Agent Native

examples-run-analysis

Analyzes logs and artifacts from manual examples runs to validate example behaviors, identify failures, and classify issues.

by OpenAIOfficial publisherNot reviewed

Identity proven by GitHub's verified organisation badge (openai)

Agents
Claude Code, Codex, Cursor, Gemini CLI, GitHub Copilot, OpenCode, claude.ai and API
License
MIT (permissive)
Pin
125efa0pinned Oct 9, 2026
Context cost
about 1,000 tokenswhen the skill loads
GitHub stars
29,916on openai/openai-agents-python
Scripts
None found

What it does

“Analyze logs and source from a completed manual examples run. Never execute or control examples.”
The author's description, from SKILL.md

Generated summary from the skill's own text

Analyzes logs and artifacts from manual examples runs to validate example behaviors, identify failures, and classify issues. This read-only skill inspects completed run outputs without executing or controlling examples execution.

When to use it

  • After manually running an examples Make target to review results
  • Debugging why specific examples failed or were skipped
  • Validating that example source code behaves as intended in logs
  • Assessing SDK defects versus environment or dependency problems

When not to

  • To execute or control examples runs directly
  • When no examples run has been completed manually yet
  • To modify artifacts or update the runtime environment

Install

These commands install from the publisher's repository and need no account.

skills CLI

npx skills add openai/openai-agents-python --skill examples-run-analysis

Installs from the repository's default branch, which may have changed since the commit we checked.

GitHub CLI 2.90 or later

gh skill install openai/openai-agents-python examples-run-analysis@125efa029b4bfd84238bd2c4fd69c3406f802663

Installs the commit we checked (125efa0).

Copy by hand

Download the folder at 125efa0 and put it in .claude/skills/examples-run-analysis for Claude Code and in .agents/skills/examples-run-analysis for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode.

Skill folder on GitHub at 125efa0

Copy for your agent

A prompt for any coding agent. It installs this skill from GitHub at the pinned commit, copies the files as they are, and runs nothing unless you ask.

Prompt

Install the agent skill "examples-run-analysis" by OpenAI.

Source, pinned to commit 125efa029b4bfd84238bd2c4fd69c3406f802663:
https://github.com/openai/openai-agents-python/tree/125efa029b4bfd84238bd2c4fd69c3406f802663/.agents/skills/examples-run-analysis

1. Download that folder at exactly this commit. Do not use the default branch.
2. Copy every file unchanged, keeping the folder structure, into .claude/skills/examples-run-analysis/ for Claude Code and .agents/skills/examples-run-analysis/ for Codex, Cursor, Gemini CLI, GitHub Copilot and OpenCode. Create the folders if they do not exist.
3. Do not edit the files, and do not run any script in the folder unless I ask you to.
4. When you are done, list the files you copied and where you put them.

Listing and checks: https://www.agentnative.dev/skills/openai/examples-run-analysis-openai-agents-python

SKILL.md at the pin

---
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.

## 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.

From openai/openai-agents-python at 125efa0, shown as plain text under its license: MIT (permissive). The license text is in LICENSE.

View SKILL.md on GitHub at 125efa0

Files

2 files, 4.5 KB in total. Each file has a sha256 in the pin's manifest; the first 12 characters are shown.

Skill folder on GitHub at 125efa0
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