openclaw-agent-optimize-skill
DEPRECATED — duplicate listing. Please use the canonical "openclaw-agent-optimize" skill instead.
Why use this skill?
Discover the canonical OpenClaw agent optimization skill. Learn how to refine agent performance, reduce token usage, and speed up workflows.
Install via CLI (Recommended)
clawhub install openclaw/skills/skills/phenomenoner/openclaw-agent-optimize-skillWhat This Skill Does
This entry serves as a notice that the skill previously identified as openclaw-agent-optimize-skill has been deprecated. It was a duplicate listing and has been removed to consolidate documentation and versioning under the canonical repository. The optimization engine is designed to analyze current agent performance, identify latency bottlenecks in prompt chains, and refine execution parameters to ensure that your autonomous agents operate with maximum efficiency. By shifting to the canonical version, you ensure that you are receiving the latest security patches, performance improvements, and feature updates directly from the author, phenomenoner.
Installation
To begin using the optimized skill, please use the canonical source provided by the maintainer. Open your terminal or your OpenClaw interface and execute the following command to pull the latest version:
clawhub install phenomenoner/openclaw-agent-optimize
Ensure that you have removed any local references to the deprecated openclaw-agent-optimize-skill to avoid conflicts in your execution environment.
Use Cases
- Prompt Engineering Optimization: Automatically shorten verbose system instructions while maintaining high instruction-following adherence.
- Latency Reduction: Analyze agent response times and suggest structural changes to multi-step agents to reduce tokens processed.
- Performance Benchmarking: Compare the efficiency of different model configurations against your specific task load.
- Resource Management: Optimize the token consumption of long-running autonomous workflows to lower operational costs.
Example Prompts
- "Analyze my current agent execution logs and suggest three ways to reduce the token count of my primary system prompt without losing accuracy."
- "Review the execution speed of this multi-agent chain and identify where the longest latency occurs, then propose an optimized sequence."
- "Refactor my agent's reasoning loop to be more concise while maintaining its ability to handle complex edge cases."
Tips & Limitations
- Always verify the source: Only install skills from verified repository paths to maintain system integrity.
- Test in isolation: When applying optimization suggestions to production agents, always run a small test batch first to ensure that the logic remains intact after the reduction steps.
- Feedback Loop: If the optimization makes the agent too brief, manually adjust the verbosity constraints in your agent configuration.
Metadata
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Find the right skillPaste this into your clawhub.json to enable this plugin.
{
"plugins": {
"official-phenomenoner-openclaw-agent-optimize-skill": {
"enabled": true,
"auto_update": true
}
}
}Tags(AI)
Related Skills
openclaw-agent-optimize
Use when: you want to optimize an OpenClaw setup (cost/quality tradeoffs, model routing, context discipline, delegation, reliability) and you’re okay with a structured audit → options → recommended plan. Don’t use when: you want immediate config mutations without review, or the question is unrelated to OpenClaw operations. Output: a prioritized plan + exact change proposals (with rollback) if approved.
context-scope-tags
Use when: you need strict context boundaries in chat (Telegram/Discord/Slack/etc.) and want to prevent topic bleed using explicit tags like [ISO], [SCOPE], [GLOBAL], [NOMEM], [REM]. Don’t use when: you want normal free-form conversation with automatic carry-over. Output: a copy/paste tag cheat sheet + routing rules.
openclaw-agent-token-optimizer
DEPRECATED — duplicate listing. Please use the canonical "openclaw-agent-optimize" skill instead.
cron-worker-guardrails
Use when hardening OpenClaw cron workers (especially isolated agentTurn jobs) against quoting failures, brittle shell patterns, SIGPIPE false failures, and cwd/env drift. Output: a scripts-first hardening checklist + portable patterns.
context-clean-up
Use when: you suspect OpenClaw prompt context is bloating (slow replies, high cost, repeated transcript noise) and you want a ranked offender list + a reversible clean-up plan. Don’t use when: you want the assistant to apply fixes automatically, or you’re asking for unrelated troubleshooting. Output: an audit summary + 3–8 concrete fix steps + rollback notes (no automatic changes).