// Run python examples in auto mode with logging, rerun helpers, and background control.
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.
Analyze main branch implementation and configuration to find missing, incorrect, or outdated documentation in docs/.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.
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.
Create a PR title and draft description after substantive code changes are finished. Trigger when wrapping up a moderate-or-larger change (runtime code, tests, build config, docs with behavior impact) and you need the PR-ready summary block with change summary plus PR draft text.
Run the mandatory verification stack when changes affect runtime code, tests, or build/test behavior in the OpenAI Agents Python repository.
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`.
| name | examples-auto-run |
| description | Run python examples in auto mode with logging, rerun helpers, and background control. |
uv run examples/run_examples.py with:
EXAMPLES_INTERACTIVE_MODE=auto (auto-input/auto-approve)..tmp/examples-start-logs/.--main-log (also under .tmp/examples-start-logs/)..tmp/examples-rerun.txt when --write-rerun is set.run.sh.stop cleans it up.# Start (auto mode; interactive included by default)
.codex/skills/examples-auto-run/scripts/run.sh start [extra args to run_examples.py]
# Examples:
.codex/skills/examples-auto-run/scripts/run.sh start --filter basic
.codex/skills/examples-auto-run/scripts/run.sh start --include-server --include-audio
# Check status
.codex/skills/examples-auto-run/scripts/run.sh status
# Stop running job
.codex/skills/examples-auto-run/scripts/run.sh stop
# List logs
.codex/skills/examples-auto-run/scripts/run.sh logs
# Tail latest log (or specify one)
.codex/skills/examples-auto-run/scripts/run.sh tail
.codex/skills/examples-auto-run/scripts/run.sh tail main_20260113-123000.log
# Collect rerun list from a main log (defaults to latest main_*.log)
.codex/skills/examples-auto-run/scripts/run.sh collect
# Rerun only failed entries from rerun file (auto mode)
.codex/skills/examples-auto-run/scripts/run.sh rerun
EXAMPLES_INTERACTIVE_MODE=autoEXAMPLES_INCLUDE_INTERACTIVE=1EXAMPLES_INCLUDE_SERVER=0EXAMPLES_INCLUDE_AUDIO=0EXAMPLES_INCLUDE_EXTERNAL=0APPLY_PATCH_AUTO_APPROVE=1, SHELL_AUTO_APPROVE=1, AUTO_APPROVE_MCP=1.tmp/examples-start-logs/main_*.logrun_examples.py): .tmp/examples-start-logs/<module_path>.log.tmp/examples-rerun.txt.tmp/examples-start-logs/stdout_*.loguv 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..tmp/examples-rerun.txt exists and is non-empty, invoking the skill with no args runs rerun by default.The runner does not perform any automated behavioral validation. After every foreground start or rerun, Codex must manually validate all exit-0 entries:
.tmp/examples-start-logs/.