A systematic framework for engineering, testing, and refining AI prompts across multiple LLMs while maintaining a consistent user voice.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install prompting
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 prompting 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 Prompting Skill?
The Prompting skill is a professional-grade toolkit designed for developers and AI enthusiasts to manage the entire lifecycle of a prompt. It provides a structured environment in the user's local directory to store prompt patterns, track iteration history, and maintain a persistent memory of user preferences. By leveraging this skill, users can ensure their AI interactions are cost-effective, model-optimized, and stylistically consistent.
This system moves beyond simple trial-and-error by implementing rigorous failure analysis and model-specific adaptation. Whether you are working with Claude, GPT-4, or lightweight models like Haiku, this skill within the Openclaw Skills ecosystem helps you achieve surgical precision in prompt adjustments, reducing token waste and improving output reliability.
Prompting Skill Use Cases
- Designing model-specific prompts for GPT-4, Claude, or Gemini to maximize performance and minimize instruction drift.
- Maintaining a consistent brand or personal voice across various AI-generated content through Voice Lock extraction.
- Troubleshooting and fixing prompt failures such as hallucinations, format breaks, or refusals using systematic classification.
- Reducing token consumption and API costs through aggressive prompt compression and simplification techniques.
- Generating robust test cases for prompt validation, including edge cases and adversarial inputs to ensure production readiness.
How Prompting Skill Works
- Analyze the target model and specific failure modes to establish a baseline for prompt development.
- Access the memory.md file to retrieve user voice patterns, model preferences, and past corrections.
- Utilize reusable templates from the patterns directory to structure the initial prompt based on the task type.
- Apply model-specific optimizations, such as explicit constraints for Claude or step-by-step scaffolding for GPT-4.
- Iterate on the prompt by changing one variable at a time and noting the surgical fixes in the history log.
- Validate the final output against platform-native constraints and user-provided writing samples.
Prompting Skill Setup
To initialize the Prompting skill, set up the required directory structure and configuration files in your home directory.
mkdir -p ~/prompting/patterns
touch ~/prompting/memory.md
touch ~/prompting/history.md
Ensure you populate memory.md with your initial preferences using the provided template to enable persistent voice preservation within your Openclaw Skills workflow.
Prompting Skill Data Schema & Taxonomy
The skill organizes data in a hierarchical markdown-based structure to ensure readability and easy iteration:
| File | Purpose | Content Type |
|---|---|---|
memory.md |
Persistent Memory | User voice, model preferences, and historical corrections. |
patterns/ |
Template Library | Reusable prompt templates categorized by task (e.g., coding, creative). |
history.md |
Log Tracking | A record of past prompts and their specific outcomes. |
failures.md |
Error Reference | Documentation of common failure modes and recommended fixes. |
models.md |
Model Intelligence | Specific quirks and optimization tips for different LLM providers. |
Prompting Skill Advanced Features
- Voice Lock: Automatically extracts sentence length, punctuation, and vocabulary patterns from samples to maintain stylistic consistency.
- Compression Bias: A built-in methodology to remove redundant words and minimize token usage without altering the output quality.
- Failure Classification: A diagnostic system that identifies if a prompt failed due to hallucination, format break, instruction drift, or refusal.
- Platform-Native Formatting: Enforces strict output constraints for specific social platforms like Twitter, LinkedIn, and Instagram.
- Multi-Model Adaptation: Switches prompting strategies dynamically based on whether the target is a high-reasoning model or a high-speed model.
SKILL.md
Loading
Related Openclaw Skills
Prometheus Monitoring & Observability
A technical guide to mastering Prometheus monitoring patterns, cardinality management, and PromQL optimization for reliable observability.
Product Skill
A strategic toolkit for validating ideas, generating AI product mockups, and optimizing marketplace listings for digital and physical goods.
Product Owner
A professional AI skill for managing backlogs, defining user stories, and maximizing product value through structured agile methodologies.
Product Manager
A strategic framework for AI agents to lead product discovery, prioritize ruthlessly, and align cross-functional teams toward high-impact outcomes.
Property Valuation
An advanced real estate analysis tool for calculating property values through comparable sales, income approaches, and market adjustments.
Psychologist
An AI skill providing empathetic emotional support, active listening, and evidence-based techniques to help users navigate their feelings.
Featured*
