Automated skill tiering and injection policy configuration tool for optimizing Openclaw Skills performance.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install skill-priority-setup
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install skill-priority-setup using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
Skill Priority Setup is an essential utility for developers using Openclaw Skills who need to manage complex agent configurations. It implements a structured 4-tier priority system (L0-L3) that intelligently controls how and when specific skills are injected into the agent's context. By moving away from a flat architecture, this tool helps maintain low latency and high accuracy for your AI agents. This skill is particularly valuable for users managing a large library of Openclaw Skills, as it automates the discovery, analysis, and tier assignment process, ensuring that the most critical core functions are always available while secondary extensions are only loaded as needed.
To get started with optimizing your Openclaw Skills, run the setup wizard included in the package:
# Run the interactive setup wizard for Openclaw Skills
python3 ~/.openclaw/workspace/skills/skill-priority-setup/scripts/setup.py
# Or use the --auto flag for a non-interactive setup with defaults
python3 ~/.openclaw/workspace/skills/skill-priority-setup/scripts/setup.py --auto
The following files are managed or created to support the priority framework for Openclaw Skills:
| File | Description |
|---|---|
| SKILL_PRIORITY_POLICY.md | Master policy document defining tier assignments for all Openclaw Skills. |
| config.yaml | Configuration file for setting token budgets and overrides. |
| AGENTS.md | System file updated with tier-based constraints and core rules. |
| SOUL.md | Behavioral file updated with agent evolution logic. |
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