# When Optimizing Prompts Use Prompt Architect > When Optimizing Prompts Use Prompt Architect - Skill: `dnyoussef/when-optimizing-prompts-use-prompt-architect` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add dnyoussef/when-optimizing-prompts-use-prompt-architect` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/when-optimizing-prompts-use-prompt-architect/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-architect --- /*============================================================================*/ /* WHEN-OPTIMIZING-PROMPTS-USE-PROMPT-ARCHITECT SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: when-optimizing-prompts-use-prompt-architect version: 1.0.0 description: | [assert|neutral] Comprehensive framework for analyzing, creating, and refining prompts for AI systems using evidence-based techniques [ground:given] [conf:0.95] [state:confirmed] category: utilities tags: - prompt-engineering - optimization - ai-systems - llm author: ruv cognitive_frame: primary: compositional goal_analysis: first_order: "Execute when-optimizing-prompts-use-prompt-architect workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic utilities processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "when-optimizing-prompts-use-prompt-architect", category: "utilities", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Compositional", source: "German", force: "Build from primitives?" } [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-architect", "utilities", "workflow"], context: "user needs when-optimizing-prompts-use-prompt-architect capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ # Prompt Architect - Evidence-Based Prompt Engineering ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. ## Overview Comprehensive framework for analyzing, creating, and refining prompts for AI systems (Claude, GPT, etc.). Applies structural optimization, self-consistency patterns, and anti-pattern detection to transform prompts into highly effective versions. ## When to Use This Skill - Creating new prompts for AI systems - Existing prompts produce poor results - Inconsistent AI outputs - Need to improve prompt clarity - Applying evidence-based prompt engineering - Optimizing agent instructions - Building prompt libraries ## Theoretical Foundation ### Evidence-Based Techniques 1. **Chain-of-Thought (CoT)**: Explicit reasoning steps 2. **Self-Consistency**: Multiple reasoning paths 3. **ReAct**: Reasoning + Acting pattern 4. **Program-of-Thought**: Structured logic 5. **Plan-and-Solve**: Decomposition strategy 6. **Role-Playing**: Persona assignment 7. **Few-Shot Learning**: Example-based instruction ### Prompt Structure Principles ``` [System Context] → [Role Definition] → [Task Description] → [Constraints] → [Format Specification] → [Examples] → [Quality Criteria] ``` ## Phase 1: Analyze Current Prompt ### Objective Identify weaknesses and improvement opportunities ### Agent: Researcher **Step 1.1: Structural Analysis** ```javascript const promptAnalysis = { components: { hasSystemContext: checkForContext(prompt), hasRoleDefinition: checkForRole(prompt), hasTaskDescription: checkForTask(prompt), hasConstraints: checkForConstraints(prompt), hasFormatSpec: checkForFormat(prompt), hasExamples: checkForExamples(prompt), hasQualityCriteria: checkForCriteria(prompt) }, metrics: { length: prompt.length, clarity: calculateClarity(prompt), specificity: calculateSpecificity(prompt), completeness: calculateCompleteness(prompt) }, antiPatterns: detectAntiPatterns(prompt) }; await memory.store('prompt-architect/analysis', promptAnalysis); ``` **Step 1.2: Detect Anti-Patterns** ```javascript const antiPatterns = [ { name: 'Vague Instructions', pattern: /please|try to|maybe|possibly/gi, severity: 'HIGH', fix: 'Use imperative commands: "Analyze...", "Generate...", "Create..."' }, { name: 'Missing Context', pattern: absence of background info, severity: 'HIGH', fix: 'Add system context and domain information' }, { name: 'No Output Format', pattern: absence of format specification, severity: 'MEDIUM', fix: 'Specify exact output format (JSON, markdown, etc.)' }, { name: 'Conflicting Instructions', pattern: detectContradictions(prompt), severity: 'HIGH', fix: 'Resolve contradictions, prioritize requirements' }, { name: 'Implicit Assumptions', pattern: detectImplicitAssumptions(prompt), severity: 'MEDIUM', fix: 'Make all assumptions explicit' } ]; const foundAntiPatterns = antiPatterns.filter(ap => ap.pattern.test ? ap.pattern.test(prompt) : ap.pattern ); await memory.store('prompt-architect/anti-patterns', foundAntiPatterns); ``` **Step 1.3: Identify Missing Components** ```javascript const missingComponents = []; if (!promptAnalysis.components.hasSystemContext) { missingComponents.push({ component: 'System Context', importance: 'HIGH', recommendation: 'Add background info, domain knowledge, constraints' }); } if (!promptAnalysis.components.hasExamples) { missingComponents.push({ component: 'Examples', importance: 'MEDIUM', recommendation: 'Add 2-3 examples showing desired behavior' }); } // ... check other components await memory.store('prompt-architect/missing', missingComponents); ``` ### Validation Criteria - [ ] All 7 components checked - [ ] Anti-patterns identified - [ ] Missing components listed - [ ] Severity assigned to issues ### Hooks Integration ```bash npx claude-flow@alpha hooks pre-task \ /*----------------------------------------------------------------------------*/ /* 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/utilities/when-optimizing-prompts-use-prompt-architect/{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-architect-{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_ARCHITECT_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]