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Found 1,660 Skills
This skill should be used when containerizing applications with Docker, creating Dockerfiles, docker-compose configurations, or deploying containers to various platforms. Ideal for Next.js, React, Node.js applications requiring containerization for development, production, or CI/CD pipelines. Use this skill when users need Docker configurations, multi-stage builds, container orchestration, or deployment to Kubernetes, ECS, Cloud Run, etc.
Cross-compile OBS Studio plugins from Linux to Windows using MinGW, CMake presets, and CI/CD workflows. Covers toolchain files, headers-only linking, OBS SDK fetching, and multi-platform artifact packaging. Use when building OBS plugins for Windows from Linux or setting up CI pipelines.
Run this repo’s Units+Checkpoints research pipelines end-to-end (survey/综述/review/调研/教程/系统综述/审稿), with workspaces + checkpoints. **Trigger**: run pipeline, kickoff, 继续执行, 自动跑, 写一篇, survey/综述/review/调研/教程/系统综述/审稿. **Use when**: 用户希望端到端跑流程（创建 `workspaces/<name>/`、生成/执行 `UNITS.csv`、遇到 HUMAN checkpoint 停下等待）。 **Skip if**: 用户明确要手工逐条执行（用 `unit-executor`），或你不应自动推进到 prose 阶段。 **Network**: depends on selected pipeline (arXiv/PDF/citation verification may need network; offline import supported where available). **Guardrail**: 必须尊重 checkpoints（无 Approve 不写 prose）；遇到 HUMAN 单元必须停下等待；禁止在 repo root 创建 workspace 工件。
Write a systematic review protocol into `output/PROTOCOL.md` (databases, queries, inclusion/exclusion, time window, extraction fields). **Trigger**: protocol, PRISMA, systematic review, inclusion/exclusion, 检索式, 纳入排除. **Use when**: systematic review pipeline 的起点（C1），需要先锁定 protocol 再开始 screening/extraction。 **Skip if**: 不是做 systematic review（或 protocol 已经锁定且不允许修改）。 **Network**: none. **Guardrail**: protocol 必须包含可执行的检索与筛选规则；需要 HUMAN 签字后才能进入 screening。
Build multiple AI agents that work together. Use when you need a supervisor agent that delegates to specialists, agent handoff, parallel research agents, support escalation (L1 to L2), content pipeline (writer + editor + fact-checker), or any multi-agent system. Powered by DSPy for optimizable agents and LangGraph for orchestration.
Name a business, product, or service and secure a matching domain. Use when brainstorming names, evaluating name quality, checking domain availability, choosing between name candidates, or planning a domain strategy. Covers naming frameworks, name-quality criteria, trademark basics, domain extensions, and the full name-to-domain pipeline. Trigger on "help me name my business", "name ideas", "find a domain", "business name", "product name", "domain name", "what should I call it", "naming strategy", "check domain availability".
Run cold and warm outreach campaigns to find and engage potential customers or partners. Use when building a prospecting pipeline, writing cold emails or LinkedIn messages, identifying and qualifying leads, planning an outreach strategy, or scaling lead generation as a solopreneur. Covers lead identification, qualification frameworks, cold email writing, LinkedIn outreach, multi-touch sequences, and tracking. Trigger on "cold outreach", "prospecting", "find customers", "cold email", "LinkedIn outreach", "lead generation", "outreach strategy", "build a pipeline", "find clients".
Implement 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.
Strategic guidance for operationalizing machine learning models from experimentation to production. Covers experiment tracking (MLflow, Weights & Biases), model registry and versioning, feature stores (Feast, Tecton), model serving patterns (Seldon, KServe, BentoML), ML pipeline orchestration (Kubeflow, Airflow), and model monitoring (drift detection, observability). Use when designing ML infrastructure, selecting MLOps platforms, implementing continuous training pipelines, or establishing model governance.
End-to-end guidance for protein design pipelines. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design. For QC thresholds, use protein-qc.
Orchestration pattern for sequential, dependent tasks. When work must flow through stages where each stage depends on the previous (design → implement → test → review), structure as a pipeline with explicit handoffs. Each stage completes before the next begins.
NestJS 11+ best practices for enterprise Node.js applications with TypeScript. Use when writing, reviewing, or refactoring NestJS controllers, services, modules, or APIs. Triggers on: NestJS modules, controllers, providers, dependency injection, @Injectable, @Controller, @Module, middleware, guards, interceptors, pipes, exception filters, ValidationPipe, class-validator, class-transformer, DTOs, JWT authentication, Passport strategies, @nestjs/passport, TypeORM entities, Prisma client, Drizzle ORM, repository pattern, circular dependencies, forwardRef, @nestjs/swagger, OpenAPI decorators, GraphQL resolvers, @nestjs/graphql, microservices, TCP transport, Redis transport, NATS, Kafka, NestJS 11 breaking changes, Express v5 migration, custom decorators, ConfigService, @nestjs/config, health checks, or NestJS testing patterns.