Official agent skill

Examples Run Analysis

by openai in openai/openai-agents-python

Analyze logs and source from a completed manual examples run.

OfficialMITAuto-check passedDevelopment

Install Examples Run Analysis

skills CLI
$ npx skills add openai/openai-agents-python --skill examples-run-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install openai/openai-agents-python examples-run-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/openai/openai-agents-python.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/examples-run-analysis .claude/skills/examples-run-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
examples-run-analysis
GitHub stars
30k
Token cost
~1.1k tokens
SKILL.md length
569 words
Files
2
Skills in repo
14
Repo updated
First seen
Licence
MIT

At a glance

Analyze logs and source from a completed manual examples run.

  • Works in 7 steps: Inspect the process table and… → Select the newest main_*.log. Require… → Treat the result as stale when relevant… → …
  • Development work in your project
  • SKILL.md covers Hard boundary, Artifacts to inspect, Analysis workflow and Manual commands to request…
  • Calls make and git

What it does

Examples Run Analysis is an agent skill from openai/openai-agents-python, published by the product's own GitHub organization. Analyze logs and source from a completed manual examples run. Never execute or control examples.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files (for example `agents/openai.yaml`).

It sits in Development. It works with OpenAI and GitHub. The repository describes itself as: A lightweight, powerful framework for multi-agent workflows. The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “/examples-run-analysis”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. Inspect the process table and .tmp/examples-auto-run.pid without changing either. Treat a process as an active examples run only when its…
  2. Select the newest main_*.log. Require exactly one terminal # summary executed= skipped= failed= record. Treat a missing or malformed…
  3. Treat the result as stale when relevant runner or selected example source content changed after the run. Use Git history and file…
  4. Parse every PASSED, FAILED, and SKIPPED record. Reconcile their counts with the terminal summary. Confirm that every referenced…
  5. For every PASSED record, without sampling, read the complete example source and its per-example log. Infer the intended flow, tools, side…
  6. Read the relevant per-example logs for failures and environment-related skips. Classify each result as an example or SDK defect…
  7. Report the selected main log, freshness and completeness evidence, summary counts, validation status for every exit-0 example, classified…

What it can do on your machine

Read from SKILL.md and the folder at commit 71c2da4. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • make
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Examples Run Analysis loads about 1.1k tokens when it runs. Until then it costs about 30 tokens; SKILL.md has 569 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~30
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from openai/openai-agents-python at commit 71c2da4, republished under its MIT licence (© openai). 569 words, ~1,055 tokens.

Download SKILL.mdSave it as .claude/skills/examples-run-analysis/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
examples-run-analysis
description
Analyze logs and source from a completed manual examples run. Never execute or control examples.

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, and SKIPPED records.
  • 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

  1. Inspect the process table and .tmp/examples-auto-run.pid without 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.sh or examples/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 run make examples-status manually for a background run, before requesting analysis again.
  2. 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.
  3. 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.
  4. Parse every PASSED, FAILED, and SKIPPED record. Reconcile their counts with the terminal summary. Confirm that every referenced per-example log exists.
  5. For every PASSED record, 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.
  6. 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.
  7. 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.
Show full SKILL.md (35 more words)Show less

Manual commands to request when artifacts are unusable

Choose the narrowest applicable command and ask the user to run it in a terminal:

bash
make examples-run
make examples-run EXAMPLES_ARGS="--filter basic"
make examples-run-background EXAMPLES_ARGS="--include-server --include-audio"
make examples-status

Do not execute any of these commands as part of this skill.

© openai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in .agents/skills/examples-run-analysis of openai/openai-agents-python.

  • SKILL.md
  • agents/openai.yaml

Open the folder on GitHubat commit 71c2da4

Compare with similar skills

Examples Run Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Release Highlightsgarfiec/Librechat-Mobile110—~2kAutomated safety check: NotesMIT

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Works with

Categories

Questions about Examples Run Analysis

What does Examples Run Analysis do?

Analyze logs and source from a completed manual examples run. Examples Run Analysis is an agent skill from openai/openai-agents-python, published by the product's own GitHub organization. Analyze logs and source from a completed manual examples run.

When should I use Examples Run Analysis?

Examples Run Analysis fits situations like: development work in your project.

How do I install Examples Run Analysis in Claude Code?

Run `npx skills add openai/openai-agents-python --skill examples-run-analysis -a claude-code`. Or copy the skill folder (.agents/skills/examples-run-analysis in openai/openai-agents-python) into .claude/skills/examples-run-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Examples Run Analysis in Codex?

Run `npx skills add openai/openai-agents-python --skill examples-run-analysis -a codex`. Or copy the skill folder (.agents/skills/examples-run-analysis in openai/openai-agents-python) into .agents/skills/examples-run-analysis in your project. Codex loads it when a task matches its description.

Can I use Examples Run Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add openai/openai-agents-python --skill examples-run-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/examples-run-analysis, .gemini/skills/examples-run-analysis, .github/skills/examples-run-analysis and .opencode/skills/examples-run-analysis in your project.

What does Examples Run Analysis need to run?

Going by SKILL.md and its folder, Examples Run Analysis needs the command-line tools its instructions call (make and git).

Does Examples Run Analysis access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Examples Run Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Examples Run Analysis use?

Examples Run Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Examples Run Analysis use?

About 1.1k tokens (SKILL.md is roughly 4.2k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Examples Run Analysis?

Skills that share tags, products or a category with Examples Run Analysis: Release (OpenSource03/harnss, 381 stars), Release (pwrdrvr/openclaw-codex-app-server, 265 stars), Skills Constitution (jiabaobei/skills-constitution, 231 stars) and Community Triage (roryeckel/wyoming_openai, 218 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Examples Run Analysis?

openai (a GitHub organization, an official publisher) maintains it in openai/openai-agents-python, which has 29,873 GitHub stars. The repository holds 14 skills in this directory. The repository was last updated on October 7, 2026.

Source: openai/openai-agents-python on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.