---

name: verification-before-completion compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent verification before completion with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: verification-before-completion, verification before completion, how do i verification-before-completion, orchestrate verification-before-completion, automate verification-before-completion, agent verification-before-completion archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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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

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

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

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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
• IEEE 1012 — System and Software Verification and Validation Standard
• Formal Methods in Software Verification
• Property-Based Verification with Hypothesis
• AWS Well-Architected Framework — Reliability Pillar

Related Skills

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