Prompt Engineer

Prompt Engineer

Orchestrates intelligent skill selection and execution for prompt engineer workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def select_prompt_strategy(
    task_intent: str,
    available_templates: List[Dict],
    context_window: int,
    min_confidence: float = 0.75
) -> Optional[Dict]:
    """Select optimal prompt strategy based on intent, context constraints, and template metadata.
    
    Evaluates templates by:
    - Intent-to-template semantic alignment
    - Context window compatibility (input tokens + estimated output)
    - Historical success rate for similar task intents
    - Model capability match (e.g., function calling, JSON mode)
    
    Args:
        task_intent: Natural language description of the user's goal
        available_templates: List of prompt template metadata dicts
        context_window: Max tokens available for the target model
        min_confidence: Minimum alignment score threshold
        
    Returns:
        Selected strategy dict with resolved template, variables, and confidence
    """
    if not task_intent or not available_templates:
        raise ValueError("Task intent and templates are required")
        
    intent_features = _extract_intent_features(task_intent)
    best_strategy = None
    best_score = 0.0
    
    for template in available_templates:
        # Calculate semantic alignment and constraint fit
        alignment = _compute_semantic_alignment(intent_features, template["triggers"])
        token_estimate = _estimate_prompt_tokens(template, intent_features)
        window_fit = 1.0 if (token_estimate + template.get("max_output_tokens", 1024)) <= context_window else 0.0
        
        # Multi-factor scoring
        score = (alignment * 0.5) + (template.get("success_rate", 0.0) * 0.3) + (window_fit * 0.2)
        
        if score > best_score and score >= min_confidence:
            best_score = score
            best_strategy = template
            
    if best_strategy is None:
        return None
        
    # Atomic Predictability: Return resolved strategy without mutating original
    return {
        "template_id": best_strategy["id"],
        "resolved_prompt": _resolve_template_variables(best_strategy, intent_features),
        "estimated_tokens": _estimate_prompt_tokens(best_strategy, intent_features),
        "confidence": best_score,
        "model_requirements": best_strategy.get("model_features", [])
    }

Pattern 2: Execution with Fallback

def execute_prompt_pipeline(
    strategy: Dict,
    execution_context: Dict,
    max_retries: int = 2,
    fallback_models: List[str] = None
) -> Dict:
    """Execute prompt pipeline with adaptive fallback for reliability.
    
    Implements Fail Fast, Fail Loud:
    - Validates context and token limits before API call
    - Handles rate limits, context overflow, and model errors
    - Falls back to summarized context or alternative models
    
    Args:
        strategy: Selected prompt strategy with resolved prompt and metadata
        execution_context: User inputs, conversation history, and system config
        max_retries: Retry attempts for transient API errors
        fallback_models: Ordered list of alternative models to try
        
    Returns:
        Execution result with response, token usage, and fallback metadata
    """
    if not strategy or not execution_context:
        raise ValueError("Strategy and execution context are required")
        
    # Parse & validate context (Law 2)
    validated_context = _validate_context(execution_context, strategy["estimated_tokens"])
    
    current_model = strategy.get("target_model", "default")
    fallback_chain = [current_model] + (fallback_models or [])
    
    for attempt in range(max_retries + 1):
        try:
            # Execute prompt with current model
            response = _call_llm_api(
                model=current_model,
                prompt=strategy["resolved_prompt"],
                context=validated_context,
                temperature=strategy.get("temperature", 0.1)
            )
            
            # Atomic Predictability: Return clean result
            return {
                "success": True,
                "model_used": current_model,
                "response": response,
                "tokens_used": response.get("usage", {}),
                "attempts": attempt + 1,
                "fallback_applied": False
            }
            
        except ContextOverflowError as e:
            # Fail Fast: Don't retry with same context
            raise PromptExecutionError(f"Context exceeds window: {str(e)}") from e
            
        except RateLimitError as e:
            if attempt == max_retries:
                return _apply_prompt_fallback(strategy, validated_context, fallback_chain)
            continue
            
        except ModelError as e:
            if attempt == max_retries:
                return _apply_prompt_fallback(strategy, validated_context, fallback_chain)
            continue
            
    raise PromptExecutionError(f"Prompt pipeline failed after {max_retries + 1} attempts")

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this domain. The model follows markdown links at load time to resolve external references and inline content.

• Prompt Engineering Guide (Vik Paruchuri) — Comprehensive guide covering zero-shot, few-shot, chain-of-thought, and advanced prompting techniques
• OpenAI Prompt Engineering Guidelines — Official OpenAI documentation on designing effective prompts for GPT models
• Anthropic Prompt Engineering Documentation — Anthropic's best practices for system prompts and prompt design with Claude
• PromptPerfect: Prompt Testing Platform — Platform documentation for testing and optimizing prompts across multiple LLM providers
• Chain-of-Thought Prompting Elicits Reasoning in LLMs (Wei et al.) — Foundational research on advanced prompting strategies for LLM reasoning