Prompting

What This Skill Does

The Prompting skill provides an end-to-end framework for engineering, refining, and managing AI interactions. Unlike simple prompt generators, it treats prompts as structured software, storing user preferences and model-specific nuances in a local ~/prompting/ directory. It focuses on systematic optimization, ensuring that outputs remain consistent across different model architectures through a dedicated memory system that tracks voice patterns and past failures.

Installation

To integrate this skill into your OpenClaw environment, execute the following command in your terminal: clawhub install openclaw/skills/skills/ivangdavila/prompting Ensure your file system permissions allow reading and writing to the ~/prompting/ directory to enable the memory persistence features.

Use Cases

This skill is ideal for power users and developers who need high-fidelity AI output. It is particularly effective for:

• Content Pipeline Development: Ensuring brand voice consistency across multiple social media platforms by enforcing style constraints and formatting rules.
• Complex Reasoning Tasks: Debugging prompts that experience 'instruction drift' or 'hallucination' by following the structured failure classification workflow.
• Cost Optimization: Compressing prompt token usage by identifying and removing redundant instructions without degrading performance.
• Multi-Model Orchestration: Maintaining separate prompt optimization strategies for different models like Claude, GPT-4, and Gemini.

Example Prompts

1. "I am seeing constant formatting breaks on LinkedIn posts from the current prompt. Review the failure patterns in failures.md and suggest a structural fix for my LinkedIn template."
2. "Extract my writing voice from the provided samples.txt file and update ~/prompting/memory.md to ensure my future newsletter drafts sound more authoritative."
3. "Refactor my summary prompt for Haiku. It is too verbose and exceeds my budget. Keep the logic, but strip every word that doesn't strictly change the output quality."

Tips & Limitations

• Iteration is Key: Always prioritize the 'one change at a time' rule. Drastic rewrites often mask the source of an issue.
• Mind the Memory: The effectiveness of this skill relies on the quality of your memory.md. Regularly clean it to remove deprecated preferences.
• Constraint Limitations: Remember that even the best prompt cannot override hard-coded model refusals; use the 'Refusal' classification to troubleshoot intent phrasing before assuming a systemic failure.
• Compression Bias: Trust the 'remove, don't add' approach. If a prompt is underperforming, the solution is usually reduction, not elaboration.