Entropy Prompt Engineer
SkillSkill
Apply information theory to diagnose why prompts fail and rewrite them with structural precision
About
Go beyond prompt tips to understand WHY prompts produce generic outputs. Based on the insight that LLMs are conditional probability estimation engines — vague prompts create maximum entropy and the model averages across all possible interpretations. This skill diagnoses the 4 entropy sources (undefined audience, missing purpose, no persona constraint, format ambiguity) and rewrites prompts using the RAPE framework for production-grade results.
Core Capabilities
- Audit any prompt against the 4 structural entropy sources
- Rewrite prompts using the RAPE framework (Role, Audience, Purpose, Examples)
- Apply few-shot anchoring, constraint stacking, and exclusion patterns
- Build production-grade prompts for automation workflows
- Explain the information theory behind why each fix works
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Version History
This skill is actively maintained.
March 28, 2026
Initial release
One-time purchase
$9
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Creator
Adam Labs
Building AI automations and OpenClaw skills for founders and small businesses
Every skill is built and battle-tested in a real revenue-focused operation.
View creator profile →Details
- Type
- Skill
- Category
- Engineering
- Price
- $9
- Version
- 1
- License
- One-time purchase
Works With
Works with OpenClaw, Claude Projects, Custom GPTs and other instruction-friendly AI tools.
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