Ask Questions If Underspecified

Ask Questions If Underspecified

Orchestrates intelligent skill selection and execution for ask questions if underspecified 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: Request Completeness & Question Generation

def assess_request_completeness(
    user_input: str,
    required_fields: List[str],
    ambiguity_threshold: float = 0.6
) -> Dict:
    """Assess if a user request is underspecified and generate clarifying questions.
    
    Implements Law 2 (Parse at boundary) by validating input against schema.
    Implements Law 1 (Early Exit) by returning immediately if fully specified.
    
    Args:
        user_input: Raw text from the user
        required_fields: List of expected parameters/entities
        ambiguity_threshold: Confidence score below which a field is considered ambiguous
        
    Returns:
        Dict with 'is_complete', 'missing_fields', 'ambiguous_fields', 'clarifying_questions'
    """
    # Early exit if input is empty (Law 1)
    if not user_input or not user_input.strip():
        return {
            "is_complete": False,
            "missing_fields": required_fields,
            "ambiguous_fields": [],
            "clarifying_questions": ["Please provide a complete request."]
        }
    
    # Parse and extract entities (Law 2)
    extracted = _extract_entities(user_input)
    missing = [f for f in required_fields if f not in extracted]
    ambiguous = [f for f in extracted if extracted[f].get("confidence", 1.0) < ambiguity_threshold]
    
    # Early exit if fully specified
    if not missing and not ambiguous:
        return {"is_complete": True, "missing_fields": [], "ambiguous_fields": [], "clarifying_questions": []}
    
    # Generate domain-specific clarifying questions
    questions = []
    for field in missing:
        questions.append(f"What is the value for '{field}'?")
    for field in ambiguous:
        questions.append(f"Could you clarify the expected format/value for '{field}'?")
        
    # Return new structure (Law 3)
    return {
        "is_complete": False,
        "missing_fields": missing,
        "ambiguous_fields": ambiguous,
        "clarifying_questions": questions
    }

Pattern 2: Clarification Routing & Fallback

def route_underspecified_request(
    assessment: Dict,
    conversation_history: List[Dict],
    max_clarification_rounds: int = 3
) -> Dict:
    """Route underspecified requests through clarification or fallback chains.
    
    Implements Law 4 (Fail Fast/Loud) by escalating after max rounds.
    Implements adaptive fallback based on conversation context.
    
    Args:
        assessment: Output from assess_request_completeness
        conversation_history: Previous turns to avoid repetitive questions
        max_clarification_rounds: Threshold before deferring to human/simpler path
        
    Returns:
        Routing decision with action, payload, and metadata
    """
    # Guard clause - validate assessment structure
    if not assessment.get("is_complete"):
        # Check if we've exceeded clarification rounds
        clarification_count = sum(1 for turn in conversation_history if turn.get("type") == "clarification")
        if clarification_count >= max_clarification_rounds:
            # Fallback: Defer to human or simplified execution path
            return {
                "action": "defer_to_human",
                "reason": "Max clarification rounds exceeded",
                "payload": {"original_request": assessment.get("clarifying_questions")},
                "metadata": {"fallback_level": 2, "confidence": 0.3}
            }
        
        # Fallback Level 1: Retry with simplified/rephrased questions
        simplified_questions = _simplify_questions(assessment.get("clarifying_questions", []))
        return {
            "action": "ask_clarification",
            "reason": "Request underspecified",
            "payload": {"questions": simplified_questions},
            "metadata": {"fallback_level": 1, "confidence": 0.8}
        }
    
    # Fully specified - proceed to execution pipeline
    return {
        "action": "proceed_to_execution",
        "reason": "Request complete",
        "payload": {"validated_context": _build_execution_context(assessment)},
        "metadata": {"fallback_level": 0, "confidence": 0.95}
    }

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 skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Self-Consistency Improves Chain of Thought Reasoning (Huang et al.)
• PromptingGuide — Self-Consistency Technique
• LLM Prompt Engineering for Clarification (LangChain Blog)
• Clarifying Ambiguity in LLM Interactions (ACL Anthology)
• Interactive Questioning Strategies for AI Assistants