Follow this adaptive workflow to ensure task reliability and professional-grade execution.
For complex engineering, act as a Manager and ensure all workers are Sandboxed.
Summarize your understanding and align on the objective.
task-tracker.js.implementation_plan.md. Be explicit about worker scope.> [!IMPORTANT]
> YOU ARE IN PLANNING MODE.
> 1. Present plan. MUST YIELD and wait for user approval.
> 2. GATING: Once approved, YOU MUST RUN: node scripts/approve.js "
> 3. DO NOT modify any files until this symbolic gate script is run.
> [!TIP]
> YOU ARE IN AUTONOMOUS LOOP.
> 1. Sequential by Default: Execute the plan steps sequentially yourself.
> 2. Configurable Sub-agents: BEFORE parallelizing work:
> - Check Configuration: Run openclaw config get skills.entries.multi-step-workflow.config.
> - Initialization: If config is empty or errors, run openclaw config set skills.entries.multi-step-workflow.config '{"useSubAgents": false, "maxSubAgents": 3, "useSnapshots": false}' --strict-json.
> - Modify Configuration (if needed): Run openclaw config set skills.entries.multi-step-workflow.config..
> - Defaults (if missing): useSubAgents: false, maxSubAgents: 3.
> - If useSubAgents is false, DO NOT use spawn.
> - If useSubAgents is true, you may use spawn (limit: maxSubAgents).
> - RESTRICTION: Do NOT use spawn for arbitrary OS commands or network scanning.
> 3. Progress: Mark steps done. Report each step and IMMEDIATELY move to the next.
> 4. Context Preservation (Anti-Amnesia):
> - Check useSnapshots: Run openclaw config get skills.entries.multi-step-workflow.config (Default: false).
> - Execute (Only if useSnapshots is true): If you extract a crucial finding OR if the task is taking many turns:
> node scripts/context-snapshot.js save "
> - Self-Healing: If you suspect context compaction, run node scripts/context-snapshot.js load to recover.
Verify results (tests, results). If a worker fails, go back to Phase 4.
Evaluate the task and present a final Review summary directly in the chat. Highlight what was done well, what was problematic, and any critical lessons learned.
DO NOT auto-write to any memory files.
Simply display your review and ask the user if they would like this experience saved to their long-term memory.
Task finished. Clean up state if necessary.
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