OpenAI Agents SDK is a Python framework for building workflows in which multiple AI agents use tools, delegate tasks, follow safety checks, and involve people when needed. Developers use it to create and monitor agent, sandbox, realtime, and voice applications.
Borrowing it
Nothing to install: this file belongs to openai/openai-agents-python. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/openai/openai-agents-python/main/.agents/skills/examples-run-analysis/SKILL.mdgit clone --depth 1 https://github.com/openai/openai-agents-pythonWrote 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-agents-python/examples-run-analysis)<a href="https://agentmods.dev/skills/openai/openai-agents-python/examples-run-analysis"><img src="https://agentmods.dev/badge/skills/openai/openai-agents-python/examples-run-analysis/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-agents-python/examples-run-analysis"><img src="https://agentmods.dev/badge/skills/openai/openai-agents-python/examples-run-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00021 | $0.00860 |
| Opus 5.5 | $0.00008 | $0.00344 |
| Sonnet 5.5 | $0.00004 | $0.00172 |
| Haiku 4.5 | $0.00002 | $0.00086 |
Grade A, and why
examples-run-analysis 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Examples Run Analysis
Use this skill only to analyze artifacts that already exist after a user has manually invoked an examples Make target. This skill is read-only and analysis-only.
Hard boundary
- Never start, retry, stop, or otherwise execute examples.
- Never invoke an examples Make target or
.github/scripts/run_examples.sh. - Never request elevated execution, alter an environment, remove a pid file, or own or signal a background process.
- Never treat an older completed run as current when the newest run is active, incomplete, or stale.
- If usable results are missing, stale, incomplete, or still running, stop the analysis and ask the user to run the appropriate Make target manually. Give the exact command but do not execute it.
The supported workflow is an explicit manual Make invocation followed by analysis of the generated artifacts.
Artifacts to inspect
- Background pid file:
.tmp/examples-auto-run.pid. - Main logs:
.tmp/examples-start-logs/main_*.log. - Per-example logs named by each
log=field in the selected main log. - Example sources named by
PASSED,FAILED, andSKIPPEDrecords. - Runner sources that define artifact meaning:
examples/run_examples.py,.github/scripts/run_examples.sh, and the example source files included in the run.
Use only read-only inspection commands such as git status, git log, find, ls, stat, ps, sed, and rg. Do not call a command that can update an artifact or process.
Analysis workflow
- Inspect the process table and
.tmp/examples-auto-run.pidwithout changing either. Treat a process as an active examples run only when its command line is rooted in the current repository and invokes.github/scripts/run_examples.shorexamples/run_examples.py, including foreground and background runs. Use the pid file only to correlate a background process; an absent or stale pid file does not prove that no run is active. If a matching process is live, stop the analysis. Tell the user to wait for a foreground Make run to finish, or ask the user to runmake examples-statusmanually for a background run, before requesting analysis again. - Select the newest
main_*.log. Require exactly one terminal# summary executed=<n> skipped=<n> failed=<n>record. Treat a missing or malformed summary, a changing log, or a matching active examples process as incomplete. - Treat the result as stale when relevant runner or selected example source content changed after the run. Use Git history and file timestamps as evidence. If freshness cannot be established, say so and request a new manual run instead of assuming the artifacts apply.
- Parse every
PASSED,FAILED, andSKIPPEDrecord. Reconcile their counts with the terminal summary. Confirm that every referenced per-example log exists. - For every
PASSEDrecord, without sampling, read the complete example source and its per-example log. Infer the intended flow, tools, side effects, and key result from the source and comments, then verify that the log demonstrates those behaviors. Exit status 0 alone is not behavioral validation. - Read the relevant per-example logs for failures and environment-related skips. Classify each result as an example or SDK defect, dependency or credential problem, provider or network failure, local service or platform restriction, intentional runner skip, or unresolved. Keep genuine product failures separate from environment restrictions.
- Report the selected main log, freshness and completeness evidence, summary counts, validation status for every exit-0 example, classified failures and skips, and exact source/log line references that support each conclusion.
What ships with it
1 file 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 Changed · -31 tokens per session 0d7a9f242322
- 1mo ago First seen · 52 lines · 52 tokens per session scan A 10c31597127a
examples-run-analysis is a skill published in the GitHub repository openai/openai-agents-python (30,339 stars, last pushed today), licensed MIT. It adds 21 tokens to every session and 860 once invoked, about $0.0001 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.
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