Topic: anthropic
9,221 skills in this topic.
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golang-patterns
构建健壮、高效且可维护 Go 应用程序的惯用法（Idiomatic Go）、最佳实践与规范。
xu-xiang/everything-claude-code-zh 383
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backend-patterns
后端架构模式、API 设计、数据库优化以及适用于 Node.js、Express 和 Next.js API 路由的服务端最佳实践。
xu-xiang/everything-claude-code-zh 383
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eval-harness
Claude Code 会话的正式评测框架，实现了评测驱动开发（EDD）原则
xu-xiang/everything-claude-code-zh 383
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article-writing
编写文章、指南、博客、教程、时事通讯（Newsletter）等长内容，支持从示例或品牌指南中提取独特的语感语调。适用于需要撰写超过一个段落的精炼文本，尤其是对语气一致性、结构和可信度有较高要求时。
xu-xiang/everything-claude-code-zh 383
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skill-stocktake
用于审计Claude技能和命令的质量。支持快速扫描（仅变更技能）和全面盘点模式，采用顺序子代理批量评估。
xu-xiang/everything-claude-code-zh 383
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python-testing
使用 pytest、TDD 方法论、固件（Fixtures）、模拟（Mocking）、参数化及覆盖率要求的 Python 测试策略。
xu-xiang/everything-claude-code-zh 383
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golang-testing
Go测试模式包括表格驱动测试、子测试、基准测试、模糊测试和测试覆盖率。遵循TDD方法论，采用地道的Go实践。
xu-xiang/everything-claude-code-zh 383
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article-writing
根据提供的示例或品牌指导，以独特的语气撰写文章、指南、博客帖子、教程、新闻简报等长篇内容。当用户需要超过一段的精致书面内容时使用，尤其是当语气一致性、结构和可信度至关重要时。
xu-xiang/everything-claude-code-zh 383
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session-memory
Maintains awareness across sessions. Spawns observer agent on start, loads context, notifies of evolution opportunities.
humanplane/homunculus 357
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instinct-apply
Surfaces relevant instincts during work. Use when starting a task to check if any learned behaviors apply.
humanplane/homunculus 357
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memory
Claude Mind - Search and manage Claude's persistent memory stored in a single portable .mv2 file
memvid/claude-brain 355
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mind
Claude Mind - Search and manage Claude's persistent memory stored in a single portable .mv2 file
memvid/claude-brain 355
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building-forms
Builds form components and data collection interfaces including contact forms, registration flows, checkout processes, surveys, and settings pages. Includes 50+ input types, validation strategies, accessibility patterns (WCAG 2.1), multi-step wizards, and UX best practices. Provides decision trees from data type to component selection, validation timing guidance, and error handling patterns. Use when creating forms, collecting user input, building surveys, implementing validation, designing multi-step workflows, or ensuring form accessibility.
ancoleman/ai-design-components 333
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implementing-observability
Monitoring, logging, and tracing implementation using OpenTelemetry as the unified standard. Use when building production systems requiring visibility into performance, errors, and behavior. Covers OpenTelemetry (metrics, logs, traces), Prometheus, Grafana, Loki, Jaeger, Tempo, structured logging (structlog, tracing, slog, pino), and alerting.
ancoleman/ai-design-components 333
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managing-configuration
Guide users through creating, managing, and testing server configuration automation using Ansible. When automating server configurations, deploying applications with Ansible playbooks, managing dynamic inventories for cloud environments, or testing roles with Molecule, this skill provides idempotency patterns, secrets management with ansible-vault and HashiCorp Vault, and GitOps workflows for configuration as code.
ancoleman/ai-design-components 333
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visualizing-data
Builds dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.
ancoleman/ai-design-components 333
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deploying-on-aws
Selecting and implementing AWS services and architectural patterns. Use when designing AWS cloud architectures, choosing compute/storage/database services, implementing serverless or container patterns, or applying AWS Well-Architected Framework principles.
ancoleman/ai-design-components 333
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managing-dns
Manage DNS records, TTL strategies, and DNS-as-code automation for infrastructure. Use when configuring domain resolution, automating DNS from Kubernetes with external-dns, setting up DNS-based load balancing, or troubleshooting propagation issues across cloud providers (Route53, Cloud DNS, Azure DNS, Cloudflare).
ancoleman/ai-design-components 333
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writing-dockerfiles
Writing optimized, secure, multi-stage Dockerfiles with language-specific patterns (Python, Node.js, Go, Rust), BuildKit features, and distroless images. Use when containerizing applications, optimizing existing Dockerfiles, or reducing image sizes.
ancoleman/ai-design-components 333
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writing-github-actions
Write GitHub Actions workflows with proper syntax, reusable workflows, composite actions, matrix builds, caching, and security best practices. Use when creating CI/CD workflows for GitHub-hosted projects or automating GitHub repository tasks.
ancoleman/ai-design-components 333
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implementing-realtime-sync
Real-time communication patterns for live updates, collaboration, and presence. Use when building chat applications, collaborative tools, live dashboards, or streaming interfaces (LLM responses, metrics). Covers SSE (server-sent events for one-way streams), WebSocket (bidirectional communication), WebRTC (peer-to-peer video/audio), CRDTs (Yjs, Automerge for conflict-free collaboration), presence patterns, offline sync, and scaling strategies. Supports Python, Rust, Go, and TypeScript.
ancoleman/ai-design-components 333
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using-vector-databases
Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search patterns.
ancoleman/ai-design-components 333
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resource-tagging
Apply and enforce cloud resource tagging strategies across AWS, Azure, GCP, and Kubernetes for cost allocation, ownership tracking, compliance, and automation. Use when implementing cloud governance, optimizing costs, or automating infrastructure management.
ancoleman/ai-design-components 333
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building-clis
Build professional command-line interfaces in Python, Go, and Rust using modern frameworks like Typer, Cobra, and clap. Use when creating developer tools, automation scripts, or infrastructure management CLIs with robust argument parsing, interactive features, and multi-platform distribution.
ancoleman/ai-design-components 333