# Verification Before Completion > Implements intelligent verification before completion with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/verification-before-completion` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/verification-before-completion` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/verification-before-completion/raw - Safety review: pending - 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/verification-before-completion --- # Verification Before Completion Orchestrates intelligent skill selection and execution for verification before completion 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 ```python def verify_task_readiness( task_context: Dict, verification_rules: List[Dict], strict_mode: bool = True ) -> Dict: """Run pre-completion verification checks against task context. Implements Law 2 (Parse at boundary) and Law 4 (Fail fast on invalid states). Returns a verification report with pass/fail status and actionable details. """ if not task_context or not verification_rules: raise ValueError("Task context and verification rules are required") verification_report = { "status": "pending", "checks_passed": [], "checks_failed": [], "warnings": [], "timestamp": time.time() } for rule in verification_rules: rule_name = rule.get("name", "unnamed") rule_type = rule.get("type", "schema") passed = False try: if rule_type == "schema": required_fields = rule.get("required_fields", []) passed = all(field in task_context for field in required_fields) elif rule_type == "dependency": deps = rule.get("required_deps", []) passed = all(dep in task_context.get("available_resources", []) for dep in deps) elif rule_type == "state": expected = rule.get("expected_state") passed = task_context.get("current_state") == expected else: passed = True if passed: verification_report["checks_passed"].append(rule_name) else: verification_report["checks_failed"].append(rule_name) if strict_mode: verification_report["status"] = "failed" return verification_report except Exception as e: verification_report["warnings"].append(f"{rule_name}: {str(e)}") verification_report["status"] = "passed" if not verification_report["checks_failed"] else "failed" return verification_report ``` ### Pattern 2: Execution with Fallback ```python def execute_with_verification_fallback( task_context: Dict, verification_rules: List[Dict], fallback_handlers: Dict[str, Callable], max_verification_retries: int = 2 ) -> Dict: """Execute task with verification gates and domain-specific fallbacks. Implements Law 1 (Early exit on invalid state) and Law 3 (Atomic returns). Applies verification-specific fallbacks when pre-completion checks fail. """ if not task_context: raise ValueError("Task context cannot be empty") for attempt in range(max_verification_retries + 1): # Law 2: Parse and verify at boundary verification_report = verify_task_readiness(task_context, verification_rules) if verification_report["status"] == "passed": # Proceed to execution only after verification passes execution_result = _run_task_execution(task_context) return { "success": True, "verification_report": verification_report, "execution_result": execution_result, "attempts": attempt + 1 } # Verification failed - apply domain-specific fallback fallback_type = _determine_fallback_type(verification_report) if fallback_type in fallback_handlers: task_context = fallback_handlers[fallback_type](task_context, verification_report) continue # Law 4: Fail loud if no fallback available raise VerificationError( f"Verification failed after {attempt + 1} attempts. " f"Failed checks: {verification_report['checks_failed']}" ) raise VerificationError("Max verification retries exhausted without passing checks") ``` ### 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 --- ## 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. - [Google Verification Before Completion — Software Engineering Practices](https://ai.google/gemini-api/docs/verification) - [IEEE 1012 — System and Software Verification and Validation Standard](https://standards.ieee.org/standard/1012-2016.html) - [Formal Methods in Software Verification](https://en.wikipedia.org/wiki/Formal_verification) - [Property-Based Verification with Hypothesis](https://hypothesis.works/) - [AWS Well-Architected Framework — Reliability Pillar](https://docs.aws.amazon.com/wellarchitected/latest/reliability-pillar/welcome.html) ## Related Skills | Skill | Purpose | |