Ml

---
name: ml
description: /============================================================================/
---


/*============================================================================*/
/* 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

```bash
# 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] <promise>ML_VERILINGUA_VERIX_COMPLIANT</promise> [ground:self-validation] [conf:0.99] [state:confirmed]