codex-autoresearch

Autonomous goal-directed iteration. Modify -> Verify -> Keep/Discard -> Repeat.

When Activated

1. Classify the request as loop, plan, debug, fix, security, ship, or exec, and parse any inline config from the prompt.
2. Load references/core-principles.md and references/structured-output-spec.md. For active execution modes (loop, debug, fix, security, ship, exec), also load references/runtime-hard-invariants.md.
3. Load only the additional references the current situation needs:
   • references/session-resume-protocol.md when resuming or controlling an existing run
   • references/environment-awareness.md before choosing hardware-sensitive work
   • references/interaction-wizard.md for every new interactive launch (loop, debug, fix, security, ship) before execution begins
   • references/results-logging.md only when debugging TSV/state semantics or helper behavior directly
4. Load the selected mode workflow reference plus only the detailed cross-cutting protocols that actually apply (lessons, pivot, health-check, parallel, web-search, hypothesis-perspectives).
5. Use the bundled helper scripts when stateful artifacts or runtime control are involved. Resolve them relative to the loaded skill bundle root (<skill-root>/scripts/...), not the target repo root. In the common repo-local install this means commands such as python3 .agents/skills/codex-autoresearch/scripts/autoresearch_init_run.py .... For repo-managed control-plane helpers (autoresearch_resume_check.py, autoresearch_launch_gate.py, autoresearch_resume_prompt.py, autoresearch_supervisor_status.py, autoresearch_runtime_ctl.py status/stop), prefer --repo <repo> and let the helper derive default artifact paths.
6. Execute the selected workflow exactly as written and produce the required structured output and artifacts.

Core Loop