Agent skill

examples-auto-run

Run python examples in auto mode with logging, rerun helpers, and background control.

Stars 20,562
Forks 3,370

Install this agent skill to your Project

npx add-skill https://github.com/openai/openai-agents-python/tree/main/.agents/skills/examples-auto-run

SKILL.md

examples-auto-run

What it does

  • Runs uv run examples/run_examples.py with:
    • EXAMPLES_INTERACTIVE_MODE=auto (auto-input/auto-approve).
    • Per-example logs under .tmp/examples-start-logs/.
    • Main summary log path passed via --main-log (also under .tmp/examples-start-logs/).
    • Generates a rerun list of failures at .tmp/examples-rerun.txt when --write-rerun is set.
  • Provides start/stop/status/logs/tail/collect/rerun helpers via run.sh.
  • Background option keeps the process running with a pidfile; stop cleans it up.

Usage

bash
# Start (auto mode; interactive included by default)
.agents/skills/examples-auto-run/scripts/run.sh start [extra args to run_examples.py]
# Examples:
.agents/skills/examples-auto-run/scripts/run.sh start --filter basic
.agents/skills/examples-auto-run/scripts/run.sh start --include-server --include-audio

# Check status
.agents/skills/examples-auto-run/scripts/run.sh status

# Stop running job
.agents/skills/examples-auto-run/scripts/run.sh stop

# List logs
.agents/skills/examples-auto-run/scripts/run.sh logs

# Tail latest log (or specify one)
.agents/skills/examples-auto-run/scripts/run.sh tail
.agents/skills/examples-auto-run/scripts/run.sh tail main_20260113-123000.log

# Collect rerun list from a main log (defaults to latest main_*.log)
.agents/skills/examples-auto-run/scripts/run.sh collect

# Rerun only failed entries from rerun file (auto mode)
.agents/skills/examples-auto-run/scripts/run.sh rerun

Defaults (overridable via env)

  • EXAMPLES_INTERACTIVE_MODE=auto
  • EXAMPLES_INCLUDE_INTERACTIVE=1
  • EXAMPLES_INCLUDE_SERVER=0
  • EXAMPLES_INCLUDE_AUDIO=0
  • EXAMPLES_INCLUDE_EXTERNAL=0
  • Auto-approvals in auto mode: APPLY_PATCH_AUTO_APPROVE=1, SHELL_AUTO_APPROVE=1, AUTO_APPROVE_MCP=1

Log locations

  • Main logs: .tmp/examples-start-logs/main_*.log
  • Per-example logs (from run_examples.py): .tmp/examples-start-logs/<module_path>.log
  • Rerun list: .tmp/examples-rerun.txt
  • Stdout logs: .tmp/examples-start-logs/stdout_*.log

Notes

  • The runner delegates to uv run examples/run_examples.py, which already writes per-example logs and supports --collect, --rerun-file, and --print-auto-skip.
  • start uses --write-rerun so failures are captured automatically.
  • If .tmp/examples-rerun.txt exists and is non-empty, invoking the skill with no args runs rerun by default.

Behavioral validation (Codex/LLM responsibility)

The runner does not perform any automated behavioral validation. After every foreground start or rerun, Codex must manually validate all exit-0 entries:

  1. Read the example source (and comments) to infer intended flow, tools used, and expected key outputs.
  2. Open the matching per-example log under .tmp/examples-start-logs/.
  3. Confirm the intended actions/results occurred; flag omissions or divergences.
  4. Do this for all passed examples, not just a sample.
  5. Report immediately after the run with concise citations to the exact log lines that justify the validation.

Expand your agent's capabilities with these related and highly-rated skills.

openai/openai-agents-python

openai-knowledge

Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.

20,562 3,370
Explore
openai/openai-agents-python

final-release-review

Perform a release-readiness review by locating the previous release tag from remote tags and auditing the diff (e.g., v1.2.3...<commit>) for breaking changes, regressions, improvement opportunities, and risks before releasing openai-agents-python.

20,562 3,370
Explore
openai/openai-agents-python

implementation-strategy

Decide how to implement runtime and API changes in openai-agents-python before editing code. Use when a task changes exported APIs, runtime behavior, serialized state, tests, or docs and you need to choose the compatibility boundary, whether shims or migrations are warranted, and when unreleased interfaces can be rewritten directly.

20,562 3,370
Explore
openai/openai-agents-python

docs-sync

Analyze main branch implementation and configuration to find missing, incorrect, or outdated documentation in docs/. Use when asked to audit doc coverage, sync docs with code, or propose doc updates/structure changes. Only update English docs under docs/** and never touch translated docs under docs/ja, docs/ko, or docs/zh. Provide a report and ask for approval before editing docs.

20,562 3,370
Explore
openai/openai-agents-python

runtime-behavior-probe

Plan and execute runtime-behavior investigations with temporary probe scripts, validation matrices, state controls, and findings-first reports. Use only when the user explicitly invokes this skill to verify actual runtime behavior beyond normal code-level checks, especially to uncover edge cases, undocumented behavior, or common failure modes in local or live integrations. A baseline smoke check is fine as an entry point, but do not stop at happy-path confirmation.

20,562 3,370
Explore
openai/openai-agents-python

test-coverage-improver

Improve test coverage in the OpenAI Agents Python repository: run `make coverage`, inspect coverage artifacts, identify low-coverage files, propose high-impact tests, and confirm with the user before writing tests.

20,562 3,370
Explore

Didn't find tool you were looking for?

Be as detailed as possible for better results