Code Correctness Verifier

Code Correctness Verifier

Orchestrates intelligent skill selection and execution for code correctness verifier 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

import ast
import hashlib
from datetime import datetime
from typing import List, Optional
from dataclasses import dataclass, field

@dataclass
class Rule:
    name: str
    severity: str
    pattern: str

@dataclass
class Violation:
    rule_name: str
    line_no: int
    severity: str
    message: str

@dataclass
class VerificationReport:
    source_hash: str
    total_lines: int
    violations: List[Violation] = field(default_factory=list)
    is_correct: bool = False
    timestamp: str = ""

def verify_code_correctness(source_code: str, rules: List[Rule]) -> VerificationReport:
    """Verify source code against a set of correctness rules.
    
    Implements the 5 Laws of Elegant Defense:
    - Law 1: Early exit on empty or malformed input
    - Law 2: Parse code into AST to make illegal states unrepresentable
    - Law 3: Return new VerificationReport, never mutate input rules
    - Law 4: Fail immediately on syntax errors or unsupported constructs
    """
    if not source_code or not source_code.strip():
        raise ValueError("Source code cannot be empty")
        
    try:
        tree = ast.parse(source_code, type_comments=True)
    except SyntaxError as e:
        raise CodeVerificationError(f"Syntax error at line {e.lineno}: {e.msg}") from e
        
    violations = analyze_ast_for_violations(tree, rules)
    
    # Atomic Predictability - construct new report
    report = VerificationReport(
        source_hash=hashlib.sha256(source_code.encode()).hexdigest(),
        total_lines=len(source_code.splitlines()),
        violations=violations,
        is_correct=len(violations) == 0,
        timestamp=datetime.utcnow().isoformat()
    )
    return report

Pattern 2: Execution with Fallback

class RuleVisitor(ast.NodeVisitor):
    def __init__(self, rules: List[Rule]):
        self.rules = rules
        self.violations: List[Violation] = []
        
    def visit(self, node: ast.AST) -> None:
        super().visit(node)
        
    def get_violations(self) -> List[Violation]:
        return self.violations

def analyze_ast_for_violations(tree: ast.AST, rules: List[Rule]) -> List[Violation]:
    """Traverse AST and apply correctness rules to detect violations.
    
    Implements Fail Fast, Fail Loud:
    - Invalid rule configurations halt immediately
    - No silent suppression of critical violations
    """
    if not rules:
        raise ValueError("At least one correctness rule must be provided")
        
    visitor = RuleVisitor(rules)
    visitor.visit(tree)
    
    violations = visitor.get_violations()
    
    # Sort by severity and line number for deterministic output
    violations.sort(key=lambda v: (v.severity, v.line_no))
    
    # Apply minimum 2-level fallback chain concept:
    # 1. Critical violations block execution immediately
    # 2. Warning violations are logged but allow continuation
    critical_violations = [v for v in violations if v.severity == "CRITICAL"]
    if critical_violations:
        raise CodeVerificationError(
            f"Critical correctness violations found: {len(critical_violations)}"
        )
        
    return violations

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.

• Program Verification Survey (ArXiv:2305.14942)
• Wikipedia — Formal Verification
• Coq Proof Assistant — Official Documentation
• Isabelle/HOL — Theorem Prover Guide
• Software Model Checking (Springer Handbook)