This skill enables users to construct high-quality AI prompts using the CLEAR Framework, enhancing their ability to communicate complex business requirements effectively to AI models. The framework focuses on clarity, logic, examples, adaptation, and results to ensure precise and actionable outputs.
claude skill add clear-framework-prompt-builder-mkug3ywsThe CLEAR Framework Prompt Builder is an innovative Claude Code skill designed to empower users in constructing high-quality AI prompts. By utilizing the CLEAR Framework, which emphasizes clarity, logic, examples, adaptation, and results, this skill enhances your ability to communicate complex business requirements to AI models effectively. This structured approach ensures that the outputs generated by AI are precise and actionable, making it a valuable tool for developers, product managers, and AI practitioners alike. One of the key benefits of the CLEAR Framework Prompt Builder is its ability to streamline the process of creating detailed automation instructions for AI in business processes. While the exact time savings are not quantified, users can expect a significant reduction in the time spent refining prompts and improving the accuracy of AI implementations in enterprise systems. With just 30 minutes needed for implementation, this skill offers a quick and efficient solution for enhancing your AI automation capabilities. This skill is particularly beneficial for roles that require a deep understanding of AI and its applications, such as developers working on AI-driven projects, product managers overseeing AI product development, and AI practitioners looking to optimize their workflows. The CLEAR Framework Prompt Builder can be applied in various scenarios, including designing robust AI-generated solutions for business challenges and enhancing the accuracy of AI outputs in critical enterprise applications. With an intermediate difficulty level, the CLEAR Framework Prompt Builder requires users to have a foundational understanding of AI concepts and prompt engineering. It fits seamlessly into AI-first workflows by providing a structured methodology for prompt construction, ensuring that AI agents can deliver outcomes that align with business goals. By adopting this skill, organizations can significantly improve their AI automation processes and achieve better results in their digital transformation efforts.
1. Identify the business problem or requirement. 2. Structure your problem using the CLEAR framework. 3. Fill in placeholders with specific and relevant details. 4. Input the structured prompt into an AI tool like GPT or Claude. 5. Review the output and iterate based on feedback, adjusting the prompt as necessary.
Creating detailed automation instructions for AI in business processes
Designing robust AI-generated solutions for business challenges
Enhancing the accuracy of AI implementations in enterprise systems
No install command available. Check the GitHub repository for manual installation instructions.
Copy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
To utilize the CLEAR framework, follow this structure: 1. **Clarity**: Clearly define the problem and desired outcome. For example: "[DEFINE_CLEAR_GOAL]" 2. **Logic**: Outline the logic or steps the AI should follow. For example: "[STEPS_FOR_AI]" 3. **Examples**: Provide specific scenarios and potential outcomes. For example: - Scenario 1: [SCENARIO_ONE] - Outcome: [EXPECTED_OUTCOME_ONE] 4. **Adaptation**: Mention how the AI should adapt based on feedback. For example: "If [CONDITION], then [ADAPTATION_ACTION]." 5. **Results**: Define how success will be measured. For example: "Success is measured by [SUCCESS_METRIC]." Use this structure to prompt AI for a specific task by filling in the placeholders with your details.
Clarity: Create a one-page qualification SOP that identifies companies with 50+ employees in manufacturing interested in automation. Logic: Qualify leads based on industry and engagement metrics. Examples: Scenario: Lead scores above 80 points Outcome: Route to senior sales rep with a Slack notification. Adaptation: If lead score changes, adjust qualification criteria automatically. Results: Success is measured by a 20% increase in qualified leads.
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