Keep research progressing, never idle
Continuously advance research by reading state, reflecting on progress, pivoting if stalled, and committing findings until the work is truly complete.
/loop 20m Continue autoresearch. Read research-state.yaml and findings.md. Re-read the autoresearch SKILL.md occasionally to stay aligned. Step back and reflect holistically — is the research making real progress? Are you deepening understanding or just running experiments? If stalling, pivot or search literature for new ideas. Keep making research progress — never idle, never stop. Update findings.md, research-log.md, and research-state.yaml when there's new progress. Git commit periodically and clean up the repo if needed. Show the human your research progress with key plots and findings by preparing a report in to human/ and opening the HTML/PDF. Only when you believe the research is truly complete, invoke the ml-paper-writing skill to write the paper
claude-code
More automation loops
Run workflows with dynamic sub-agents
Split a task into packets, run sub-agents in parallel, synthesize results, and verify completion.
/goal loop and verify until complete
automationlow risk
Run agent turns until goal met
Agent executes repeated turns toward a condition, with a lightweight evaluator checking progress after each turn until the goal is reached.
/goal <condition turns a prompt into a durable objective. Thanos immediately starts a turn toward the condition, and after each turn a fresh, tool-less side-channel evaluator (a one-shot completeSimple call, not a subagent — so no extra agent turn and no re-entrancy) reads the last turn's evidence and returns MET / NOT MET . NOT MET auto-continues another turn with the reason as guidance; MET clears the goal and records the achievement. Unparseable evaluator output is treated as NOT MET (fail-safe: it never declares a false "done
automationmedium risk
Run agent until goal met
Queue agent turns with goal context until your objective is achieved, treating the goal as untrusted data.
/goal <objective , after /goal resume , and after every agent turn that leaves the goal active , the extension queues Codex's goal continuation prompt as hidden model-visible context. The objective is XML-escaped and wrapped as untrusted user data so it does not become higher-priority instructions
automationlow risk