gitlab-ci-patterns
Implement professional GitLab CI/CD pipelines with multi-stage workflows, caching strategies, and Kubernetes deployment patterns for scalable automation.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
482 skills found
Implement professional GitLab CI/CD pipelines with multi-stage workflows, caching strategies, and Kubernetes deployment patterns for scalable automation.
A framework for applying Test-Driven Development to process documentation, ensuring agent reliability by using pressure scenarios to identify and patch rationalization loopholes.
Architectural guidance and pattern implementation for Java Spring Boot backends, covering REST API design, JPA, caching, async processing, and logging.
Deployment skill for AWS Elastic Beanstalk Node.js apps, providing dependency strategies for monorepos, private packages, and environment configuration.
Monitor and manage margin-living strategy by tracking balances, interest costs, and coverage ratios. Provides automated scaling recommendations and safety alerts based on portfolio-to-margin thresholds.
Expert framework for designing agent-facing tools, optimizing tool descriptions, enforcing contract-based APIs, and implementing architectural reduction for reliable AI agent tool selection.
Robot perception system design, configuration, and optimization for cameras, LiDAR, and sensor fusion pipelines. Includes camera calibration, 3D reconstruction, and production deployment best practices.
Expert assistant for the DGame Unity framework, facilitating development, architecture, hotfix, and resource management within the TEngine-based ecosystem.
Implement professional TDD workflows with strict 80% coverage, automated testing strategies, and AAA pattern enforcement for robust, high-quality code.
A comprehensive configuration suite for Claude Code, featuring production-grade agents, skills, hooks, and automated workflows optimized for high-intensity development.
Operate the btca CLI for source-first code research. Manage git, local, and npm resources to ground AI answers in actual codebase context rather than outdated documentation.
Systematic technical conversation logging for developers and engineers. Captures decisions, implementation details, and session outcomes with factual precision.