# Data Science and Machine Learning - Skills and Agents Backlog (Phase 4)

## Overview

This backlog identifies specialized skills and agents that could enhance the Data Science and Machine Learning processes beyond general-purpose capabilities. Each skill or agent is designed to provide domain-specific tooling, validation, or automation for ML workflows.

## Summary Statistics

- **Total Skills Identified**: 24
- **Total Agents Identified**: 18
- **Shared Candidates (Cross-Specialization)**: 8
- **Categories**: 6 (Data Engineering, Model Development, MLOps, Explainability, Monitoring, Validation)

---

## Skills

### Data Engineering Skills

#### 1. pandas-dataframe-analyzer
**Description**: Automated DataFrame analysis skill for statistical summaries, missing value detection, data type inference, and memory optimization recommendations.

**Capabilities**:
- Statistical profiling of DataFrames
- Missing value pattern detection
- Data type optimization suggestions
- Memory footprint analysis
- Duplicate detection and handling

**Used By Processes**:
- Exploratory Data Analysis (EDA) Pipeline
- Data Collection and Validation Pipeline
- Feature Engineering Design and Implementation

**Tools/Libraries**: pandas, pandas-profiling, ydata-profiling

---

#### 2. great-expectations-validator
**Description**: Data quality validation skill using Great Expectations for schema validation, expectation suites, and data documentation.

**Capabilities**:
- Expectation suite creation and execution
- Data documentation generation
- Checkpoint management
- Validation result reporting
- Custom expectation development

**Used By Processes**:
- Data Collection and Validation Pipeline
- Exploratory Data Analysis (EDA) Pipeline
- ML System Integration Testing

**Tools/Libraries**: Great Expectations, SQLAlchemy

---

#### 3. dvc-dataset-versioning
**Description**: Dataset versioning skill using DVC for tracking data changes, managing data pipelines, and ensuring reproducibility.

**Capabilities**:
- Dataset version tracking
- Data pipeline definition
- Remote storage management
- Reproducibility enforcement
- Data lineage tracking

**Used By Processes**:
- Data Collection and Validation Pipeline
- ML Model Retraining Pipeline
- Feature Store Implementation

**Tools/Libraries**: DVC, Git

---

#### 4. feast-feature-store
**Description**: Feature store management skill for online/offline feature serving, feature registration, and training-serving consistency.

**Capabilities**:
- Feature definition and registration
- Online feature serving setup
- Offline feature retrieval
- Point-in-time correctness validation
- Feature freshness monitoring

**Used By Processes**:
- Feature Store Implementation and Management
- Feature Engineering Design and Implementation
- Model Training Pipeline

**Tools/Libraries**: Feast, Redis, PostgreSQL

---

### Model Development Skills

#### 5. sklearn-model-trainer
**Description**: Scikit-learn model training skill with cross-validation, hyperparameter tuning, and pipeline construction.

**Capabilities**:
- Model training with cross-validation
- GridSearchCV / RandomizedSearchCV
- Pipeline construction
- Feature selection integration
- Model serialization (joblib/pickle)

**Used By Processes**:
- Model Training Pipeline with Experiment Tracking
- AutoML Pipeline Orchestration
- ML Architecture Design and Model Selection

**Tools/Libraries**: scikit-learn, joblib

---

#### 6. pytorch-trainer
**Description**: PyTorch model training skill with custom training loops, gradient management, and GPU optimization.

**Capabilities**:
- Custom training loop execution
- Learning rate scheduling
- Gradient clipping and accumulation
- Mixed precision training (AMP)
- Checkpoint management
- DataLoader optimization

**Used By Processes**:
- Model Training Pipeline with Experiment Tracking
- Distributed Training Orchestration
- AutoML Pipeline Orchestration

**Tools/Libraries**: PyTorch, PyTorch Lightning, torchvision

---

#### 7. tensorflow-trainer
**Description**: TensorFlow/Keras model training skill with callbacks, distributed strategies, and TensorBoard integration.

**Capabilities**:
- Keras model training with callbacks
- Custom training loops with tf.GradientTape
- Distribution strategy configuration
- TensorBoard logging
- SavedModel export
- TF Serving preparation

**Used By Processes**:
- Model Training Pipeline with Experiment Tracking
- Distributed Training Orchestration
- Model Deployment Pipeline

**Tools/Libraries**: TensorFlow, Keras, TensorBoard

---

#### 8. optuna-hyperparameter-tuner
**Description**: Hyperparameter optimization skill using Optuna with advanced search strategies and pruning.

**Capabilities**:
- TPE, CMA-ES, and grid sampling
- Multi-objective optimization
- Hyperband/ASHA pruning
- Parameter importance analysis
- Parallel trial execution
- Study persistence and resumption

**Used By Processes**:
- Model Training Pipeline with Experiment Tracking
- AutoML Pipeline Orchestration
- Experiment Planning and Hypothesis Testing

**Tools/Libraries**: Optuna, Optuna Dashboard

---

#### 9. ray-distributed-trainer
**Description**: Distributed computing skill using Ray for parallel training, hyperparameter search, and resource management.

**Capabilities**:
- Ray Train for distributed training
- Ray Tune for hyperparameter search
- Cluster resource management
- Fault tolerance and checkpointing
- Actor-based parallelism

**Used By Processes**:
- Distributed Training Orchestration
- AutoML Pipeline Orchestration
- Model Training Pipeline

**Tools/Libraries**: Ray, Ray Train, Ray Tune

---

### MLOps Skills

#### 10. mlflow-experiment-tracker
**Description**: MLflow integration skill for experiment tracking, model registry, and artifact management.

**Capabilities**:
- Experiment and run management
- Parameter and metric logging
- Artifact storage and retrieval
- Model registry operations
- Model staging and production transitions
- Run comparison and visualization

**Used By Processes**:
- Model Training Pipeline with Experiment Tracking
- ML Model Retraining Pipeline
- AutoML Pipeline Orchestration
- Model Deployment Pipeline

**Tools/Libraries**: MLflow, MLflow Model Registry

---

#### 11. wandb-experiment-tracker
**Description**: Weights & Biases integration skill for experiment tracking, hyperparameter sweeps, and artifact versioning.

**Capabilities**:
- Experiment logging and visualization
- Hyperparameter sweep configuration
- Artifact versioning and lineage
- Table and media logging
- Team collaboration features
- Report generation

**Used By Processes**:
- Model Training Pipeline with Experiment Tracking
- Experiment Planning and Hypothesis Testing
- Model Evaluation and Validation Framework

**Tools/Libraries**: Weights & Biases

---

#### 12. kubeflow-pipeline-executor
**Description**: Kubeflow Pipelines skill for ML workflow orchestration, component management, and Kubernetes-native ML.

**Capabilities**:
- Pipeline definition and compilation
- Component creation and reuse
- Pipeline versioning
- Artifact tracking
- Kubernetes resource management
- Pipeline scheduling

**Used By Processes**:
- Model Training Pipeline
- Distributed Training Orchestration
- Model Deployment Pipeline
- ML Model Retraining Pipeline

**Tools/Libraries**: Kubeflow Pipelines, KFP SDK

---

#### 13. seldon-model-deployer
**Description**: Seldon Core deployment skill for model serving, A/B testing, and canary deployments on Kubernetes.

**Capabilities**:
- SeldonDeployment creation
- Multi-model serving
- Traffic splitting (canary/shadow)
- Model monitoring integration
- Custom inference graphs
- Explainer deployment

**Used By Processes**:
- Model Deployment Pipeline with Canary Release
- A/B Testing Framework for ML Models
- ML Model Retraining Pipeline

**Tools/Libraries**: Seldon Core, Seldon Deploy

---

#### 14. bentoml-model-packager
**Description**: BentoML skill for model packaging, serving, and containerization.

**Capabilities**:
- Bento creation and versioning
- Multi-framework model support
- API endpoint definition
- Docker containerization
- Kubernetes deployment YAML generation
- Adaptive batching configuration

**Used By Processes**:
- Model Deployment Pipeline with Canary Release
- Model Training Pipeline
- ML System Integration Testing

**Tools/Libraries**: BentoML

---

### Explainability Skills

#### 15. shap-explainer
**Description**: SHAP-based model explainability skill for feature attribution, summary plots, and interaction analysis.

**Capabilities**:
- TreeExplainer for tree-based models
- DeepExplainer for neural networks
- KernelExplainer for model-agnostic
- Summary, dependence, and force plots
- Interaction value computation
- Cohort-based analysis

**Used By Processes**:
- Model Interpretability and Explainability Analysis
- Model Evaluation and Validation Framework
- A/B Testing Framework for ML Models

**Tools/Libraries**: SHAP

---

#### 16. lime-explainer
**Description**: LIME-based local explanation skill for individual predictions across tabular, text, and image data.

**Capabilities**:
- Tabular data explanations
- Text classification explanations
- Image classification explanations
- Submodular pick for representative samples
- Custom distance metrics

**Used By Processes**:
- Model Interpretability and Explainability Analysis
- Model Evaluation and Validation Framework

**Tools/Libraries**: LIME

---

#### 17. alibi-explainer
**Description**: Alibi explainability skill for counterfactual explanations, anchors, and trust scores.

**Capabilities**:
- Counterfactual instance generation
- Anchor explanations
- Integrated gradients
- Kernel SHAP integration
- Contrastive explanation method (CEM)
- Trust scores

**Used By Processes**:
- Model Interpretability and Explainability Analysis
- Model Evaluation and Validation Framework

**Tools/Libraries**: Alibi, Alibi Detect

---

### Monitoring Skills

#### 18. evidently-drift-detector
**Description**: Evidently AI skill for data drift detection, model performance monitoring, and automated reporting.

**Capabilities**:
- Data drift reports
- Target drift detection
- Classification/regression performance reports
- Custom metric calculation
- Test suites and thresholds
- HTML and JSON report generation

**Used By Processes**:
- Model Performance Monitoring and Drift Detection
- ML System Observability and Incident Response
- ML Model Retraining Pipeline

**Tools/Libraries**: Evidently AI

---

#### 19. whylabs-monitor
**Description**: WhyLabs integration skill for ML observability, profile logging, and anomaly detection.

**Capabilities**:
- Data profile generation (whylogs)
- Profile upload to WhyLabs
- Anomaly detection alerts
- Segment analysis
- Performance monitoring
- Integration with ML pipelines

**Used By Processes**:
- Model Performance Monitoring and Drift Detection
- ML System Observability and Incident Response

**Tools/Libraries**: whylogs, WhyLabs

---

#### 20. arize-observability
**Description**: Arize AI skill for production ML monitoring, embedding drift, and performance analysis.

**Capabilities**:
- Production data logging
- Embedding drift detection
- Performance monitoring dashboards
- Root cause analysis
- Slice and dice analysis
- Bias monitoring

**Used By Processes**:
- Model Performance Monitoring and Drift Detection
- ML System Observability and Incident Response
- Model Evaluation and Validation Framework

**Tools/Libraries**: Arize AI SDK

---

### Validation Skills

#### 21. fairlearn-bias-detector
**Description**: Fairness assessment skill using Fairlearn for bias detection, mitigation, and compliance reporting.

**Capabilities**:
- Demographic parity assessment
- Equalized odds evaluation
- Disparity metrics calculation
- Bias mitigation algorithms
- Fairness constraint optimization
- Compliance documentation

**Used By Processes**:
- Model Evaluation and Validation Framework
- Model Interpretability and Explainability Analysis
- A/B Testing Framework for ML Models

**Tools/Libraries**: Fairlearn

---

#### 22. model-card-generator
**Description**: Model documentation skill for generating model cards following Google's model card framework.

**Capabilities**:
- Model details documentation
- Intended use specification
- Performance metrics documentation
- Ethical considerations
- Caveats and limitations
- Quantitative analysis sections

**Used By Processes**:
- Model Interpretability and Explainability Analysis
- Model Evaluation and Validation Framework
- ML Model Retraining Pipeline

**Tools/Libraries**: Model Card Toolkit, TensorFlow Model Analysis

---

#### 23. pytest-ml-tester
**Description**: ML-specific testing skill using pytest with fixtures for data, models, and predictions.

**Capabilities**:
- Data validation fixtures
- Model loading fixtures
- Prediction testing utilities
- Performance regression tests
- Integration test helpers
- Coverage reporting for ML code

**Used By Processes**:
- ML System Integration Testing
- Model Evaluation and Validation Framework
- Data Collection and Validation Pipeline

**Tools/Libraries**: pytest, pytest-ml, hypothesis

---

#### 24. jupyter-notebook-executor
**Description**: Jupyter notebook execution skill for running notebooks programmatically and extracting outputs.

**Capabilities**:
- Parameterized notebook execution
- Output extraction and validation
- Notebook conversion (to HTML/PDF)
- Cell execution control
- Error handling and reporting
- Environment management

**Used By Processes**:
- Exploratory Data Analysis (EDA) Pipeline
- Model Interpretability and Explainability Analysis
- Experiment Planning and Hypothesis Testing

**Tools/Libraries**: papermill, nbconvert, jupyter

---

## Agents

### Planning Agents

#### 1. ml-requirements-analyst
**Description**: Agent specialized in ML project scoping, requirements gathering, and feasibility assessment.

**Responsibilities**:
- Business objective translation to ML metrics
- Data availability assessment
- Technical feasibility analysis
- Resource estimation
- Risk identification
- Success criteria definition

**Used By Processes**:
- ML Project Scoping and Requirements Analysis
- ML Architecture Design and Model Selection

**Required Skills**: pandas-dataframe-analyzer, great-expectations-validator

---

#### 2. eda-analyst
**Description**: Agent specialized in exploratory data analysis, pattern discovery, and data quality assessment.

**Responsibilities**:
- Statistical analysis execution
- Distribution visualization
- Correlation discovery
- Outlier identification
- Feature quality assessment
- Data documentation

**Used By Processes**:
- Exploratory Data Analysis (EDA) Pipeline
- Feature Engineering Design and Implementation

**Required Skills**: pandas-dataframe-analyzer, jupyter-notebook-executor, great-expectations-validator

---

#### 3. feature-engineer
**Description**: Agent specialized in feature creation, transformation, and selection strategies.

**Responsibilities**:
- Feature derivation from raw data
- Encoding strategy selection
- Feature scaling decisions
- Feature selection methods
- Leakage prevention
- Training-serving skew avoidance

**Used By Processes**:
- Feature Engineering Design and Implementation
- Feature Store Implementation and Management

**Required Skills**: pandas-dataframe-analyzer, feast-feature-store, sklearn-model-trainer

---

#### 4. ml-architect
**Description**: Agent specialized in ML system architecture, model selection, and pipeline design.

**Responsibilities**:
- Architecture pattern selection
- Model family recommendation
- Pipeline design decisions
- Infrastructure requirements
- Scalability planning
- Technical debt assessment

**Used By Processes**:
- ML Architecture Design and Model Selection
- Distributed Training Orchestration

**Required Skills**: kubeflow-pipeline-executor, ray-distributed-trainer, mlflow-experiment-tracker

---

#### 5. experiment-designer
**Description**: Agent specialized in experiment design, hypothesis formulation, and statistical testing plans.

**Responsibilities**:
- Hypothesis formulation
- Sample size calculation
- Test design (A/B, multi-arm bandit)
- Statistical power analysis
- Metric selection
- Bias prevention

**Used By Processes**:
- Experiment Planning and Hypothesis Testing
- A/B Testing Framework for ML Models

**Required Skills**: wandb-experiment-tracker, mlflow-experiment-tracker

---

### Execution Agents

#### 6. data-engineer
**Description**: Agent specialized in data pipeline construction, validation, and versioning.

**Responsibilities**:
- Data ingestion pipeline creation
- Schema validation implementation
- Data quality checks
- Version control integration
- Pipeline monitoring setup
- Error handling strategies

**Used By Processes**:
- Data Collection and Validation Pipeline
- Feature Store Implementation and Management

**Required Skills**: great-expectations-validator, dvc-dataset-versioning, feast-feature-store

---

#### 7. model-trainer
**Description**: Agent specialized in model training, hyperparameter tuning, and experiment management.

**Responsibilities**:
- Training script development
- Hyperparameter search execution
- Cross-validation management
- Early stopping configuration
- Checkpoint management
- Training monitoring

**Used By Processes**:
- Model Training Pipeline with Experiment Tracking
- AutoML Pipeline Orchestration

**Required Skills**: sklearn-model-trainer, pytorch-trainer, tensorflow-trainer, optuna-hyperparameter-tuner, mlflow-experiment-tracker

---

#### 8. feature-store-engineer
**Description**: Agent specialized in feature store infrastructure, feature serving, and consistency validation.

**Responsibilities**:
- Feature store deployment
- Online/offline serving configuration
- Feature freshness management
- Consistency validation
- Performance optimization
- Schema evolution handling

**Used By Processes**:
- Feature Store Implementation and Management

**Required Skills**: feast-feature-store, great-expectations-validator

---

#### 9. deployment-engineer
**Description**: Agent specialized in model deployment, serving infrastructure, and traffic management.

**Responsibilities**:
- Model packaging and containerization
- Serving infrastructure setup
- Traffic routing configuration
- Health check implementation
- Rollback mechanism setup
- Performance optimization

**Used By Processes**:
- Model Deployment Pipeline with Canary Release
- ML Model Retraining Pipeline

**Required Skills**: seldon-model-deployer, bentoml-model-packager, kubeflow-pipeline-executor

---

#### 10. automl-orchestrator
**Description**: Agent specialized in AutoML workflow execution, model selection, and ensemble creation.

**Responsibilities**:
- AutoML framework configuration
- Search space definition
- Resource allocation
- Ensemble strategy selection
- Model comparison
- Human-in-loop validation

**Used By Processes**:
- AutoML Pipeline Orchestration

**Required Skills**: optuna-hyperparameter-tuner, sklearn-model-trainer, mlflow-experiment-tracker

---

#### 11. distributed-training-engineer
**Description**: Agent specialized in distributed training orchestration, resource management, and fault tolerance.

**Responsibilities**:
- Cluster configuration
- Data parallelism setup
- Model parallelism strategies
- Gradient synchronization
- Checkpointing strategies
- Failure recovery

**Used By Processes**:
- Distributed Training Orchestration

**Required Skills**: ray-distributed-trainer, pytorch-trainer, tensorflow-trainer, kubeflow-pipeline-executor

---

#### 12. retraining-orchestrator
**Description**: Agent specialized in model retraining triggers, pipeline execution, and deployment coordination.

**Responsibilities**:
- Staleness detection
- Retraining trigger management
- Data validation for retraining
- Model comparison with baseline
- Deployment decision making
- Rollback coordination

**Used By Processes**:
- ML Model Retraining Pipeline

**Required Skills**: mlflow-experiment-tracker, evidently-drift-detector, seldon-model-deployer

---

### Validation Agents

#### 13. model-evaluator
**Description**: Agent specialized in comprehensive model evaluation, metric calculation, and performance analysis.

**Responsibilities**:
- Multi-metric evaluation
- Error analysis
- Slice-based evaluation
- Business metric impact assessment
- Performance comparison
- Evaluation report generation

**Used By Processes**:
- Model Evaluation and Validation Framework
- A/B Testing Framework for ML Models

**Required Skills**: sklearn-model-trainer, fairlearn-bias-detector, model-card-generator

---

#### 14. ab-test-analyst
**Description**: Agent specialized in A/B test execution, statistical analysis, and decision making.

**Responsibilities**:
- Traffic splitting management
- Statistical significance calculation
- Metric comparison
- Guardrail monitoring
- Decision recommendation
- Test documentation

**Used By Processes**:
- A/B Testing Framework for ML Models

**Required Skills**: wandb-experiment-tracker, evidently-drift-detector

---

#### 15. explainability-analyst
**Description**: Agent specialized in model interpretability, explanation generation, and compliance validation.

**Responsibilities**:
- Feature importance analysis
- Local explanation generation
- Global model behavior analysis
- Counterfactual generation
- Bias detection
- Compliance documentation

**Used By Processes**:
- Model Interpretability and Explainability Analysis

**Required Skills**: shap-explainer, lime-explainer, alibi-explainer, fairlearn-bias-detector, model-card-generator

---

#### 16. integration-tester
**Description**: Agent specialized in ML system integration testing, E2E validation, and performance testing.

**Responsibilities**:
- Test scenario design
- E2E test execution
- Performance benchmark running
- Resilience testing
- Coverage analysis
- Test reporting

**Used By Processes**:
- ML System Integration Testing

**Required Skills**: pytest-ml-tester, great-expectations-validator

---

### Monitoring Agents

#### 17. drift-detective
**Description**: Agent specialized in drift detection, root cause analysis, and alerting configuration.

**Responsibilities**:
- Data drift monitoring
- Concept drift detection
- Performance degradation analysis
- Root cause investigation
- Alert threshold configuration
- Retraining recommendation

**Used By Processes**:
- Model Performance Monitoring and Drift Detection

**Required Skills**: evidently-drift-detector, whylabs-monitor, arize-observability

---

#### 18. incident-responder
**Description**: Agent specialized in ML incident response, diagnosis, and remediation coordination.

**Responsibilities**:
- Incident triage
- Severity assessment
- Root cause analysis
- Remediation planning
- Rollback coordination
- Post-incident documentation

**Used By Processes**:
- ML System Observability and Incident Response

**Required Skills**: evidently-drift-detector, arize-observability, seldon-model-deployer

---

## Process to Skills/Agents Mapping

| Process | Recommended Skills | Recommended Agents |
|---------|-------------------|-------------------|
| ML Project Scoping and Requirements Analysis | pandas-dataframe-analyzer, great-expectations-validator | ml-requirements-analyst |
| Exploratory Data Analysis (EDA) Pipeline | pandas-dataframe-analyzer, jupyter-notebook-executor, great-expectations-validator | eda-analyst |
| Feature Engineering Design and Implementation | pandas-dataframe-analyzer, feast-feature-store, sklearn-model-trainer | feature-engineer |
| ML Architecture Design and Model Selection | kubeflow-pipeline-executor, ray-distributed-trainer, mlflow-experiment-tracker | ml-architect |
| Experiment Planning and Hypothesis Testing | wandb-experiment-tracker, mlflow-experiment-tracker | experiment-designer |
| Data Collection and Validation Pipeline | great-expectations-validator, dvc-dataset-versioning, feast-feature-store | data-engineer |
| Model Training Pipeline with Experiment Tracking | sklearn-model-trainer, pytorch-trainer, tensorflow-trainer, optuna-hyperparameter-tuner, mlflow-experiment-tracker | model-trainer |
| Feature Store Implementation and Management | feast-feature-store, great-expectations-validator | feature-store-engineer |
| Model Deployment Pipeline with Canary Release | seldon-model-deployer, bentoml-model-packager, kubeflow-pipeline-executor | deployment-engineer |
| AutoML Pipeline Orchestration | optuna-hyperparameter-tuner, sklearn-model-trainer, mlflow-experiment-tracker | automl-orchestrator |
| Distributed Training Orchestration | ray-distributed-trainer, pytorch-trainer, tensorflow-trainer, kubeflow-pipeline-executor | distributed-training-engineer |
| ML Model Retraining Pipeline | mlflow-experiment-tracker, evidently-drift-detector, seldon-model-deployer | retraining-orchestrator |
| Model Evaluation and Validation Framework | sklearn-model-trainer, fairlearn-bias-detector, model-card-generator | model-evaluator |
| A/B Testing Framework for ML Models | wandb-experiment-tracker, evidently-drift-detector | ab-test-analyst |
| Model Interpretability and Explainability Analysis | shap-explainer, lime-explainer, alibi-explainer, fairlearn-bias-detector, model-card-generator | explainability-analyst |
| ML System Integration Testing | pytest-ml-tester, great-expectations-validator | integration-tester |
| Model Performance Monitoring and Drift Detection | evidently-drift-detector, whylabs-monitor, arize-observability | drift-detective |
| ML System Observability and Incident Response | evidently-drift-detector, arize-observability, seldon-model-deployer | incident-responder |

---

## Shared Candidates (Cross-Specialization)

These skills and agents could be shared with other specializations:

### Shared Skills

1. **great-expectations-validator** - Useful for any data-intensive workflows (Data Engineering, Analytics)
2. **pytest-ml-tester** - Can be adapted for general testing (QA, DevOps)
3. **jupyter-notebook-executor** - Useful for any notebook-based workflows (Data Analysis, Research)
4. **model-card-generator** - Adaptable for general documentation (Technical Documentation)

### Shared Agents

1. **data-engineer** - Core data pipeline expertise applicable to Data Engineering specialization
2. **integration-tester** - Testing expertise applicable to QA Testing Automation specialization
3. **deployment-engineer** - Deployment expertise applicable to DevOps/SRE specialization
4. **incident-responder** - Incident management applicable to DevOps/SRE specialization

---

## Implementation Priority

### High Priority (Core ML Workflows)
1. mlflow-experiment-tracker
2. sklearn-model-trainer
3. great-expectations-validator
4. evidently-drift-detector
5. model-trainer (agent)
6. model-evaluator (agent)

### Medium Priority (Advanced Capabilities)
1. pytorch-trainer
2. tensorflow-trainer
3. optuna-hyperparameter-tuner
4. shap-explainer
5. feast-feature-store
6. explainability-analyst (agent)
7. drift-detective (agent)

### Lower Priority (Specialized Use Cases)
1. ray-distributed-trainer
2. kubeflow-pipeline-executor
3. alibi-explainer
4. whylabs-monitor
5. arize-observability
6. distributed-training-engineer (agent)
7. automl-orchestrator (agent)

---

## Notes

- All skills should implement standardized input/output schemas for composability
- Skills should support both synchronous and asynchronous execution modes
- Agents should be able to use multiple skills in sequence or parallel
- Error handling and retry mechanisms should be built into each skill
- Monitoring and logging should be consistent across all components
- Skills should support both local and cloud-based execution where applicable
- Agents should provide detailed reasoning for their decisions for auditability
