llm-and-ai-cost-optimization
Strategies for managing and reducing costs in LLM-powered applications, from token economics to RAG architectures.
发现 测试与安全 相关 Skills,比较来源仓库和用途,查看适合你工作流的技能说明。
Strategies for managing and reducing costs in LLM-powered applications, from token economics to RAG architectures.
Practical deployment strategies (rolling, blue-green, canary) for safe releases with rollback, health checks, and database compatibility guidance—without requiring full Kubernetes complexity
Comprehensive guide to backward compatibility rules for APIs, databases, and data contracts with migration strategies and testing approaches
See the main On-Call Playbooks skill for comprehensive runbook templates and examples.
Comprehensive guide to implementing audit trails and logging for AI agents including tracing, observability, compliance, and debugging
Systematic evaluation of architectural trade-offs to make informed decisions balancing competing concerns.
See the main Model Explainability skill for comprehensive XAI coverage.
A practical framework to map change dependencies and blast radius, including who/what is affected, what to test, rollout/rollback strategies, and stakeholder ownership
Systematic approach for breaking down complex, high-level requests into atomic, executable steps to ensure precision and prevent overwhelm.
Best practices for conducting effective code reviews that improve code quality, share knowledge, and maintain team standards.
Structured approach to deprecating features, APIs, and systems with clear timelines and migration paths.
Comprehensive guide to OpenAPI governance, API design standards, automated linting, breaking change detection, and API lifecycle management
Comprehensive guide to choosing and tracking validation metrics that prove product-market fit and drive actionable insights
Standardized commit message formats and conventions for clear, searchable, and automated-friendly version control history.
Patterns and strategies for cache invalidation - one of the two hardest problems in computer science.
Understanding and implementing database locking mechanisms for concurrency control.
Comprehensive guide to writing testable product hypotheses that drive learning and prevent building the wrong things
Comprehensive guide to regression benchmarks for AI systems including test suite creation, CI/CD integration, and continuous benchmarking strategies
Generator สำหรับสร้าง CI/CD pipeline configs (GitHub Actions, GitLab CI) พร้อม testing, linting, building และ deployment
Continuous monitoring and diagnostics for ensuring high availability, performance, and stability of database systems.
Automated code quality enforcement through linting, formatting, and type checking to maintain consistent, error-free codebases.
See the main Schema Drift Detection skill for comprehensive coverage of schema drift detection and management.
Implementing human override capabilities for AI decisions with proper tracking, auditing, and feedback loops.
Policy สำหรับ enforce code quality ผ่าน linting, testing, และ type checking ใน CI pipeline
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库,同一仓库的多个技能可能共享相同数值;它反映仓库关注度,不代表单个技能的安装量或质量评分。