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Agent skills
anthropic

Topic: anthropic

9,221 skills in this topic.

springboot-verification Spring Boot 项目的验证循环：构建、静态分析、带有覆盖率的测试、安全扫描以及发布或 PR 前的差异审查。
xu-xiang/everything-claude-code-zh 383
springboot-tdd 使用 JUnit 5, Mockito, MockMvc, Testcontainers 和 JaCoCo 进行 Spring Boot 的测试驱动开发（TDD）。适用于添加新功能、修复 bug 或重构场景。
xu-xiang/everything-claude-code-zh 383
springboot-security Spring Boot 服务的 Spring Security 身份验证/授权、验证、CSRF、密钥、响应头、速率限制和依赖项安全最佳实践。
xu-xiang/everything-claude-code-zh 383
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
postgres-patterns PostgreSQL 数据库模式，涵盖查询优化、架构设计、索引和安全。基于 Supabase 最佳实践。
xu-xiang/everything-claude-code-zh 383
nutrient-document-processing 使用 Nutrient DWS API 进行文档处理、转换、OCR、提取、脱敏、签名以及表单填充。支持 PDF、DOCX、XLSX、PPTX、HTML 和图像格式。
xu-xiang/everything-claude-code-zh 383
springboot-patterns Spring Boot 架构模式、REST API 设计、分层服务、数据访问、缓存、异步处理与日志。适用于 Java Spring Boot 后端开发。
xu-xiang/everything-claude-code-zh 383
security-scan 使用 AgentShield 扫描 Claude Code 配置（.claude/ 目录）中的安全漏洞、配置错误和注入风险。检查 CLAUDE.md、settings.json、MCP 服务端、钩子（Hooks）和智能体（Agents）定义。
xu-xiang/everything-claude-code-zh 383
instinct-apply Surfaces relevant instincts during work. Use when starting a task to check if any learned behaviors apply.
humanplane/homunculus 357
session-memory Maintains awareness across sessions. Spawns observer agent on start, loads context, notifies of evolution opportunities.
humanplane/homunculus 357
memory Claude Mind - Search and manage Claude's persistent memory stored in a single portable .mv2 file
memvid/claude-brain 355
mind Claude Mind - Search and manage Claude's persistent memory stored in a single portable .mv2 file
memvid/claude-brain 355
managing-git-workflows Manage Git branching strategies, commit conventions, and collaboration workflows. Use when choosing between trunk-based development, GitHub Flow, or GitFlow, implementing conventional commits for automated versioning, setting up Git hooks for quality gates, or organizing monorepos with clear ownership.
ancoleman/ai-design-components 333
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
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
implementing-service-mesh Implement production-ready service mesh deployments with Istio, Linkerd, or Cilium. Configure mTLS, authorization policies, traffic routing, and progressive delivery patterns for secure, observable microservices. Use when setting up service-to-service communication, implementing zero-trust security, or enabling canary deployments.
ancoleman/ai-design-components 333
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
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
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
designing-distributed-systems When designing distributed systems for scalability, reliability, and consistency. Covers CAP/PACELC theorems, consistency models (strong, eventual, causal), replication patterns (leader-follower, multi-leader, leaderless), partitioning strategies (hash, range, geographic), transaction patterns (saga, event sourcing, CQRS), resilience patterns (circuit breaker, bulkhead), service discovery, and caching strategies for building fault-tolerant distributed architectures.
ancoleman/ai-design-components 333
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
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
architecting-security Design comprehensive security architectures using defense-in-depth, zero trust principles, threat modeling (STRIDE, PASTA), and control frameworks (NIST CSF, CIS Controls, ISO 27001). Use when designing security for new systems, auditing existing architectures, or establishing security governance programs.
ancoleman/ai-design-components 333
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