# When Optimizing Prompts Use Prompt Optimization Analyzer > When Optimizing Prompts Use Prompt Optimization Analyzer - Skill: `dnyoussef/when-optimizing-prompts-use-prompt-optimization-analyzer` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add dnyoussef/when-optimizing-prompts-use-prompt-optimization-analyzer` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/when-optimizing-prompts-use-prompt-optimization-analyzer/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/when-optimizing-prompts-use-prompt-optimization-analyzer --- /*============================================================================*/ /* WHEN-OPTIMIZING-PROMPTS-USE-PROMPT-OPTIMIZATION-ANALYZER SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: when-optimizing-prompts-use-prompt-optimization-analyzer version: 1.0.0 description: | [assert|neutral] Active diagnostic tool for analyzing prompt quality, detecting anti-patterns, identifying token waste, and providing optimization recommendations [ground:given] [conf:0.95] [state:confirmed] category: foundry tags: - meta-tool - prompt-engineering - optimization - analysis - diagnostics author: ruv cognitive_frame: primary: evidential goal_analysis: first_order: "Execute when-optimizing-prompts-use-prompt-optimization-analyzer workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic foundry processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "when-optimizing-prompts-use-prompt-optimization-analyzer", category: "foundry", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Evidential", source: "Turkish", force: "How do you know?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["when-optimizing-prompts-use-prompt-optimization-analyzer", "foundry", "workflow"], context: "user needs when-optimizing-prompts-use-prompt-optimization-analyzer capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ ## Skill Execution Criteria ### When to Use This Skill - [AUTO-EXTRACTED from skill description and content] - [Task patterns this skill is optimized for] - [Workflow contexts where this skill excels] ### When NOT to Use This Skill - [Situations where alternative skills are better suited] - [Anti-patterns that indicate wrong skill choice] - [Edge cases this skill doesn't handle well] ### Success Criteria - primary_outcome: "[SKILL-SPECIFIC measurable result based on skill purpose]" - [assert|neutral] quality_threshold: 0.85 [ground:acceptance-criteria] [conf:0.90] [state:provisional] - verification_method: "[How to validate skill executed correctly and produced expected outcome]" ### Edge Cases - case: "Ambiguous or incomplete input" handling: "Request clarification, document assumptions, proceed with explicit constraints" - case: "Conflicting requirements or constraints" handling: "Surface conflict to user, propose resolution options, document trade-offs" - case: "Insufficient context for quality execution" handling: "Flag missing information, provide template for needed context, proceed with documented limitations" ### Skill Guardrails NEVER: - "[SKILL-SPECIFIC anti-pattern that breaks methodology]" - "[Common mistake that degrades output quality]" - "[Shortcut that compromises skill effectiveness]" ALWAYS: - "[SKILL-SPECIFIC requirement for successful execution]" - "[Critical step that must not be skipped]" - "[Quality check that ensures reliable output]" ### Evidence-Based Execution self_consistency: "After completing this skill, verify output quality by [SKILL-SPECIFIC validation approach]" program_of_thought: "Decompose this skill execution into: [SKILL-SPECIFIC sequential steps]" plan_and_solve: "Plan: [SKILL-SPECIFIC planning phase] -> Execute: [SKILL-SPECIFIC execution phase] -> Verify: [SKILL-SPECIFIC verification phase]" # Prompt Optimization Analyzer ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. **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" # Store original metrics npx claude-flow@alpha memory store --key "optimization/original-tokens" --value "{ \"total_tokens\": , \"redundancy_score\": <0-100>, \"verbosity_score\": <0-100> }" ``` **Analysis Script:** ```javascript // Embedded token analysis function analyzeTokenWaste(promptText) { const metrics = { totalWords: promptText.split(/\s+/).length, totalChars: promptText.length, redundancyScore: 0, verbosityScore: /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/foundry/when-optimizing-prompts-use-prompt-optimization-analyzer/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "when-optimizing-prompts-use-prompt-optimization-analyzer-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] WHEN_OPTIMIZING_PROMPTS_USE_PROMPT_OPTIMIZATION_ANALYZER_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]