Quality Boost is a professional prompt engineering evaluation tool that applies 9 rigorous rules to significantly enhance the accuracy, logic, and reliability of AI agent responses.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install quality-boost
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 quality-boost 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 Quality Boost?
Quality Boost serves as a high-performance quality enhancer for large language model outputs within the ecosystem of Openclaw Skills. It is specifically designed to tackle common AI pitfalls such as hallucinations, off-topic drifting, verbosity, and the omission of key constraints. By implementing a systematic framework, it transforms standard AI interactions into professional-grade outputs with quantified quality metrics.
The skill operates as an evaluation-focused version of prompt engineering, providing users with the tools to analyze and improve response quality through modular structures and active verification. Whether you are using the minimal version for token efficiency or the strict version for mission-critical tasks, Quality Boost ensures that the AI agent remains honest, concise, and aligned with user intent at all times.
Quality Boost Use Cases
- Refining marketing copy to ensure stylistic consistency without adding unauthorized information.
- Performing fact-based information retrieval where preventing hallucinations is the top priority.
- Conducting technical code reviews that require systematic identification of potential bugs.
- Managing long-form translations while maintaining the strict original meaning of the source text.
- Generating executive summaries where information density and the conclusion-first format are required.
How Quality Boost Works
- The user triggers the skill by requesting a quality improvement or by applying the 9 rules to a specific task.
- The AI agent reorganizes the prompt into a modular structure, clearly separating roles, constraints, and task modules.
- An active restatement phase begins where the agent confirms its understanding of the core requirements before taking action.
- During execution, the agent applies strict negative constraints (e.g., "do not guess") and prioritizes a conclusion-first output format.
- Upon completion, the agent performs a mandatory self-verification against a quality checklist to ensure all constraints are met.
- The final output is delivered along with an optional quality report detailing accuracy, completeness, and compliance scores.
Quality Boost Setup
To enable Quality Boost within your environment, update your local configuration file to include the skill settings. Open your ~/.openclaw/config.json and add the following:
{
"skills": {
"quality-boost": {
"autoApply": true,
"mode": "minimal"
}
}
}
Alternatively, you can trigger it manually in any conversation by starting your prompt with phrases like Apply quality rules or Use 9 rules to activate the enhancement logic for Openclaw Skills.
Quality Boost Data Schema & Taxonomy
Quality Boost organizes its evaluation and output data using the following taxonomy to ensure high standards within Openclaw Skills:
| Data Component | Description | Format |
|---|---|---|
| 9-Rule Framework | Modular rules covering restatement, self-check, and bans. | Markdown Templates |
| Quality Metrics | Quantitative scores for accuracy, completeness, and density. | JSON/Table |
| Evaluation Script | Logic for detecting hallucinations and compliance. | JavaScript Function |
| Report Template | Standardized structure for A/B testing and quality auditing. | Markdown |
| Configuration | Mode selection (Minimal, Standard, Strict). | JSON |
Quality Boost Advanced Features
- Automated A/B testing to compare the performance delta between standard and rule-enhanced AI responses.
- Multi-mode execution allowing users to choose between token-saving minimal logic and high-precision strict enforcement.
- Integrated memory management consciousness to prevent constraint loss during long-context conversations.
- Quantified quality reports providing 5-star ratings across dimensions like compliance and information density.
- Custom self-correction loops that force the agent to find and fix its own logical inconsistencies before the final delivery.
SKILL.md
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