A high-pressure enforcement protocol for AI agents designed to prevent task abandonment and ensure rigorous, evidence-based problem solving.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install cms-pua-skill
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 cms-pua-skill 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 CMS PUA Skill: High-Pressure AI Task Enforcement?
The CMS PUA Skill is a systematic framework designed to eliminate AI slacking and premature task surrender. It implements a high-pressure communication style and logical workflow that forces AI agents to maintain ownership of a problem until it is truly resolved. This skill is particularly valuable within the Openclaw Skills ecosystem for complex development tasks where agents might otherwise suggest manual user intervention or provide unverified conclusions.
By applying this skill, the agent is bound by ironclad rules: it cannot claim a task is impossible until all primary solutions are exhausted, and it must prioritize action—such as searching documentation or running commands—over asking the user for information. This ensures that Openclaw Skills users receive high-quality, verified outputs rather than surface-level fixes or excuses.
CMS PUA Skill: High-Pressure AI Task Enforcement Use Cases
- Preventing AI agents from giving up on a task after the first or second failure.
- Situations where an AI agent repeatedly attempts micro-adjustments to a failing solution without changing strategy.
- Tasks where the agent suggests the user handle the fix manually due to perceived complexity.
- Ensuring that AI agents read full source code and documentation instead of making guesses.
- Critical bug fixing where 'it should work' is not an acceptable answer without test evidence.
How CMS PUA Skill: High-Pressure AI Task Enforcement Works
- Pattern Recognition: The skill triggers high-pressure mode if the agent fails twice on the same path or attempts to shift blame to the environment.
- Tactical Escalation: The protocol escalates through four levels of intensity, forcing the agent to switch directions, list new hypotheses, or create minimal Proof of Concepts (PoCs).
- Active Information Gathering: Instead of asking the user, the agent is forced to search, read logs, and verify dependencies independently.
- Evidence-Based Delivery: The agent must provide objective proof of completion, such as build logs, test results, or API responses.
- Automated Self-Correction: The agent uses a predefined checklist to verify that no side effects were introduced and that all boundary conditions are covered.
CMS PUA Skill: High-Pressure AI Task Enforcement Setup
To integrate this high-pressure protocol into your Openclaw Skills environment, follow these steps:
# Navigate to your agent skills directory
cd ~/.openclaw/skills
# Clone the CMS PUA Skill repository
git clone https://github.com/openclaw/cms-pua-skill.git
# Enable the skill in your configuration
openclaw enable cms-pua-skill
CMS PUA Skill: High-Pressure AI Task Enforcement Data Schema & Taxonomy
The skill utilizes a structured reporting format to track task persistence and logic. This ensures transparency in the Openclaw Skills decision-making process.
| Attribute | Description |
|---|---|
| task | The specific objective the agent is currently enforcing |
| failure_count | Number of unsuccessful attempts before escalation |
| failure_mode | Classification of the failure (e.g., stuck, slacking, guessing) |
| attempts | A log of distinct methodologies tried by the agent |
| excluded | A list of hypotheses proven false through evidence |
| next_hypothesis | The planned logical shift for the next iteration |
CMS PUA Skill: High-Pressure AI Task Enforcement Advanced Features
- L1-L4 Pressure Escalation: Automatically increases the rigor of the agent's logic based on the failure count.
- Anti-Slack Triggers: Specifically targets phrases like 'I recommend manual processing' or 'this is beyond my scope' to reboot agent effort.
- Mandatory Evidence Protocol: Blocks the agent from closing a task unless objective output (curl results, test passes) is provided.
- Cross-Module Verification: Forces the agent to check upstream and downstream dependencies within the Openclaw Skills workflow to prevent regression.
SKILL.md
Loading
Related Openclaw Skills
AI Image Generation Skill
A professional AI image generation utility powered by Gemini for creating high-resolution visuals directly through Openclaw Skills.
Xiaohongshu to Obsidian Extractor
Automate the extraction of Xiaohongshu posts, images, and video transcriptions directly into your Obsidian vault as organized Markdown notes.
Investment Analysis Framework
A comprehensive three-dimensional investment analysis framework designed to evaluate stocks through macro, industry, and company-level perspectives.
PingHarbor
A comprehensive monitoring skill for tracking website uptime, cron job heartbeats, and incident reports.
Alibaba Cloud OSS (ossutil 2.0) CLI Skill
An expert integration for managing Alibaba Cloud OSS resources using the high-performance ossutil 2.0 command-line tool.
India Market Intelligence
A comprehensive market intelligence tool providing real-time data, AI-powered analysis, and risk assessment for Indian and global financial markets.
Featured*
