An AI-driven task processor that automatically translates vague human requirements into precise, machine-executable instructions.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prompt-optimizer-dxx
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 prompt-optimizer-dxx 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 Prompt Optimizer?
Prompt Optimizer is a technical utility designed to bridge the gap between fuzzy natural language and high-quality AI output. By acting as a specialized middleware within the Openclaw Skills ecosystem, it takes raw user inputs and wraps them in professional personas, structural constraints, and formatting rules. This ensures that the underlying AI models receive the context necessary to produce expert-level results without the user needing to manually engineer complex prompts.
The tool is built for developers and power users who need consistent, high-performance interactions with LLMs. Whether you are generating code, summarizing documents, or performing data analysis, the Prompt Optimizer ensures your instructions are optimized for clarity, persona-alignment, and Markdown-ready output, significantly increasing the success rate of automated AI workflows.
Prompt Optimizer Use Cases
- Standardizing vague requests like "write code" into professional engineering prompts with specific formatting.
- Automating research queries by adding requirements for source attribution and accurate data.
- Batch processing large lists of raw prompts from text files for high-volume content generation.
- Refining technical tasks such as code reviews and refactoring with specialized context.
- Improving the consistency of Openclaw Skills by providing a unified optimization layer for all agent interactions.
How Prompt Optimizer Works
- The user provides a raw instruction via the command line or a file input.
- The system analyzes the input to determine which of the 13 supported task types (e.g., coding, translation, or brainstorming) applies.
- It queries the internal configuration data to retrieve the appropriate professional persona and structural template.
- An LRU cache check is performed to see if an identical optimization has been recently generated to save resources.
- The tool merges the raw intent with the template, producing a refined, structured prompt.
- The final output is delivered as either plain Markdown text or a structured JSON object for programmatic use.
Prompt Optimizer Setup
To integrate this tool into your environment, follow these steps:
# Clone or copy the skill into your local workspace directory
cp -r prompt-optimizer/ ~/.openclaw/workspace/skills/
# Run a basic test to ensure functionality
python prompt_optimizer.py "Help me write a sorting algorithm"
# For batch processing, use the file flag
python prompt_optimizer.py --file raw_prompts.txt --output optimized.json --json
Prompt Optimizer Data Schema & Taxonomy
The skill manages its operations through a modular file structure to ensure high performance and easy customization within the Openclaw Skills framework:
| File | Role | Description |
|---|---|---|
prompt_optimizer.py |
Core Logic | Handles CLI arguments, caching, and the optimization pipeline. |
config_data.py |
Knowledge Base | Contains the 13 task templates and persona definitions. |
config.yaml |
User Settings | Allows for local overrides of system-wide defaults. |
cache |
Performance | Uses an LRU (Least Recently Used) strategy to store recent optimizations. |
| Output JSON | Metadata | Provides the task type, original prompt, and optimized result in a parseable format. |
Prompt Optimizer Advanced Features
- Support for 13 distinct task categories including Code Review, Content Generation, and Data Analysis.
- High-efficiency LRU caching to eliminate redundant optimization cycles.
- Modular configuration architecture that separates core logic from prompt templates for easy maintenance.
- Batch processing capabilities to handle multiple instructions via file input.
- Verbose logging and statistical reporting for monitoring optimization performance.
- Full JSON output mode for seamless integration into larger Openclaw Skills automation pipelines.
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