examples-run-analysis

A read-only process for reviewing logs and source files from the latest completed manual examples run.

In plain words
What is it for?
It is for validating completed example runs, investigating their logs, and classifying the reasons for failures or skips.
Why use it?
It distinguishes successful examples from failures, skips, and environment restrictions without accidentally running or changing the examples.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/openai/openai-agents-python/examples-run-analysis
Any agent
npx skills add openai/openai-agents-python --skill examples-run-analysis
Clone the repo
git clone --depth 1 https://github.com/openai/openai-agents-python

Made for: Claude Code, Codex.

Per session 52 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 891 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00052 $0.00891
Opus 5 $0.00026 $0.00445
Sonnet 5 $0.00010 $0.00178
Haiku 4.5 $0.00005 $0.00089

Measured 2d ago against content hash 10c31597127a, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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 2d 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.

.agents/skills/examples-run-analysis/SKILL.md · 52 lines

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, 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.

Read the full file on GitHub · 52 lines

Files

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.

Changes

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.

  1. 2d ago First seen · 52 lines · 52 tokens per session scan A 10c31597127a

Subscribe to this mod's changes

examples-run-analysis is a skill published in the GitHub repository openai/openai-agents-python (29,075 stars, last pushed 4d ago), licensed MIT. It adds 52 tokens to every session and 891 once invoked, about $0.0003 per session on Opus 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.