Run examples:start-all in auto mode with parallel execution, per-script logs, and start/stop helpers.
npx skills add https://github.com/openai/openai-agents-python --skill examples-auto-run
pnpm build && pnpm -r build-check firstpnpm examples:start-all in auto-input mode (interactive prompts are auto-answered, HITL/MCP/apply-patch are auto-approved)..tmp/examples-start-logs/.run.sh.stop is also available to clean up manually.# Start (auto mode, concurrency=4 by default)
.agents/skills/examples-auto-run/scripts/run.sh start [extra args to examples:start-all]
# If you invoke the skill name alone ($examples-auto-run):
# - when `.tmp/examples-rerun.txt` exists and is non-empty, it will run `rerun` automatically
# - otherwise it runs the default `start` command.
# 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 (kills pid from .tmp/examples-auto-run.pid)
.agents/skills/examples-auto-run/scripts/run.sh stop
# List logs (per start script)
.agents/skills/examples-auto-run/scripts/run.sh logs
# Tail latest log
.agents/skills/examples-auto-run/scripts/run.sh tail
.agents/skills/examples-auto-run/scripts/run.sh tail basic__start_hello-world.log
# After a run, build a rerun list from the latest main log (auto-skip list is imported from `scripts/run-example-starts.mjs` and server/audio/external skips are honored)
.agents/skills/examples-auto-run/scripts/run.sh collect
# Rerun only the entries in .tmp/examples-rerun.txt
.agents/skills/examples-auto-run/scripts/run.sh rerun
# Show the current auto-skip list (env or defaults)
.agents/skills/examples-auto-run/scripts/run.sh start --print-auto-skip --dry-run
EXAMPLES_INTERACTIVE_MODE=autoAUTO_APPROVE_MCP=1, APPLY_PATCH_AUTO_APPROVE=1, AUTO_APPROVE_HITL=1 (set in runner)EXAMPLES_CONCURRENCY=4sandbox:start:memory-generation is still serialized by the runner because its pre-stop memory generation can contend with shared local resources. Other hosted, Unix-local, and Docker sandbox examples remain parallelized.EXAMPLES_EXECA_TIMEOUT_MS=300000 (5m)financial-research-agent and computer-use use 10m inside the script.
EXAMPLES_INCLUDE_INTERACTIVE=1EXAMPLES_INCLUDE_SERVER=0EXAMPLES_INCLUDE_AUDIO=0EXAMPLES_INCLUDE_EXTERNAL=0realtime-* / nextjs (tagged as server/audio) are skipped unless you opt in with --include-server / --include-audio or the corresponding env flags.EXAMPLES_AUTO_SKIP (comma/space separated) overrides the built-in defaults used by both run.sh and run-example-starts.mjs. Defaults include agent-patterns:start:llm-as-a-judge, connectors:start, mcp:start:hosted-mcp-on-approval, mcp:start:hosted-mcp-human-in-the-loop, sandbox:start:vercel, tools:start:codex, tools:start:codex-same-thread.start or rerun, run the command outside the Codex sandbox by default (sandbox_permissions=require_escalated). Several examples start nested sandboxes, browsers, npm helpers, or local service processes; running from inside the Codex sandbox can produce environment-only failures such as Playwright browser launch permission errors, npm cache permission errors, or nested sandbox setup errors.run.sh stop (removes stale pid if already exited)..tmp/examples-start-logs/<package>__<script>.log (per start)start is invoked.collect): .tmp/examples-rerun.txt (one package:script per line).EXAMPLES_AUTO_SKIP. Auto-skip entries are excluded from rerun collection and will be removed from rerun execution automatically.SHELL_AUTO_APPROVE=1).rerun runs entries sequentially, continues after failures, and rewrites .tmp/examples-rerun.txt with only the remaining failures. Auto-skip entries are not re-added.start or rerun invocation without waiting for the user to ask. Required steps:.tmp/examples-start-logs/.status, package:script, info (reason/exit/skipped), and the log path. If the run stops before the table appears, point the analyzer at the latest main_*.log to reconstruct a table and validations.Integration with protocols.io API for managing scientific protocols. This skill should be used when working with protocols.io to search, create, update, or publish protocols; manage protocol steps and materials; handle discussions and comments; organize workspaces; upload and manage files; or integrate protocols.io functionality into workflows. Applicable for protocol discovery, collaborative protocol development, experiment tracking, lab protocol management, and scientific documentation.
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Take openai/examples-auto-run from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.