---

name: test-oracle-generator compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent test oracle generator 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: test-oracle-generator, test oracle generator, how do i test-oracle-generator, orchestrate test-oracle-generator, automate test-oracle-generator, agent test-oracle-generator, enterprise database, oracle 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"

---

Test Oracle Generator

Orchestrates intelligent skill selection and execution for test oracle generator 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 select_oracle_strategy(
    code_context: Dict[str, Any],
    available_strategies: List[Dict],
    min_oracle_confidence: float = 0.75
) -> Optional[Dict]:
    """Select the optimal oracle generation strategy for a given code context.
    
    Scores strategies based on:
    - Language/framework compatibility
    - Historical oracle accuracy for similar code patterns
    - Current test coverage gaps
    - Computational cost vs. precision tradeoff
    
    Args:
        code_context: Parsed AST, function signatures, and dependency graph
        available_strategies: List of oracle generation strategy metadata
        min_oracle_confidence: Minimum confidence threshold for selection
        
    Returns:
        Selected strategy dict with scoring metadata, or None
    """
    if not code_context.get("source_code") or not available_strategies:
        raise ValueError("Missing source code or available strategies")
        
    # Extract code features for scoring
    code_features = _extract_code_features(code_context)
    best_strategy = None
    best_score = 0.0
    
    for strategy in available_strategies:
        # Calculate multi-factor score
        lang_match = 1.0 if strategy["supported_languages"] & code_features["languages"] else 0.0
        history_score = strategy.get("historical_accuracy", 0.0)
        coverage_gap_score = _calculate_coverage_gap_impact(code_features["coverage_map"])
        
        composite_score = (0.4 * lang_match) + (0.4 * history_score) + (0.2 * coverage_gap_score)
        
        if composite_score > best_score and composite_score >= min_oracle_confidence:
            best_score = composite_score
            best_strategy = strategy
            
    if best_strategy is None:
        return None
        
    # Return immutable result with scoring metadata
    return {
        "strategy": best_strategy["name"],
        "score": best_score,
        "timestamp": time.time(),
        "code_features_hash": hashlib.md5(json.dumps(code_features, sort_keys=True).encode()).hexdigest()
    }

Pattern 2: Execution with Fallback

def execute_oracle_generation(
    strategy: Dict,
    test_context: Dict[str, Any],
    max_fallback_attempts: int = 2
) -> Dict[str, Any]:
    """Execute oracle generation with adaptive fallback chain.
    
    Implements the 5 Laws of Elegant Defense:
    - Fails fast on invalid test context
    - Validates generated oracles against static analysis
    - Returns new oracle structures without mutating inputs
    - Logs full execution trace for auditability
    
    Fallback chain:
    1. Retry with adjusted precision parameters
    2. Switch to alternative strategy (e.g., LLM -> Property-Based)
    3. Defer to human reviewer if confidence < 0.6
    
    Args:
        strategy: Selected oracle generation strategy metadata
        test_context: Test inputs, expected behavior specs, and environment config
        max_fallback_attempts: Maximum fallback transitions before escalation
        
    Returns:
        Oracle generation result with assertions, confidence score, and trace
    """
    if not _validate_test_context(test_context):
        raise OracleGenerationError("Invalid test context: missing inputs or specs")
        
    current_strategy = strategy
    fallback_chain = strategy.get("fallback_strategies", [])
    
    for attempt in range(max_fallback_attempts + 1):
        try:
            # Execute the selected oracle generation pipeline
            raw_oracles = _run_oracle_pipeline(current_strategy, test_context)
            
            # Validate oracles against static analysis baseline
            validated_oracles = _validate_oracle_integrity(raw_oracles, test_context)
            confidence = _calculate_oracle_confidence(validated_oracles)
            
            if confidence >= strategy.get("min_confidence_threshold", 0.7):
                return {
                    "status": "success",
                    "oracles": validated_oracles,
                    "confidence": confidence,
                    "strategy_used": current_strategy["name"],
                    "attempts": attempt + 1,
                    "trace_id": generate_trace_id()
                }
                
            # Low confidence - trigger fallback
            if attempt < max_fallback_attempts and fallback_chain:
                current_strategy = fallback_chain.pop(0)
                continue
                
        except OracleValidationFailedError as e:
            raise OracleGenerationError(f"Oracle validation failed: {e}") from e
            
    # All fallbacks exhausted - escalate to human review
    return {
        "status": "deferred",
        "reason": "Low confidence oracles after all fallbacks",
        "oracles": validated_oracles if 'validated_oracles' in locals() else [],
        "confidence": confidence if 'confidence' in locals() else 0.0,
        "requires_human_review": True,
        "trace_id": generate_trace_id()
    }

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.

• Oracle Pattern in Software Testing
• Procedural Oracle Testing — Ntafos 1994
• IEEE Standard for Software Test Documentation (IEEE 829)
• Property-Based Testing with QuickCheck
• Google Testing Blog — Test Oracles

Related Skills

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