self-optimize

Analyze OpenClaw-CC performance and rewrite prompts, hooks, and workflows.

4|Updated Mar 21, 2026
One-click install
npx skills add https://github.com/Kit4Some/Oh-my-ClaudeClaw --skill self-optimize
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: self-optimize
Source: https://github.com/Kit4Some/Oh-my-ClaudeClaw/tree/main/skills/self-optimize
Command: npx skills add https://github.com/Kit4Some/Oh-my-ClaudeClaw --skill self-optimize

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Continuously improve the OpenClaw-CC system by analyzing its performance, identifying failure patterns, and modifying skill prompts, hooks, and workflows to reduce errors and increase success rates in production environments.

Core Features & Use Cases

  • Automated cycle: measure, analyze, modify, verify, and commit improvements to prompts, hooks, and agent prompts.
  • Guardrails: limit edits per cycle, require git stash or branch, and run tests before merging to prevent regressions.
  • Memory-driven optimization: leverage memory logs to track performance, compare cycles, and enable rollbacks if metrics worsen.
  • Weekly cadence: automated optimization runs via cron to keep the system evolving.

Quick Start

Initiate a self-optimization cycle to review recent errors and propose minimal, safe improvements.

Frequently Asked Questions about self-optimize

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I continuously improve agent prompts and workflows to reduce production errors?

To continuously improve agent prompts and workflows, you run an automated self-optimization cycle that analyzes performance, identifies failure patterns, and rewrites prompts and hooks to reduce errors and increase success rates.

What are the guardrails for safe automated prompt engineering in production environments?

Safe automated prompt engineering requires change-guardrails that limit edits per cycle, mandate git stash or branch usage, and run automated testing before merging changes to prevent regressions in production environments.

How does memory-driven self-optimization handle rollbacks when system performance worsens?

Memory-driven self-optimization leverages memory logs to track performance and compare optimization cycles, enabling automated rollbacks to previous stable states if metrics worsen after applying new prompt modifications.

Can I schedule automated workflow optimization to run on a weekly cadence?

Yes, you can schedule automated workflow optimization to run on a weekly cadence using cron, ensuring the system evolves continuously by measuring, analyzing, modifying, verifying, and committing improvements automatically.

Do I need git-based backups to autonomously modify agent prompts and hooks?

Yes, you need git-based backups to autonomously modify agent prompts and hooks, as the system requires branching or stashing changes and running tests before merging to maintain rollback safeguards and prevent regressions.

What limitations exist when applying self-optimization to production skills and agents?

Limitations of self-optimization include restricted edit counts per cycle to ensure safety, and strict dependencies on git-based backups and automated testing, meaning it cannot apply unguarded or unverified modifications to production skills.