SIPS Selfloop

Start or control the persistent loop with python3 scripts/goal_state.py selfloop-set "<focus>" from the SIPS plugin root, following commands/selfloop.md. Use the homebase_selfloop MCP tool instead when the live server exposes it.

For each active cycle, establish a measured baseline, select one evidence-backed self-improvement target, checkpoint, make the smallest useful change, verify the gain against the baseline, and record the outcome. Restrict work to SIPS or the agent's reasoning, tools, memory, verification, context efficiency, autonomy, and self-correction. Do not substitute unrelated product work or cosmetic churn.

After a proven gain, run python3 scripts/goal_state.py selfloop-record improved "<proof-bearing summary>" (or homebase_selfloop with action record when exposed), then record any durable lesson through SIPS Memory Fabric. Continue immediately while the goal is active. Two independent plateau cycles may complete the current objective. A real external block pauses the loop; an explicit stop clears it.

Cost-bounded planning and review

For a planning or research-heavy cycle, use one drafting pass, one independent blocker audit, and one final validation pass. Give each delegated task a bounded surface and acceptance check, reuse the same agent for corrections, and do not start a fresh audit round after every repair. Prefer mailbox completion events to repeated list_agents/short wait_agent polling.

If the user says to finish, stop, or mentions usage/cost, freeze scope immediately: stop pending expansion, ask active agents for blocker-only results, apply only material fixes, run the minimum gating validation, and hand off. Do not spend another review round polishing an already executable plan.

Report the current cycle, target, baseline, verification, and outcome.