doanchienthangdev/omgkit

design-system-context

Automatic design system context injection for UI consistency

developing-test-driven

AI agent practices test-first development with the Red-Green-Refactor cycle for confident, well-designed code. Use when implementing features, fixing bugs, or establishing testing practices.

test-enforcement

The agent enforces mandatory test completion before any task or feature can be marked as done, ensuring code quality through strict validation gates and evidence-based completion criteria.

test-task-generation

The agent automatically generates comprehensive test tasks from feature requirements, ensuring every implementation task has corresponding test coverage with proper acceptance criteria.

workflow-config-system

The agent implements a centralized workflow configuration system for Git workflows, enabling set-once-use-everywhere automation for trunk-based development, gitflow, and github-flow patterns with integrated testing automation.

git-hooks-integration

The agent implements Git hooks that integrate with the workflow config system, automating pre-commit checks, commit message validation, pre-push tests, and post-merge actions.

dora-metrics-and-devops-performance

The agent implements DORA metrics tracking for measuring and improving software delivery performance. Use when establishing engineering metrics, benchmarking teams, or driving DevOps transformation.

feature-flags-and-progressive-delivery

The agent implements feature flag systems for trunk-based development, canary releases, and A/B testing. Use when implementing gradual rollouts, kill switches, or experiment-driven development.

stacked-diffs-workflow

The agent implements stacked diffs (stacked PRs) for breaking large changes into reviewable chunks. Use when working on complex features, managing dependent changes, or optimizing code review flow.

chaos-engineering-and-resilience-testing

The agent implements chaos engineering practices for building resilient systems. Use when testing fault tolerance, designing game days, or validating system recovery.

testingchaos-testing

Chaos engineering and fault injection patterns for testing system resilience, failure recovery, and graceful degradation

testingcomprehensive-testing

Comprehensive 4D testing methodology covering Accuracy, Performance, Security, and Accessibility for production-ready quality assurance

testingmutation-testing

Mutation testing with Stryker to verify test quality by introducing code mutations and measuring detection rates

testingperformance-testing

Performance testing patterns including load testing, stress testing, benchmarking, and profiling for optimal application performance

testingproperty-testing

Property-based testing with Fast-Check for finding edge cases through automated input generation and invariant verification

testingsecurity-testing

Security testing patterns covering OWASP Top 10, injection prevention, authentication, and vulnerability scanning

ai-accelerators

AI hardware accelerators including GPUs, TPUs, custom silicon, and hardware-aware optimization strategies for ML workloads.

data-engineering

ML data engineering covering data pipelines, data quality, collection strategies, storage, and versioning for machine learning systems.

deep-learning-primer

Deep learning foundations including neural network basics, backpropagation, optimization, regularization, and training best practices.

deployment-paradigms

ML deployment paradigms including batch vs real-time inference, online vs offline serving, edge deployment, and serverless ML.

dnn-architectures

Deep neural network architectures including CNNs, RNNs, Transformers, and modern architectures for vision, NLP, and multimodal tasks.

edge-deployment

Edge deployment strategies including mobile optimization, embedded systems, TFLite, Core ML, and resource-constrained inference.

efficient-ai

Efficient AI techniques including model compression, quantization, pruning, knowledge distillation, and hardware-aware optimization for production systems.

feature-engineering

Feature engineering techniques including feature extraction, transformation, selection, and feature store management for ML systems.

ml-frameworks

ML framework best practices for PyTorch, TensorFlow, scikit-learn, and modern ML libraries including training patterns and optimization.

ml-serving-optimization

ML serving optimization techniques including batching, caching, model compilation, and latency reduction for production ML systems.

ml-systems-fundamentals

Core ML systems concepts including ML lifecycle, system architecture, requirements, and design principles for production ML.

ml-workflow

ML development workflow covering experiment design, baseline establishment, iterative improvement, and experiment tracking best practices.

mlops

MLOps practices including CI/CD for ML, experiment tracking, model monitoring, pipeline orchestration, and production ML operations.

model-deployment

Model deployment strategies including serving infrastructure, containerization, model packaging, versioning, and production deployment patterns.

model-development

Model development practices including model selection, training pipelines, hyperparameter tuning, evaluation, and model selection strategies.

model-optimization

Model optimization techniques including hyperparameter tuning, architecture search, training optimization, and performance profiling for ML systems.

robust-ai

Building robust AI systems including model monitoring, drift detection, reliability engineering, and failure handling for production ML.

ml-systems

Machine Learning Systems - comprehensive knowledge for building production ML systems from data engineering through deployment and operations. Based on Harvard ML Systems course and Designing ML Systems by Chip Huyen.

training-data

Training data management including labeling strategies, data augmentation, handling imbalanced data, and data splitting best practices.

contract-testing

Consumer-driven contract testing with Pact, schema validation, provider verification, and CI/CD integration.

distributed-tracing

Comprehensive distributed tracing with Jaeger, Zipkin, OpenTelemetry, correlation IDs, and span design.

service-discovery

Service discovery patterns with Consul, Kubernetes DNS, Eureka, health checks, and client-side load balancing.

service-mesh

Advanced service mesh implementation with Istio, Linkerd, traffic management, mTLS, and observability.

project-orchestration

Orchestrate autonomous project development through state-driven execution