# Ml Pipeline Workflow > Implements intelligent ml pipeline workflow with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/ml-pipeline-workflow` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/ml-pipeline-workflow` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/ml-pipeline-workflow/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/ml-pipeline-workflow --- # Ml Pipeline Workflow Orchestrates intelligent skill selection and execution for ml pipeline workflow 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_pipeline_step( task_spec: Dict, available_steps: List[Dict], hardware_constraints: Dict ) -> Optional[Dict]: """Select optimal ML pipeline step based on task type, data schema, and hardware. Evaluates steps using multi-factor scoring: - Task compatibility (classification vs regression vs clustering) - Data schema alignment (feature types, dimensionality) - Hardware fit (GPU memory, CPU cores, storage I/O) - Historical performance on similar datasets Args: task_spec: ML task configuration (type, data_path, target_column) available_steps: List of ML step metadata (framework, algorithm, resource_req) hardware_constraints: Current system resources (gpu_mem_gb, cpu_cores) Returns: Selected step metadata or None if no compatible step exists """ # Guard clause - Early Exit (Law 1) if not task_spec.get("task_type") or not available_steps: raise ValueError("Task type and available steps are required") best_step = None best_score = 0.0 for step in available_steps: # Check hardware compatibility first (Make Illegal States Unrepresentable - Law 2) if step["resource_req"]["gpu_mem_gb"] > hardware_constraints.get("gpu_mem_gb", 0): continue # Calculate compatibility score task_match = _match_task_type(task_spec["task_type"], step["supported_tasks"]) schema_match = _validate_schema(task_spec.get("features", []), step["required_schema"]) perf_history = step.get("historical_rmse", 1.0) if task_spec["task_type"] == "regression" else 1.0 - step.get("historical_accuracy", 0.0) score = (task_match * 0.4) + (schema_match * 0.3) + (perf_history * 0.3) if score > best_score: best_score = score best_step = step if best_step is None: return None # Atomic Predictability (Law 3) - Return new dict, don't mutate return { **best_step, "selection_score": best_score, "selected_at": datetime.utcnow().isoformat(), "hardware_profile": hardware_constraints } ``` ### Pattern 2: Execution with Fallback ```python def execute_ml_step_with_fallback( step_config: Dict, run_context: Dict, max_retries: int = 2 ) -> Dict: """Execute ML pipeline step with resilience patterns for training/evaluation. Implements ML-specific fallback chain: 1. Retry with original hyperparameters 2. Fallback to simplified model architecture (e.g., linear vs neural net) 3. Use cached/precomputed features if training fails 4. Defer to human review for critical model validation failures Args: step_config: ML step configuration (algorithm, hyperparams, data_path) run_context: Execution context (session_id, output_dir, metrics_tracker) max_retries: Maximum retry attempts before fallback Returns: Execution result with model artifacts, metrics, and fallback metadata """ # Guard clause - validate config (Early Exit) if not step_config.get("algorithm"): raise ValueError("Algorithm specification is required for execution") validated_config = _normalize_ml_config(step_config) output_dir = run_context.get("output_dir", "/tmp/ml_run") for attempt in range(max_retries + 1): try: # Execute training/evaluation pipeline artifacts, metrics = _run_ml_pipeline(validated_config, output_dir) # Validate model quality (Fail Fast on poor performance - Law 4) if metrics.get("validation_loss", float('inf')) > run_context.get("max_acceptable_loss", 1.0): raise ModelQualityError(f"Validation loss {metrics['validation_loss']} exceeds threshold") return { "success": True, "step_executed": validated_config["algorithm"], "artifacts_path": artifacts, "metrics": metrics, "attempts": attempt + 1, "latency_sec": time.time() - run_context.get("start_time", time.time()) } except OutOfMemoryError as e: # Fallback: Reduce batch size or switch to CPU if attempt < max_retries: validated_config["batch_size"] = max(1, validated_config.get("batch_size", 32) // 2) continue return _apply_ml_fallback(validated_config, run_context, "memory_overflow") except ConvergenceError as e: # Fallback: Adjust learning rate or switch algorithm if attempt < max_retries: validated_config["learning_rate"] *= 0.5 continue return _apply_ml_fallback(validated_config, run_context, "convergence_failure") # All retries exhausted - Fail Loud (Law 4) raise PipelineExecutionError(f"ML step {validated_config['algorithm']} failed after {max_retries + 1} attempts") ``` ### 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 ## Related Skills | Skill | Purpose | | --- --- ## 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. - [MLflow Experiment Tracking and Pipeline Orchestration]() - [Apache Airflow ML Pipeline Guide]() - [Kubeflow ML Pipeline on Kubernetes]() - [DVC (Data Version Control) for ML Pipelines]() - [Scikit-learn Pipeline API]()