# Ask Questions If Underspecified > Implements intelligent ask questions if underspecified with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/ask-questions-if-underspecified` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/ask-questions-if-underspecified` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/ask-questions-if-underspecified/raw - Safety review: PASS (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `behavioral-modes` | Behavioral mode selection for agent conversations | --- --- ## 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.)](https://arxiv.org/abs/2203.11171) - [PromptingGuide — Self-Consistency Technique](https://www.promptingguide.ai/techniques/self_consistency) - [LLM Prompt Engineering for Clarification (LangChain Blog)](https://blog.langchain.dev/prompt-engineering/) - [Clarifying Ambiguity in LLM Interactions (ACL Anthology)](https://aclanthology.org/2023.findings-acl.678/) - [Interactive Questioning Strategies for AI Assistants](https://arxiv.org/abs/2310.05029)