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Import Skills

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labring-actions labring-actions
from GitHub Ops & Delivery
  • 📁 references/
  • 📁 scripts/
  • 📄 SKILL.md
devops automatically documentation

docker-to-sealos

Convert Docker Compose files or deployment documentation into complete Sealos templates automatically. Use this skill when the user provides Docker Compose YAML content or installation documentation and requests a Sealos template. This skill handles all complexity levels including databases, caching, object storage, ConfigMaps, and persistent volumes. The skill operates fully automatically without requiring user input during conversion.

⬇0 ❤41 11 days ago · Uploaded Detail →
Swayyyyy Swayyyyy
from GitHub Development & Coding
  • 📁 scripts/
  • 📄 SKILL.md
frontend main devops

vibebridge-main-install

Install, deploy, or reconfigure a VibeBridge Main server. Use when setting up configs/main.toml, asking the user for required main deployment settings, creating a dedicated Python virtualenv, deciding whether to rebuild the frontend, choosing nohup/systemd/launchd/manual process management, and validating the Main health endpoint plus browser entrypoint. 适用于安装或部署 VibeBridge Main 服务，并在改配置前向用户确认必要参数。

⬇0 ❤36 10 days ago · Uploaded Detail →
juchanhwang juchanhwang
from GitHub Databases & Storage
  • 📄 SKILL.md
database api devops

be

백엔드 도메인 knowledge. Node.js/TypeScript API 설계, PostgreSQL/Drizzle ORM, 인증/보안, 캐싱(Redis), 메시지 큐(BullMQ), 동시성, 분산 시스템, 마이크로서비스, 성능 최적화, 장애 대응, 배포, 모니터링 시 활성화. 서버 로직, DB 쿼리, API endpoint 작성/수정/리뷰 시, 백엔드 설계 판단이 필요할 때 사용한다. 사용자가 API endpoint, DB 스키마, 쿼리 최적화, 인증 플로우, 캐시 전략, 큐 처리, 배포 파이프라인, 서버 에러를 언급하면 반드시 이 스킬을 활성화하라. 명시적으로 '백엔드'라고 말하지 않더라도 서버 사이드 로직, 인프라, .controller.ts/.service.ts/.module.ts/.entity.ts 파일을 다루는 작업이면 활성화한다. --- # BE Domain Knowledge 매핑 테이블의 참조 파일에는 프로젝트 고유의 아키텍처 결정, 검증된 패턴, 안티패턴이 담겨 있다. 이를 읽지 않으면 기존 시스템 설계와 충돌하는 코드를 작성하게 되고, 리뷰에서 반려된다. 코드를 작성하거나 리뷰하기 전에 아래 매핑 테이블에서 태스크에 해당하는 파일을 반드시 Read하라. **기본 경로**: `~/.claude/skills/be/` — 아래 테이블의 파일명 앞에 이 경로를 붙여서 Read한다. ## 핵심 원칙 - 시스템 철학: "견고하고 확장 가능한 시스템" - 4대 원칙: 안정성(Reliability), 확장성(Scalability), 관찰 가능성(Observability), 보안(Security) - 기술 스택: TypeScript strict, Node.js, Fastify 5, PostgreSQL 16+, Drizzle ORM, Redis, BullMQ, Pino, Vitest - API 계약: RFC 9457 Problem Details 표준, Breaking change는 versioning - 안티패턴: Happy path만 구현, 추측 기반 최적화, "나중에 보안 처리", 로그 없는 시스템 ## 태스크-지식 매핑 | 태스크 유형 | 판단 기준 | Read할 파일 | |---|---|---| | API 설계/구현 | endpoint 정의·요청/응답 스키마·라우팅 | `api-design.md` + `error-handling.md` | | DB 스키마/쿼리 | 테이블 설계·마이그레이션·raw 쿼리 | `database.md` + `postgresql.md` | | Drizzle ORM 작업 | ORM 스키마·쿼리 빌더·관계 정의 | `drizzle-orm.md` + `database.md` | | 프로젝트 구조/설계 | 디렉토리·모듈·레이어 구조 결정 | `architecture.md` | | 인증/인가/보안 | 로그인·토큰·권한·암호화 | `security.md` + `api-design.md` | | 테스트 작성 | 단위·통합·E2E 서버 테스트 | `testing.md` | | 로깅/모니터링 | 로그 수집·메트릭·알럿·트레이싱 | `observability.md` | | 에러 처리 | 예외 설계·에러 응답·복구 전략 | `error-handling.md` + `observability.md` | | 성능 최적화 | 응답 시간·쓰루풋·병목 해결 | `performance.md` + `nodejs-internals.md` + `postgresql.md` | | 배포/인프라 | CI/CD·컨테이너·환경 설정 | `deployment.md` + `architecture.md` + `observability.md` | | 시스템 설계 | 대규모 아키텍처·서비스 간 통신 | `system-design.md` + `distributed-systems.md` + `microservices.md` | | 캐싱 | Redis·캐시 전략·무효화 정책 | `caching.md` + `performance.md` | | 메시지 큐 | 비동기 처리·이벤트·BullMQ | `message-queues.md` + `distri

⬇0 ❤24 8 days ago · Uploaded Detail →
dash0hq dash0hq
from GitHub Ops & Delivery
  • 📁 rules/
  • 📄 README.md
  • 📄 SKILL.md
devops collector configuring

otel-collector

Expert guidance for configuring and deploying the OpenTelemetry Collector. Use when setting up a Collector pipeline, configuring receivers, exporters, or processors, deploying a Collector to Kubernetes or Docker, or forwarding telemetry to Dash0. Triggers on requests involving collector, pipeline, OTLP receiver, exporter, or Dash0 collector setup.

⬇0 ❤26 11 days ago · Uploaded Detail →
lithqube lithqube
from GitHub Development & Coding
  • 📁 concepts/
  • 📁 examples/
  • 📁 patterns/
  • 📄 SKILL.md
devops jetstream python

jetstream-architecture

Use this skill whenever users are designing, modeling, or writing code for NATS JetStream — including stream configuration, subject namespace design, consumer types (pull vs push), ack policies, retention policies, delivery guarantees, messaging patterns (fanout, work queue, request-reply), idempotent publishing, exactly-once semantics, or JetStream code examples in Go, JavaScript, or Python. Use this skill even when the user doesn't say "JetStream" explicitly — if they're asking how to build a message queue, event stream, or worker system on NATS, this skill applies. Do NOT use for deployment/clustering/Kubernetes questions (use jetstream-deployment) or troubleshooting/monitoring (use jetstream-operations).

⬇0 ❤23 9 days ago · Uploaded Detail →
digitalocean-labs digitalocean-labs
from GitHub Databases & Storage
  • 📁 .github/
  • 📁 analytics/
  • 📁 architecture/
  • 📄 .ggshield.yaml
  • 📄 .gitguardian.yaml
  • 📄 .gitignore
database devops data

app-platform-router

Routes DigitalOcean App Platform tasks to specialized sub-skills. Use when working with App Platform deployments, migrations, database configuration, networking, or troubleshooting.

⬇0 ❤18 6 days ago · Uploaded Detail →
fuzhengwei fuzhengwei
from GitHub Ops & Delivery
  • 📁 references/
  • 📁 scripts/
  • 📄 .DS_Store
  • 📄 .gitignore
  • 📄 README.md
devops ddd infrastructure

xfg-ddd-skills

DDD 六边形架构设计与部署技能包。提供 Domain/Case/Infrastructure 层设计模式与代码模板，以及 Docker 环境部署脚本。当用户询问 DDD 架构、设计模式或需要部署项目时调用。

⬇0 ❤22 11 days ago · Uploaded Detail →
UiPath UiPath
from GitHub Tools & Productivity
  • 📁 assets/
  • 📁 references/
  • 📄 SKILL.md
automation python devops

uipath-agents

UiPath agent lifecycle — coded (Python: LangGraph/LlamaIndex/OpenAI Agents) and low-code (agent.json from Agent Builder). Setup, auth, build, run, evaluate, deploy, sync. For C# or XAML workflows→uipath-rpa.

⬇0 ❤23 12 days ago · Uploaded Detail →
UnityAppSuite UnityAppSuite
from GitHub Business & Operations
  • 📄 SKILL.md
management devops bench

bench-commands

Frappe Bench CLI command reference for site management, app management, development, and production operations. Use when running bench commands, managing sites, migrations, builds, or deployments.

⬇0 ❤14 8 days ago · Uploaded Detail →

Skill File Structure Sample (Reference)

skill-sample/
├─ SKILL.md              ⭐ Required: skill entry doc (purpose / usage / examples / deps)
├─ manifest.sample.json  ⭐ Recommended: machine-readable metadata (index / validation / autofill)
├─ LICENSE.sample        ⭐ Recommended: license & scope (open source / restriction / commercial)
├─ scripts/
│  └─ example-run.py     ✅ Runnable example script for quick verification
├─ assets/
│  ├─ example-formatting-guide.md  🧩 Output conventions: layout / structure / style
│  └─ example-template.tex         🧩 Templates: quickly generate standardized output
└─ references/           🧩 Knowledge base: methods / guides / best practices
   ├─ example-ref-structure.md     🧩 Structure reference
   ├─ example-ref-analysis.md      🧩 Analysis reference
   └─ example-ref-visuals.md       🧩 Visual reference

More Agent Skills specs Anthropic docs: https://agentskills.io/home

SKILL.md Requirements

├─ ⭐ Required: YAML Frontmatter (must be at top)
│  ├─ ⭐ name                 : unique skill name, follow naming convention
│  └─ ⭐ description          : include trigger keywords for matching
│
├─ ✅ Optional: Frontmatter extension fields
│  ├─ ✅ license              : license identifier
│  ├─ ✅ compatibility        : runtime constraints when needed
│  ├─ ✅ metadata             : key-value fields (author/version/source_url...)
│  └─ 🧩 allowed-tools        : tool whitelist (experimental)
│
└─ ✅ Recommended: Markdown body (progressive disclosure)
   ├─ ✅ Overview / Purpose
   ├─ ✅ When to use
   ├─ ✅ Step-by-step
   ├─ ✅ Inputs / Outputs
   ├─ ✅ Examples
   ├─ 🧩 Files & References
   ├─ 🧩 Edge cases
   ├─ 🧩 Troubleshooting
   └─ 🧩 Safety notes

Why SkillWink?

Skill files are scattered across GitHub and communities, difficult to search, and hard to evaluate. SkillWink organizes open-source skills into a searchable, filterable library you can directly download and use.

We provide keyword search, version updates, multi-metric ranking (downloads / likes / comments / updates), and open SKILL.md standards. You can also discuss usage and improvements on skill detail pages.

Keyword Search Version Updates Multi-Metric Ranking Open Standard Discussion

Quick Start:

Import/download skills (.zip/.skill), then place locally:

~/.claude/skills/ (Claude Code)

~/.codex/skills/ (Codex CLI)

One SKILL.md can be reused across tools.

FAQ

Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.

1. What are Agent Skills?

A skill is a reusable capability package, usually including SKILL.md (purpose/IO/how-to) and optional scripts/templates/examples.

Think of it as a plugin playbook + resource bundle for AI assistants/toolchains.

2. How do Skills work?

Skills use progressive disclosure: load brief metadata first, load full docs only when needed, then execute by guidance.

This keeps agents lightweight while preserving enough context for complex tasks.

3. How can I quickly find the right skill?

Use these three together:

  • Semantic search: describe your goal in natural language.
  • Multi-filtering: category/tag/author/language/license.
  • Sort by downloads/likes/comments/updated to find higher-quality skills.

4. Which import methods are supported?

  • Upload archive: .zip / .skill (recommended)
  • Upload skills folder
  • Import from GitHub repository

Note: file size for all methods should be within 10MB.

5. How to use in Claude / Codex?

Typical paths (may vary by local setup):

  • Claude Code：~/.claude/skills/
  • Codex CLI：~/.codex/skills/

One SKILL.md can usually be reused across tools.

6. Can one skill be shared across tools?

Yes. Most skills are standardized docs + assets, so they can be reused where format is supported.

Example: retrieval + writing + automation scripts as one workflow.

7. Are these skills safe to use?

Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.

8. Why does it not work after import?

Most common reasons:

  • Wrong folder path or nested one level too deep
  • Invalid/incomplete SKILL.md fields or format
  • Dependencies missing (Python/Node/CLI)
  • Tool has not reloaded skills yet

9. Does SkillWink include duplicates/low-quality skills?

We try to avoid that. Use ranking + comments to surface better skills:

  • Duplicate skills: compare differences (speed/stability/focus)
  • Low quality skills: regularly cleaned up