ai-ml

# 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 ```python 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 ```python 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 --- --- ## 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](<https://pytorch.org/docs/>) - [Scikit-learn User Guide](<https://scikit-learn.org/stable/user_guide.html>) - [TensorFlow Official Docs](<https://www.tensorflow.org/guide>) - [ML Pipeline Orchestration (MLOps)](<https://ml-ops.org/content/mlops-principles>) - [arXiv ML Survey](<https://arxiv.org/list/cs.LR/recent>) ## Related Skills | Skill | Purpose | |