aws-skills
The aws-skills collection provides Claude Code with three specialized AWS development capabilities: CDK infrastructure as code, cost optimization and monitoring, and serverless event-driven architecture patterns. Use this skill set when designing AWS applications, estimating service costs, building infrastructure stacks, or implementing distributed serverless systems with integrated access to current AWS documentation and pricing tools.
git clone --depth 1 https://github.com/Microck/ordinary-claude-skills /tmp/aws-skills && cp -r /tmp/aws-skills/skills_all/aws-skills ~/.claude/skills/aws-skillsSKILL.md
# AWS Skills Collection Comprehensive AWS development capabilities for Claude Code, including CDK infrastructure as code, cost optimization, and serverless architecture patterns. ## Included Skills This collection contains specialized skills for different AWS domains: - **aws-cdk-development** - Infrastructure as code with AWS CDK - **aws-cost-operations** - Cost optimization and monitoring - **aws-serverless-eda** - Serverless and event-driven architecture patterns Each skill includes integrated MCP servers for accessing the latest AWS documentation and tools. ## Use Cases ### Infrastructure Development - Designing and implementing CDK stacks - Following AWS best practices patterns - Infrastructure validation and deployment ### Cost Management - Estimating AWS service costs - Analyzing spending patterns - Optimizing resource allocation ### Serverless Architecture - Building event-driven applications - Designing well-architected serverless systems - Implementing distributed patterns (saga, event sourcing) ## Key Features - **CDK Best Practices**: Proper construct patterns and resource naming - **MCP Integration**: Live AWS documentation and pricing lookups - **Well-Architected**: Follows AWS Well-Architected Framework - **Comprehensive**: Covers infrastructure, costs, and operations ## Getting Started Each skill within this collection is self-contained and can be used independently. Refer to the specific SKILL.md files in each subdirectory for detailed documentation.
Testing patterns for PHPUnit and Playwright E2E tests. Use when writing tests, debugging test failures, setting up test coverage, or implementing test patterns for ActivityPub features.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling to millions of vectors.