Topic: anthropic
9,221 skills in this topic.
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investor-materials
创建并更新路演 PPT (Pitch Deck)、单页简介 (One-Pager)、投资者备忘录 (Investor Memo)、加速器申请、财务模型以及融资材料。当用户需要面向投资者的文档、预测、资金用途表、里程碑计划或需要跨多个融资资产保持内部一致性的材料时，请使用此技能。
xu-xiang/everything-claude-code-zh 383
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frontend-slides
从零开始或通过转换 PowerPoint 文件创建令人惊叹、动画丰富的 HTML 演示文稿（Presentations）。适用于用户想要构建演示文稿、将 PPT/PPTX 转换为 Web 页面或为演讲/路演创建幻灯片（Slides）的场景。通过视觉探索而非抽象选择，帮助非设计师发掘其审美偏好。
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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django-tdd
Django testing strategies with pytest-django, TDD methodology, factory_boy, mocking, coverage, and testing Django REST Framework APIs.
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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tdd-workflow
在编写新功能、修复错误或重构代码时使用此技能。强制执行测试驱动开发，确保单元测试、集成测试和端到端测试的覆盖率超过80%。
xu-xiang/everything-claude-code-zh 383
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golang-patterns
用于构建健壮、高效且可维护的Go应用程序的惯用Go模式、最佳实践和约定。
xu-xiang/everything-claude-code-zh 383
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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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session-memory
Maintains awareness across sessions. Spawns observer agent on start, loads context, notifies of evolution opportunities.
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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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
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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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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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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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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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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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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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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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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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deploying-on-azure
Design and implement Azure cloud architectures using best practices for compute, storage, databases, AI services, networking, and governance. Use when building applications on Microsoft Azure or migrating workloads to Azure cloud platform.
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