/============================================================================/ /* ML SKILL :: VERILINGUA x VERIX EDITION / /============================================================================*/

name: ml version: 2.0.0 description: | [assert|neutral] Machine Learning development workflow with experiment tracking, hyperparameter optimization, and MLOps integration [ground:given] [conf:0.95] [state:confirmed] category: specialized-development tags:

• machine-learning
• mlops
• experiment-tracking
• hyperparameter-tuning
• model-registry author: ruv cognitive_frame: primary: aspectual goal_analysis: first_order: "Execute ml workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic specialized-development processes"

/----------------------------------------------------------------------------/ /* S0 META-IDENTITY / /----------------------------------------------------------------------------*/

[define|neutral] SKILL := { name: "ml", category: "specialized-development", version: "2.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S1 COGNITIVE FRAME / /----------------------------------------------------------------------------*/

[define|neutral] COGNITIVE_FRAME := { frame: "Aspectual", source: "Russian", force: "Complete or ongoing?" } [ground:cognitive-science] [conf:0.92] [state:confirmed]

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

/----------------------------------------------------------------------------/ /* S2 TRIGGER CONDITIONS / /----------------------------------------------------------------------------*/

[define|neutral] TRIGGER_POSITIVE := { keywords: ["ml", "specialized-development", "workflow"], context: "user needs ml capability" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S3 CORE CONTENT / /----------------------------------------------------------------------------*/

ML Development Skill

Kanitsal Cerceve (Evidential Frame Activation)

Kaynak dogrulama modu etkin.

When to Use This Skill

• Model Training: Training neural networks or ML models
• Hyperparameter Tuning: Optimizing model performance
• Model Debugging: Diagnosing training issues (overfitting, vanishing gradients)
• Data Pipeline: Building training/validation data pipelines
• Experiment Tracking: Managing ML experiments and metrics
• Model Deployment: Serving models in production

When NOT to Use This Skill

• Data Analysis: Exploratory data analysis or statistics (use data scientist)
• Data Engineering: Large-scale ETL or data warehouse (use data engineer)
• Research: Novel algorithm development (use research specialist)
• Simple Rules: Heuristic-based logic without ML

Success Criteria

• Model achieves target accuracy/F1/RMSE on validation set
• Training/validation curves show healthy convergence
• No overfitting (train/val gap <5%)
• Inference latency meets production requirements
• Model size within deployment constraints
• Experiment tracked with metrics and artifacts (MLflow, Weights & Biases)
• Reproducible results (fixed random seeds, versioned data)

Edge Cases to Handle

• Class Imbalance: Unequal class distribution requiring resampling
• Data Leakage: Information from validation/test leaking into training
• Catastrophic Forgetting: Model forgetting old tasks when learning new ones
• Adversarial Examples: Model vulnerable to adversarial attacks
• Distribution Shift: Training data differs from production data
• Hardware Constraints: GPU memory limitations or mixed precision training

Guardrails

• NEVER evaluate on training data
• ALWAYS use separate train/validation/test splits
• NEVER touch test set until final evaluation
• ALWAYS version datasets and models
• NEVER deploy without monitoring for data drift
• ALWAYS document model assumptions and limitations
• NEVER train on biased or unrepresentative data

Evidence-Based Validation

• Confusion matrix reviewed for class-wise performance
• Learning curves plotted (loss vs epochs)
• Validation metrics tracked across experiments
• Model profiled for inference time (TensorBoard, PyTorch Profiler)
• Ablation studies conducted for architecture choices
• Cross-validation performed for robust evaluation
• Statistical significance tested (t-test, bootstrap)

Comprehensive machine learning development workflow with enterprise-grade experiment tracking, automated hyperparameter optimization, model registry management, and production MLOps pipelines.

Overview

This Gold-tier skill provides a complete ML development lifecycle with:

• Experiment Tracking: MLflow/W&B integration for reproducible experiments
• Hyperparameter Optimization: Optuna/Ray Tune for automated tuning
• Model Registry: Centralized model versioning and deployment
• MLOps Pipeline: Production-ready model serving and monitoring

Quick Start

# Initialize ML project
npx claude-flow sparc run ml "Create ML project for image classification"

# Track experiment
python resources/scripts/experiment-tracker.py --config experiment-config.yaml

# Optimize hyperparameters
node resources/scripts/hyperparameter-tuner.js --space hyperparameter-space.json

# Deploy model
bash resources/scripts/model-registry.sh deploy production latest

Workflow Phases

1. Experiment Design

• Define hypothesis and metrics
• Configure experiment tracking
• Set up data pipelines
• Validate data quality

2. Model Development

• Implement model architecture
• Configure training pipeline
• Set up validation strategy
• Enable experiment logging

3. Hyperparameter Optimization

• Define search space
• Select optimization algorithm
• Run distributed trials
• Analyze results

4. Model Evaluation

• Comprehensive metrics analysis

/----------------------------------------------------------------------------/ /* S4 SUCCESS CRITERIA / /----------------------------------------------------------------------------*/

[define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S5 MCP INTEGRATION / /----------------------------------------------------------------------------*/

[define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed]

/----------------------------------------------------------------------------/ /* S6 MEMORY NAMESPACE / /----------------------------------------------------------------------------*/

[define|neutral] MEMORY_NAMESPACE := { pattern: "skills/specialized-development/ml/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed]

[define|neutral] MEMORY_TAGGING := { WHO: "ml-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S7 SKILL COMPLETION VERIFICATION / /----------------------------------------------------------------------------*/

[direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* S8 ABSOLUTE RULES / /----------------------------------------------------------------------------*/

[direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed]

[direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed]

/----------------------------------------------------------------------------/ /* PROMISE / /----------------------------------------------------------------------------*/

[commit|confident] ML_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]