A strategic advisory skill designed to audit and refine OpenClaw workspaces for peak efficiency, lower costs, and reduced context bloat.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install openclaw-agent-optimize
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install openclaw-agent-optimize using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is OpenClaw Agent Optimization?
OpenClaw Agent Optimization is a specialized diagnostic and advisory tool within the ecosystem of Openclaw Skills. It is engineered to streamline AI operations by focusing on cost-aware model routing, parallel-first delegation, and lean context management. The skill acts as a technical consultant for your workspace, identifying hidden inefficiencies such as transcript noise, overactive heartbeat crons, and ambient specialist surface bloat.
By prioritizing a safe, advisory-first posture, this skill ensures that no persistent system mutations occur without explicit human approval. It provides developers with a clear roadmap for improvements, including exact patch proposals, expected performance impacts, and verified rollback procedures, making it an essential component for maintaining high-performance Openclaw Skills environments.
OpenClaw Agent Optimization Use Cases
- Auditing OpenClaw setups for cost-efficiency and model routing logic.
- Reducing context bloat to prevent slow agent replies and high token usage.
- Eliminating transcript noise generated by repetitive maintenance loops or tools.
- Implementing bootstrap discipline to keep always-injected system files lean.
- Transitioning workspace architecture toward parallel-first delegation models.
How OpenClaw Agent Optimization Works
- The skill performs a comprehensive audit of rules, memory, and the active skill surface to identify restart-critical facts.
- It analyzes technical metrics such as prompt character counts and eligible skill density to detect context bloat.
- A prioritized plan is generated, offering multiple optimization options with documented trade-offs.
- For selected optimizations, the skill produces an exact configuration patch and a corresponding rollback plan.
- Upon approval, the operator applies changes, followed by a mandatory verification step in a fresh session to confirm behavioral stability.
OpenClaw Agent Optimization Setup
To integrate this capability into your suite of Openclaw Skills, place the skill definition in your workspace configuration. No complex installation is required as it operates within the standard runtime.
# Navigate to your OpenClaw workspace skills directory
cd .openclaw/skills
# Verify the presence of the optimization skill file
ls openclaw-agent-optimize.md
Ensure that complementary tools like context-clean-up are available for enhanced diagnostic depth.
OpenClaw Agent Optimization Data Schema & Taxonomy
The skill utilizes high-signal metadata and session receipts to measure optimization success. Data is organized into the following taxonomy:
| Field | Description |
|---|---|
eligible skills |
The total count of tools currently visible to the agent. |
skills.promptChars |
The character count weight each skill adds to the system prompt. |
projectContextChars |
The total volume of project-specific context being injected. |
systemPrompt.chars |
The baseline size of the core instructions before skill injection. |
promptTokens |
The actual token cost per inference request. |
OpenClaw Agent Optimization Advanced Features
- Output Discipline: Enforces silent success paths for automated maintenance loops to prevent transcript inflation.
- Ambient Surface Reduction: Detects low-frequency specialist skills and moves them to on-demand usage instead of permanent injection.
- Out-of-Band Notifications: Separates heavy computational work from human-readable receipts to keep interaction contexts lean.
- Snapshot Verification: Specifically identifies when new sessions are required to pick up skill or bootstrap updates.
- Model Routing Plans: Provides tailored selection logic for balancing reasoning-heavy research against cost-effective notifications.
SKILL.md
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