when-optimizing-prompts-use-prompt-optimization-analyzer

Solid

Active diagnostic tool for analyzing prompt quality, detecting anti-patterns, identifying token waste, and providing optimization recommendations

AI & Automation 335 stars 29 forks Updated today

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Skill Content

# Prompt Optimization Analyzer **Purpose:** Analyze prompt quality and provide actionable optimization recommendations to reduce token waste, improve clarity, and enhance effectiveness. ## When to Use This Skill - Before publishing new skills or slash commands - When prompts exceed token budgets - When responses are inconsistent or unclear - During skill maintenance and refinement - When analyzing existing prompt libraries ## Analysis Dimensions ### 1. Token Efficiency Analysis - Redundancy detection (repeated concepts, phrases) - Verbosity measurement (word count vs. information density) - Compression opportunities (equivalent shorter forms) - Example bloat (excessive or redundant examples) ### 2. Anti-Pattern Detection - Vague instructions ("do something good") - Ambiguous terminology (undefined jargon) - Conflicting requirements (contradictory rules) - Missing context (insufficient background) - Over-specification (unnecessary constraints) ### 3. Trigger Issue Analysis - Unclear activation conditions - Overlapping trigger patterns - Missing edge cases - Too broad/narrow scope ### 4. Structural Optimization - Information architecture (logical flow) - Section organization (grouping, hierarchy) - Reference efficiency (cross-references, links) - Progressive disclosure (layered detail) ## Execution Process ### Phase 1: Token Waste Detection ```bash # Analyze prompt for redundancy npx claude-flow@alpha hooks pre-task --description "Analyzing prompt for token waste" #...

Details

Author
aiskillstore
Repository
aiskillstore/marketplace
Created
5 months ago
Last Updated
today
Language
Python
License
None

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