Openclaw Agent Optimize

**Quick judgment**: This is a high-quality, advisory-only optimization skill for OpenClaw workspaces. It is especially strong on safety, structure, and practical guidance for cost/control improvements. It does *not* make changes by default; instead it produces audits and exact patch/rollback/verification plans for a human to review and apply. **Strengths** - Very explicit safety contract: no persistent config or cron changes without explicit approval; always paired with rollback + verification steps. - Clear expectations for output structure (executive summary → options → recommended plan → exact patch + rollback + verify), which improves operator trust and usability. - Targets the most common high-impact issues in real deployments: context bloat, overly verbose bootstraps, noisy automation, and suboptimal model routing. - Encourages measurement-based validation using concrete telemetry fields (promptChars, systemPrompt.chars, promptTokens, etc.). **Key risks / limitations** - Advisory-only by default: users must correctly apply the suggested patches themselves or explicitly authorize mutations in a controlled workflow. If a team expects automatic self-tuning, they may be disappointed or misconfigure changes during manual application. - Optimization can reduce monitoring or context in ways that affect niche workflows; the skill mitigates this by requiring explicit choice when coverage is reduced, but operators still need to review tradeoffs carefully. - Best suited to users who already understand OpenClaw configuration surfaces (skills, crons, bootstrap files, routing configs); less experienced users may need to read the referenced playbooks to implement changes safely. **Recommended scenarios** - Teams seeing rising costs, slower responses, or high token usage in OpenClaw and wanting a structured, low-risk tuning pass. - Operators wanting to clean up noisy logs/transcripts from crons, heartbeats, or maintenance loops without losing failure alerts. - Workspaces that have accumulated many ambient skills, large bootstraps, or ad hoc routing rules and need a principled, reversible simplification. - Any optimization effort where safety, rollbackability, and verification are non-negotiable, and changes must be justified by measurable improvements rather than intuition.