doanchienthangdev/omgkit

design-system-context

Automatic design system context injection for UI consistency

Idioma del texto original: inglés

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.

Idioma del texto original: inglés

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.

Idioma del texto original: inglés

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.

Idioma del texto original: inglés

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.

Idioma del texto original: inglés

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.

Idioma del texto original: inglés

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.

Idioma del texto original: inglés

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.

Idioma del texto original: inglés

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.

Idioma del texto original: inglés

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.

Idioma del texto original: inglés

testingchaos-testing

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

Idioma del texto original: inglés

testingcomprehensive-testing

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

Idioma del texto original: inglés

testingmutation-testing

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

Idioma del texto original: inglés

testingperformance-testing

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

Idioma del texto original: inglés

testingproperty-testing

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

Idioma del texto original: inglés

testingsecurity-testing

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

Idioma del texto original: inglés

ai-accelerators

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

Idioma del texto original: inglés

data-engineering

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

Idioma del texto original: inglés

deep-learning-primer

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

Idioma del texto original: inglés

deployment-paradigms

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

Idioma del texto original: inglés

dnn-architectures

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

Idioma del texto original: inglés

edge-deployment

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

Idioma del texto original: inglés

efficient-ai

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

Idioma del texto original: inglés

feature-engineering

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

Idioma del texto original: inglés

ml-frameworks

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

Idioma del texto original: inglés

ml-serving-optimization

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

Idioma del texto original: inglés

ml-systems-fundamentals

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

Idioma del texto original: inglés

ml-workflow

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

Idioma del texto original: inglés

mlops

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

Idioma del texto original: inglés

model-deployment

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

Idioma del texto original: inglés

model-development

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

Idioma del texto original: inglés

model-optimization

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

Idioma del texto original: inglés

robust-ai

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

Idioma del texto original: inglés

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.

Idioma del texto original: inglés

training-data

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

Idioma del texto original: inglés

contract-testing

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

Idioma del texto original: inglés

distributed-tracing

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

Idioma del texto original: inglés

service-discovery

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

Idioma del texto original: inglés

service-mesh

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

Idioma del texto original: inglés

project-orchestration

Orchestrate autonomous project development through state-driven execution

Idioma del texto original: inglés