共 5368 个 Skill，按仓库 star 排序。分类自动生成，仅供参考。
ancoleman/ai-design-components526Configure host-based firewalls (iptables, nftables, UFW) and cloud security groups (AWS, GCP, Azure) with practical rules for common scenarios like web servers, databases, and bastion hosts. Use when exposing services, hardening servers, or implementing network segmentation with defense-in-depth strategies.
前端开发
ancoleman/ai-design-components526Configure nginx for static sites, reverse proxying, load balancing, SSL/TLS termination, caching, and performance tuning. When setting up web servers, application proxies, or load balancers, this skill provides production-ready patterns with modern security best practices for TLS 1.3, rate limiting, and security headers.
前端开发
ancoleman/ai-design-components526Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.
前端开发
ancoleman/ai-design-components526Debugging workflows for Python (pdb, debugpy), Go (delve), Rust (lldb), and Node.js, including container debugging (kubectl debug, ephemeral containers) and production-safe debugging techniques with distributed tracing and correlation IDs. Use when setting breakpoints, debugging containers/pods, remote debugging, or production debugging.
前端开发
ancoleman/ai-design-components526Deployment patterns from Kubernetes to serverless and edge functions. Use when deploying applications, setting up CI/CD, or managing infrastructure. Covers Kubernetes (Helm, ArgoCD), serverless (Vercel, Lambda), edge (Cloudflare Workers, Deno), IaC (Pulumi, OpenTofu, SST), and GitOps patterns.
前端开发
ancoleman/ai-design-components526Selecting 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.
前端开发
ancoleman/ai-design-components526Design 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.
前端开发
ancoleman/ai-design-components526Implement applications using Google Cloud Platform (GCP) services. Use when building on GCP infrastructure, selecting compute/storage/database services, designing data analytics pipelines, implementing ML workflows, or architecting cloud-native applications with BigQuery, Cloud Run, GKE, Vertex AI, and other GCP services.
前端开发
ancoleman/ai-design-components526Design APIs that are secure, scalable, and maintainable using RESTful, GraphQL, and event-driven patterns. Use when designing new APIs, evolving existing APIs, or establishing API standards for teams.
前端开发
ancoleman/ai-design-components526When 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.
前端开发
ancoleman/ai-design-components526Designs layout systems and responsive interfaces including grid systems, flexbox patterns, sidebar layouts, and responsive breakpoints. Use when structuring app layouts, building responsive designs, or creating complex page structures.
前端开发
ancoleman/ai-design-components526Design production-ready SDKs with retry logic, error handling, pagination, and multi-language support. Use when building client libraries for APIs or creating developer-facing SDK interfaces.
前端开发
ancoleman/ai-design-components526Displays chronological events and activity through timelines, activity feeds, Gantt charts, and calendar interfaces. Use when showing historical events, project schedules, social feeds, notifications, audit logs, or time-based data. Provides implementation patterns for vertical/horizontal timelines, interactive visualizations, real-time updates, and responsive designs with accessibility (WCAG/ARIA).
前端开发
ancoleman/ai-design-components526Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
前端开发
ancoleman/ai-design-components526Evaluate LLM systems using automated metrics, LLM-as-judge, and benchmarks. Use when testing prompt quality, validating RAG pipelines, measuring safety (hallucinations, bias), or comparing models for production deployment.
前端开发
ancoleman/ai-design-components526Generate comprehensive technical documentation including API docs (OpenAPI/Swagger), code documentation (TypeDoc/Sphinx), documentation sites (Docusaurus/MkDocs), Architecture Decision Records (ADRs), and diagrams (Mermaid/PlantUML). Use when documenting APIs, libraries, systems architecture, or building developer-facing documentation sites.
前端开发
ancoleman/ai-design-components526Implements onboarding and help systems including product tours, interactive tutorials, tooltips, checklists, help panels, and progressive disclosure patterns. Use when building first-time experiences, feature discovery, guided walkthroughs, contextual help, setup flows, or user activation features. Provides timing strategies, accessibility patterns (keyboard, screen readers, reduced motion), and metrics for measuring onboarding success.
前端开发
ancoleman/ai-design-components526API design and implementation across REST, GraphQL, gRPC, and tRPC patterns. Use when building backend services, public APIs, or service-to-service communication. Covers REST frameworks (FastAPI, Axum, Gin, Hono), GraphQL libraries (Strawberry, async-graphql, gqlgen, Pothos), gRPC (Tonic, Connect-Go), tRPC for TypeScript, pagination strategies (cursor-based, offset-based), rate limiting, caching, versioning, and OpenAPI documentation generation. Includes frontend integration patterns for forms, tables, dashboards, and ai-chat skills.
前端开发
ancoleman/ai-design-components526Implement and maintain compliance with SOC 2, HIPAA, PCI-DSS, and GDPR using unified control mapping, policy-as-code enforcement, and automated evidence collection. Use when building systems requiring regulatory compliance, implementing security controls across multiple frameworks, or automating audit preparation.
前端开发
VILA-Lab/FigMirror523This FigMirror skill should be used when the user asks to "mirror this figure's style", "copy this figure's style", "make a chart that looks like this paper", "reproduce this figure with my data", "match this paper's aesthetic", "I want a NeurIPS-quality version of this", or any variant where they hand over a cropped or uncropped reference figure AND their own data and want their data rendered in the same visual register. ALSO triggers when the user attaches a paper-figure screenshot plus tabular data and asks for matplotlib output. Does NOT trigger on generic matplotlib chart requests with no reference image — that's a basic matplotlib task, not style transfer.
科研
VILA-Lab/FigMirror523FigMirror mirrors the visual style of a top-conference paper figure (NeurIPS / ICML / ICLR / Nature family) onto the user's own data. Takes dirty data plus a reference figure screenshot (cropped or uncropped), preprocesses the reference crop, runs a Drawer/Reviewer loop, and outputs a camera-ready PDF plus a self-contained matplotlib script with an inline DATA SECTOR.
文档与办公
VILA-Lab/FigMirror523FigMirror mirrors the visual style of a top-conference paper figure (NeurIPS / ICML / ICLR / Nature family) onto the user's own data. Takes dirty data plus a reference figure screenshot (cropped or uncropped), preprocesses the reference crop, runs a Drawer/Reviewer loop, and outputs a camera-ready PDF plus a self-contained matplotlib script with an inline DATA SECTOR.
文档与办公
VILA-Lab/FigMirror523Implement tasks from an OpenSpec change. Use when the user wants to start implementing, continue implementation, or work through tasks.
科研
VILA-Lab/FigMirror523Archive a completed change in the experimental workflow. Use when the user wants to finalize and archive a change after implementation is complete.
科研