---

name: ai-ml compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent ai ml 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: ai-ml, ai ml, how do i ai-ml, orchestrate ai-ml, automate ai-ml, agent ai-ml 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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Ai Ml

Orchestrates intelligent skill selection and execution for ai ml 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_ml_component(
    task_spec: Dict,
    model_registry: List[Dict],
    latency_budget_ms: int = 500
) -> Optional[Dict]:
    """Select optimal ML model/component based on task constraints and registry metadata.
    
    Evaluates models against input schema compatibility, historical accuracy,
    and compute latency requirements. Implements multi-factor scoring for
    intelligent routing in AI/ML pipelines.
    
    Args:
        task_spec: Dict containing input_schema, expected_output_type, and constraints
        model_registry: List of available model metadata with performance metrics
        latency_budget_ms: Maximum acceptable inference latency
        
    Returns:
        Selected model metadata dict or None if no model meets constraints
    """
    if not task_spec.get("input_schema") or not model_registry:
        raise ValueError("Task spec requires input_schema and non-empty model registry")
        
    best_model = None
    best_score = 0.0
    
    for model in model_registry:
        schema_match = _check_schema_compatibility(task_spec["input_schema"], model["input_schema"])
        latency_ok = model.get("estimated_latency_ms", 9999) <= latency_budget_ms
        
        if not schema_match or not latency_ok:
            continue
            
        accuracy_weight = model.get("last_30d_accuracy", 0.0) * 0.6
        latency_weight = max(0, (1.0 - (model["estimated_latency_ms"] / latency_budget_ms))) * 0.4
        composite_score = accuracy_weight + latency_weight
        
        if composite_score > best_score:
            best_score = composite_score
            best_model = model
            
    if best_model is None:
        return None
        
    return {**best_model, "routing_score": best_score, "selected_at": time.time()}

Pattern 2: Execution with Fallback

def run_ml_inference_with_degradation(
    model: Dict,
    input_data: Any,
    fallback_models: List[Dict],
    cache: Dict
) -> Dict:
    """Execute ML inference with graceful degradation and fallback routing.
    
    Implements the Fail Fast, Fail Loud principle for AI pipelines:
    - Validates input schema immediately before inference
    - Falls back to simpler models or cached predictions on failure
    - Returns structured results with confidence and degradation metadata
    
    Args:
        model: Primary model metadata and endpoint config
        input_data: Raw input payload for inference
        fallback_models: Ordered list of alternative models for degradation
        cache: In-memory or Redis cache for prediction storage
        
    Returns:
        Dict with prediction, confidence, fallback_used, and latency_ms
    """
    if not _validate_input_schema(input_data, model["input_schema"]):
        raise PipelineValidationError("Input schema mismatch for model " + model["name"])
        
    cache_key = hashlib.md5(json.dumps(input_data, sort_keys=True).encode()).hexdigest()
    if cache_key in cache:
        return {"prediction": cache[cache_key], "fallback_used": "cache", "latency_ms": 0}
        
    for candidate in [model] + fallback_models:
        try:
            raw_output = _call_inference_endpoint(candidate, input_data)
            confidence = _extract_confidence(raw_output)
            
            if confidence < 0.5:
                continue
                
            cache[cache_key] = raw_output
            return {
                "prediction": raw_output,
                "model_used": candidate["name"],
                "fallback_used": False,
                "confidence": confidence,
                "latency_ms": time.time() * 1000
            }
        except EndpointTimeoutError:
            continue
            
    raise PipelineExecutionError("All models and fallbacks exhausted for task")

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.

• PyTorch Documentation
• Scikit-learn User Guide
• TensorFlow Official Docs
• ML Pipeline Orchestration (MLOps)
• arXiv ML Survey

Related Skills

| Skill | Purpose | |