Start, inspect, or continue a persistent SIPS self-improvement loop. Use when the user asks for /selfloop, wants the agent to iteratively improve itself, or wants a goal dedicated only to SIPS and agent capability.
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See how to installReview the instructions the agent will receive before you install.
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.
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.
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Extract bounded, source-linked context from large files or many inputs. Use when context is too large, files are oversized, or a task needs concise excerpts.
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Inspect SIPS Homebase status, manifest wiring, routes, host visibility, and MCP freshness. Use when asked for SIPS status, command center, host audit, plugin visibility, or whether Homebase is fresh.
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Route a task through SIPS commands, agents, scripts, MCP tools, or bounded delegation. Use when asked to route, split, fan out, escalate, or choose the right SIPS path.
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Turn failures, logs, symptoms, or flaky behavior into a compact repro and verification plan. Use when debugging needs a reproducible failure path.
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Capture and develop a reusable skill, tool repair, or behavior improvement noticed while completing a task. Use when instructions are stale, a recurring workflow deserves a skill, or a tool contract fails in practice.
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Search, inspect, and record SIPS-owned Memory Fabric lessons. Use when a task needs prior lessons, recurring-fix memory, recall health, scoped historical context, or when a just-fixed bump or error should be recorded.
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