doanchienthangdev/omgkit

design-system-context

Automatic design system context injection for UI consistency

لغة النص الأصلي: الإنجليزية

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.

لغة النص الأصلي: الإنجليزية

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.

لغة النص الأصلي: الإنجليزية

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.

لغة النص الأصلي: الإنجليزية

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.

لغة النص الأصلي: الإنجليزية

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.

لغة النص الأصلي: الإنجليزية

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.

لغة النص الأصلي: الإنجليزية

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.

لغة النص الأصلي: الإنجليزية

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.

لغة النص الأصلي: الإنجليزية

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.

لغة النص الأصلي: الإنجليزية

testingchaos-testing

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

لغة النص الأصلي: الإنجليزية

testingcomprehensive-testing

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

لغة النص الأصلي: الإنجليزية

testingmutation-testing

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

لغة النص الأصلي: الإنجليزية

testingperformance-testing

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

لغة النص الأصلي: الإنجليزية

testingproperty-testing

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

لغة النص الأصلي: الإنجليزية

testingsecurity-testing

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

لغة النص الأصلي: الإنجليزية

ai-accelerators

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

لغة النص الأصلي: الإنجليزية

data-engineering

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

لغة النص الأصلي: الإنجليزية

deep-learning-primer

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

لغة النص الأصلي: الإنجليزية

deployment-paradigms

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

لغة النص الأصلي: الإنجليزية

dnn-architectures

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

لغة النص الأصلي: الإنجليزية

edge-deployment

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

لغة النص الأصلي: الإنجليزية

efficient-ai

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

لغة النص الأصلي: الإنجليزية

feature-engineering

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

لغة النص الأصلي: الإنجليزية

ml-frameworks

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

لغة النص الأصلي: الإنجليزية

ml-serving-optimization

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

لغة النص الأصلي: الإنجليزية

ml-systems-fundamentals

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

لغة النص الأصلي: الإنجليزية

ml-workflow

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

لغة النص الأصلي: الإنجليزية

mlops

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

لغة النص الأصلي: الإنجليزية

model-deployment

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

لغة النص الأصلي: الإنجليزية

model-development

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

لغة النص الأصلي: الإنجليزية

model-optimization

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

لغة النص الأصلي: الإنجليزية

robust-ai

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

لغة النص الأصلي: الإنجليزية

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.

لغة النص الأصلي: الإنجليزية

training-data

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

لغة النص الأصلي: الإنجليزية

contract-testing

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

لغة النص الأصلي: الإنجليزية

distributed-tracing

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

لغة النص الأصلي: الإنجليزية

service-discovery

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

لغة النص الأصلي: الإنجليزية

service-mesh

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

لغة النص الأصلي: الإنجليزية

project-orchestration

Orchestrate autonomous project development through state-driven execution

لغة النص الأصلي: الإنجليزية