Viboscope

Viboscope

Orchestrates intelligent skill selection and execution for viboscope 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 viboscope_select_module(
    signal_metadata: Dict[str, Any],
    available_modules: List[Dict],
    calibration_threshold: float = 0.85
) -> Optional[Dict]:
    """Select optimal viboscope processing module based on signal characteristics.
    
    Evaluates modules against raw signal features: frequency range, amplitude variance,
    and sensor calibration status. Applies multi-factor scoring to route signals
    through the most accurate processing pipeline.
    
    Args:
        signal_metadata: Parsed signal features (freq_range_hz, amplitude_std, sensor_id)
        available_modules: List of viboscope module configs with capabilities
        calibration_threshold: Minimum calibration match required for selection
        
    Returns:
        Selected module dict with routing metadata or None
    """
    # Guard clause - Early Exit (Law 1)
    if not signal_metadata or not available_modules:
        raise ValueError("Signal metadata and module registry required")
        
    best_module = None
    best_score = 0.0
    
    for module in available_modules:
        freq_match = _calculate_frequency_alignment(signal_metadata["freq_range_hz"], module["supported_hz"])
        amp_match = _calculate_amplitude_compatibility(signal_metadata["amplitude_std"], module["dynamic_range"])
        cal_score = _verify_sensor_calibration(signal_metadata["sensor_id"], module["calibrated_sensors"])
        
        composite_score = (freq_match * 0.5) + (amp_match * 0.3) + (cal_score * 0.2)
        
        if composite_score > best_score and cal_score >= calibration_threshold:
            best_score = composite_score
            best_module = module
            
    if best_module is None:
        return None
    
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    return {
        "module_id": best_module["id"],
        "routing_score": best_score,
        "signal_hash": hashlib.md5(json.dumps(signal_metadata, sort_keys=True).encode()).hexdigest(),
        "timestamp": time.time()
    }

Pattern 2: Execution with Fallback

def viboscope_execute_pipeline(
    selected_module: Dict,
    raw_signal_data: bytes,
    fallback_config: Dict[str, Any]
) -> Dict[str, Any]:
    """Execute viboscope signal processing pipeline with domain-specific fallbacks.
    
    Runs the selected module against raw vibration data. Implements graceful degradation
    when signal quality drops or hardware latency exceeds thresholds.
    
    Args:
        selected_module: Output from viboscope_select_module
        raw_signal_data: Raw byte stream from vibroscope sensor
        fallback_config: Fallback routing rules and degradation parameters
        
    Returns:
        Processed signal dict with quality metrics and routing history
    """
    pipeline_state = {"attempts": 0, "degradation_level": 0, "module_id": selected_module["module_id"]}
    
    for attempt in range(fallback_config.get("max_retries", 3)):
        pipeline_state["attempts"] += 1
        try:
            # Apply module-specific signal transformation
            processed = _apply_viboscope_transform(raw_signal_data, selected_module["module_id"])
            
            # Validate output integrity
            quality_score = _calculate_signal_to_noise_ratio(processed)
            
            if quality_score >= fallback_config.get("min_quality_threshold", 0.7):
                return {
                    "status": "success",
                    "processed_signal": processed,
                    "quality_score": quality_score,
                    "routing_path": [selected_module["module_id"]],
                    "pipeline_state": pipeline_state
                }
                
            # Signal degraded - trigger adaptive fallback
            raw_signal_data = _apply_noise_filtering(raw_signal_data)
            selected_module = fallback_config["adaptive_modules"][pipeline_state["degradation_level"]]
            pipeline_state["degradation_level"] += 1
            
        except SensorDriftError as e:
            # Hardware drift detected - switch to reference calibration
            raw_signal_data = _apply_reference_calibration(raw_signal_data, fallback_config["reference_sensor"])
            continue
            
        except HardwareTimeoutError:
            if attempt == fallback_config.get("max_retries", 3) - 1:
                raise PipelineExecutionError("Viboscope pipeline exhausted all hardware retries")
            time.sleep(fallback_config.get("backoff_seconds", 0.5))
            
    return {
        "status": "degraded",
        "processed_signal": processed,
        "quality_score": quality_score,
        "routing_path": pipeline_state.get("fallback_chain", []),
        "pipeline_state": pipeline_state
    }

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.

• ISO 10816 — Mechanical Vibration Evaluation Standards
• IEEE Std 1057 — Digital Waveform Measurements
• Fast Fourier Transform (FFT) Algorithm — Cooley & Tukey 1965
• Scipy Signal Processing Documentation
• Vibration Analysis for Predictive Maintenance — NIST

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