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 cognitiveframe: primary: compositional goalanalysis: firstorder: "Execute when-optimizing-prompts-use-prompt-architect workflow" secondorder: "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

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

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

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

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]

## 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.