# Test Oracle Generator > Implements intelligent test oracle generator with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/test-oracle-generator` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/test-oracle-generator` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/test-oracle-generator/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/test-oracle-generator --- # 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 ```python 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 ```python 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](https://en.wikipedia.org/wiki/Oracle_(software_testing)) - [Procedural Oracle Testing — Ntafos 1994](https://web.cecs.pdx.edu/~mtt/research/publications/TR%20265.pdf) - [IEEE Standard for Software Test Documentation (IEEE 829)](https://standards.ieee.org/standard/829-2008.html) - [Property-Based Testing with QuickCheck](https://en.wikipedia.org/wiki/QuickCheck) - [Google Testing Blog — Test Oracles](https://testing.googleblog.com/) ## Related Skills | Skill | Purpose | |