Scan for stale memory topics, near-dead instincts, and low-salience K-LEAN entries. Proposes cleanup actions interactively.
Quality gate for instinct persistence. Validates new instincts through a 4-stage pipeline before allowing them to be written to disk. Invoke before writing any new instinct file.
Use when the user needs thorough research on a complex topic, says 'deep dive', 'research this thoroughly', or 'sensor sweep'. Parallel agent orchestrator for comprehensive multi-angle investigation.
Autonomous experiment loop: iteratively improves a single file against a measurable metric. Uses git-as-state-management in a disposable worktree. Invoke when user wants to optimize, tune, or systematically improve a file through repeated iterations.
Use when you need multi-faction review combining security, strategy, cost, and quality perspectives. Trigger: fleet review, multi-faction assessment, comprehensive quality gate, or when escalation requires L5+.
Two-loop research orchestrator: chains deep-research (outer, hypothesis generation) with experiment-loop (inner, empirical testing). Use when research questions have a testable dimension with a measurable metric.
Rigorous, citation-backed scientific research with evidence grading and claim-evidence tracing. Use when research needs academic-grade citations, source verification, or systematic review methodology. Trigger: /scientific-research, 'research with citations', 'find papers on', 'systematic review', 'literature review'.
Use when (1) you want an independent review from a different model, (2) stuck or looping on an issue, (3) need cross-model adversarial critique, (4) user says 'ask codex', 'ask gemini', or 'get a second opinion'. Cross-model collaboration with OpenAI Codex and Google Gemini CLI.