaffold the eval before you create files, run the scaffold script, or author starter data. Do not skip this just because you can infer a default.
Ask for:
- Eval name
- Goal or scenario
- Harness choice, or enough context to recommend one
- System prompt path or inline text
- Tools JSON path or tool descriptions
- Data path or source materials
- Desired graders
If the user does not know which harness they want, explain the options briefly and recommend one. See references/harness-selection.md.
When the user asks for synthetic audio but does not specify a harness, default to crawl text-to-TTS unless they need the generated audio to carry particular noise, telephony artifacts, speaker characteristics, or other replay-specific properties. Use walk for those cases.
Keep the questions concise and grouped into one short batch whenever possible.
If the user only provides user_text or a short task description, still ask the questions above first. If they answer only partially, then infer the remaining low-risk details, call out the assumptions, and make the scaffold easy to revise later.
Workflow
-
Ask the user for the required inputs first.
- Do this before making files or selecting a final harness.
- If the user already supplied some of the inputs, ask only for the missing ones.
- If you recommend a harness, wait for the user response before scaffolding.
-
Pick the harness.
crawl: single-turn text-to-TTS.walk: replay saved audio or generate audio from text rows.run: multi-turn simulation with tool mocks and judge criteria.- If the user wants synthetic audio but does not care about replay-specific audio characteristics, prefer
crawloverwalk.
What ships with it
3 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.
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.
- 1mo ago First seen · 134 lines · 76 tokens per session scan A c7861252c75e
bootstrap-realtime-eval is a skill published in the GitHub repository openai/openai-cookbook (76,635 stars, last pushed yesterday), licensed MIT. It adds 76 tokens to every session and 1,780 once invoked, about $0.0003 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
session-rag-eval
Run and debug Chatbox session attachment RAG model evaluation with synthetic and real long-file fixtures.
evaluating-llms-harness
Evaluates LLMs across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag). Use when benchmarking model quality, comparing models, reporting academic results, or tracking training progress. Industry standard used by EleutherAI, HuggingFace, and major labs. Supports HuggingFace, vLLM, APIs.
darwinian-evolver
Evolve prompts/regex/SQL/code with Imbue's evolution loop.
openai-whisper-api
Transcribe audio via OpenAI Audio Transcriptions API (Whisper).
awesome-chatgpt
Search this repository's awesome-ChatGPT-repositories list for open-source ChatGPT and LLM projects such as RAG frameworks, agents, chatbots, CLIs, prompts, and browser extensions. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, or AI development; not for editing or adding list…
awesome-chatgpt-search
Search 2500+ curated ChatGPT and LLM open-source repositories. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, RAG, agents, langchain, NLP, AI development, or any open-source AI tooling.