doanchienthangdev/omgkit

design-system-context

Automatic design system context injection for UI consistency

원문 언어: 영어

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.

원문 언어: 영어

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.

원문 언어: 영어

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.

원문 언어: 영어

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.

원문 언어: 영어

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.

원문 언어: 영어

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.

원문 언어: 영어

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.

원문 언어: 영어

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.

원문 언어: 영어

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.

원문 언어: 영어

testingchaos-testing

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

원문 언어: 영어

testingcomprehensive-testing

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

원문 언어: 영어

testingmutation-testing

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

원문 언어: 영어

testingperformance-testing

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

원문 언어: 영어

testingproperty-testing

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

원문 언어: 영어

testingsecurity-testing

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

원문 언어: 영어

ai-accelerators

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

원문 언어: 영어

data-engineering

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

원문 언어: 영어

deep-learning-primer

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

원문 언어: 영어

deployment-paradigms

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

원문 언어: 영어

dnn-architectures

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

원문 언어: 영어

edge-deployment

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

원문 언어: 영어

efficient-ai

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

원문 언어: 영어

feature-engineering

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

원문 언어: 영어

ml-frameworks

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

원문 언어: 영어

ml-serving-optimization

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

원문 언어: 영어

ml-systems-fundamentals

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

원문 언어: 영어

ml-workflow

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

원문 언어: 영어

mlops

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

원문 언어: 영어

model-deployment

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

원문 언어: 영어

model-development

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

원문 언어: 영어

model-optimization

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

원문 언어: 영어

robust-ai

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

원문 언어: 영어

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.

원문 언어: 영어

training-data

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

원문 언어: 영어

contract-testing

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

원문 언어: 영어

distributed-tracing

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

원문 언어: 영어

service-discovery

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

원문 언어: 영어

service-mesh

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

원문 언어: 영어

project-orchestration

Orchestrate autonomous project development through state-driven execution

원문 언어: 영어