ML Pipeline Workflow

Overview

This public intake copy packages

plugins/antigravity-awesome-skills-claude/skills/ml-pipeline-workflow

https://github.com/sickn33/antigravity-awesome-skills

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses

metadata.json

ORIGIN.md

ML Pipeline Workflow Complete end-to-end MLOps pipeline orchestration from data preparation through model deployment.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: What This Skill Provides, Integration Points, Progressive Disclosure, Common Patterns, Limitations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• The task is unrelated to ml pipeline workflow
• You need a different domain or tool outside this scope
• Building new ML pipelines from scratch
• Designing workflow orchestration for ML systems
• Implementing data → model → deployment automation
• Setting up reproducible training workflows

Operating Table

metadata.json

ORIGIN.md

SKILL.md

SKILL.md

## Related Skills

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Clarify goals, constraints, and required inputs.
2. Apply relevant best practices and validate outcomes.
3. Provide actionable steps and verification.
4. If detailed examples are required, open resources/implementation-playbook.md.
5. Explore hyperparameter-tuning skill for optimization
6. Learn experiment-tracking-setup for MLflow/W&B
7. Review model-deployment-patterns for serving strategies

Imported Workflow Notes

Imported: Instructions

• Clarify goals, constraints, and required inputs.
• Apply relevant best practices and validate outcomes.
• Provide actionable steps and verification.
• If detailed examples are required, open

  resources/implementation-playbook.md

  .

Imported: Next Steps

After setting up your pipeline:

1. Explore hyperparameter-tuning skill for optimization
2. Learn experiment-tracking-setup for MLflow/W&B
3. Review model-deployment-patterns for serving strategies
4. Implement monitoring with observability tools

Imported: Overview

This skill provides comprehensive guidance for building production ML pipelines that handle the full lifecycle: data ingestion → preparation → training → validation → deployment → monitoring.

Imported: What This Skill Provides

Core Capabilities

1. Pipeline Architecture

   • End-to-end workflow design
   • DAG orchestration patterns (Airflow, Dagster, Kubeflow)
   • Component dependencies and data flow
   • Error handling and retry strategies

2. Data Preparation

   • Data validation and quality checks
   • Feature engineering pipelines
   • Data versioning and lineage
   • Train/validation/test splitting strategies

3. Model Training

   • Training job orchestration
   • Hyperparameter management
   • Experiment tracking integration
   • Distributed training patterns

4. Model Validation

   • Validation frameworks and metrics
   • A/B testing infrastructure
   • Performance regression detection
   • Model comparison workflows

5. Deployment Automation

   • Model serving patterns
   • Canary deployments
   • Blue-green deployment strategies
   • Rollback mechanisms

Reference Documentation

See the

references/

• data-preparation.md - Data cleaning, validation, and feature engineering
• model-training.md - Training workflows and best practices
• model-validation.md - Validation strategies and metrics
• model-deployment.md - Deployment patterns and serving architectures

Assets and Templates

The

assets/

• pipeline-dag.yaml.template - DAG template for workflow orchestration
• training-config.yaml - Training configuration template
• validation-checklist.md - Pre-deployment validation checklist

Examples

Example 1: Ask for the upstream workflow directly

Use @ml-pipeline-workflow to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @ml-pipeline-workflow against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @ml-pipeline-workflow for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @ml-pipeline-workflow using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Usage Patterns

Basic Pipeline Setup

# 1. Define pipeline stages
stages = [
    "data_ingestion",
    "data_validation",
    "feature_engineering",
    "model_training",
    "model_validation",
    "model_deployment"
]

# 2. Configure dependencies
# See assets/pipeline-dag.yaml.template for full example

Production Workflow

1. Data Preparation Phase

   • Ingest raw data from sources
   • Run data quality checks
   • Apply feature transformations
   • Version processed datasets

2. Training Phase

   • Load versioned training data
   • Execute training jobs
   • Track experiments and metrics
   • Save trained models

3. Validation Phase

   • Run validation test suite
   • Compare against baseline
   • Generate performance reports
   • Approve for deployment

4. Deployment Phase

   • Package model artifacts
   • Deploy to serving infrastructure
   • Configure monitoring
   • Validate production traffic

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Modularity: Each stage should be independently testable
• Idempotency: Re-running stages should be safe
• Observability: Log metrics at every stage
• Versioning: Track data, code, and model versions
• Failure Handling: Implement retry logic and alerting
• Use data validation libraries (Great Expectations, TFX)
• Version datasets with DVC or similar tools

Imported Operating Notes

Imported: Best Practices

Pipeline Design

• Modularity: Each stage should be independently testable
• Idempotency: Re-running stages should be safe
• Observability: Log metrics at every stage
• Versioning: Track data, code, and model versions
• Failure Handling: Implement retry logic and alerting

Data Management

• Use data validation libraries (Great Expectations, TFX)
• Version datasets with DVC or similar tools
• Document feature engineering transformations
• Maintain data lineage tracking

Model Operations

• Separate training and serving infrastructure
• Use model registries (MLflow, Weights & Biases)
• Implement gradual rollouts for new models
• Monitor model performance drift
• Maintain rollback capabilities

Deployment Strategies

• Start with shadow deployments
• Use canary releases for validation
• Implement A/B testing infrastructure
• Set up automated rollback triggers
• Monitor latency and throughput

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in

plugins/antigravity-awesome-skills-claude/skills/ml-pipeline-workflow

metadata.json

ORIGIN.md

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated

SKILL.md

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

Common Issues

• Pipeline failures: Check dependencies and data availability
• Training instability: Review hyperparameters and data quality
• Deployment issues: Validate model artifacts and serving config
• Performance degradation: Monitor data drift and model metrics

Debugging Steps

1. Check pipeline logs for each stage
2. Validate input/output data at boundaries
3. Test components in isolation
4. Review experiment tracking metrics
5. Inspect model artifacts and metadata

Related Skills

• @linear-claude-skill

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @linkedin-automation

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @linkedin-cli

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

• @linkedin-profile-optimizer

  - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

references

references/n/a

examples

examples/n/a

scripts

scripts/n/a

agents

agents/n/a

assets

assets/n/a

Imported Reference Notes

Imported: Integration Points

Orchestration Tools

• Apache Airflow: DAG-based workflow orchestration
• Dagster: Asset-based pipeline orchestration
• Kubeflow Pipelines: Kubernetes-native ML workflows
• Prefect: Modern dataflow automation

Experiment Tracking

• MLflow for experiment tracking and model registry
• Weights & Biases for visualization and collaboration
• TensorBoard for training metrics

Deployment Platforms

• AWS SageMaker for managed ML infrastructure
• Google Vertex AI for GCP deployments
• Azure ML for Azure cloud
• Kubernetes + KServe for cloud-agnostic serving

Imported: Progressive Disclosure

Start with the basics and gradually add complexity:

1. Level 1: Simple linear pipeline (data → train → deploy)
2. Level 2: Add validation and monitoring stages
3. Level 3: Implement hyperparameter tuning
4. Level 4: Add A/B testing and gradual rollouts
5. Level 5: Multi-model pipelines with ensemble strategies

Imported: Common Patterns

Batch Training Pipeline

# See assets/pipeline-dag.yaml.template
stages:
  - name: data_preparation
    dependencies: []
  - name: model_training
    dependencies: [data_preparation]
  - name: model_evaluation
    dependencies: [model_training]
  - name: model_deployment
    dependencies: [model_evaluation]

Real-time Feature Pipeline

# Stream processing for real-time features
# Combined with batch training
# See references/data-preparation.md

Continuous Training

# Automated retraining on schedule
# Triggered by data drift detection
# See references/model-training.md

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.