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 cognitiveframe: primary: evidential goalanalysis: firstorder: "Execute when-optimizing-prompts-use-prompt-optimization-analyzer workflow" secondorder: "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

selfconsistency: "After completing this skill, verify output quality by [SKILL-SPECIFIC validation approach]" programofthought: "Decompose this skill execution into: [SKILL-SPECIFIC sequential steps]" planand_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

# 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\": ,
  \"verbosity_score\": 
}"

Analysis Script:

// 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]

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [DNYoussef](https://github.com/DNYoussef)
- **Source:** [DNYoussef/context-cascade](https://github.com/DNYoussef/context-cascade)
- **License:** MIT

Install and usage instructions live in the source repository linked above.