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Import Skills

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knoopx knoopx
from GitHub Databases & Storage
  • 📄 SKILL.md
commit conventional messages

conventional-commits

Writes and reviews Conventional Commits commit messages (v1.0.0) to support semantic versioning and automated changelogs. Use when drafting git commit messages, PR titles, release notes, or when enforcing a conventional commit format (type(scope): subject, BREAKING CHANGE, footers, revert).

⬇0 ❤24 24 days ago · Uploaded Detail →
Lakr233 Lakr233
from GitHub Tools & Productivity
  • 📄 SKILL.md
automation user authenticated

website-login

Use whenever the user needs to log in to a website on their phone and reuse that authenticated session locally, especially for Playwright storageState JSON, cookies/localStorage capture, MFA, SMS codes, passkeys, or mobile-first login flows that are awkward in headless automation. You run Cookey CLI in your current environment; the user signs in on their iPhone in the Cookey app; the relay only transports encrypted blobs. Trigger even if the user asks more loosely for help logging in, reusing an authenticated browser state, exporting cookies, or getting a local automation session from a phone login.

⬇0 ❤23 18 days ago · Uploaded Detail →
mcs-cli mcs-cli
from GitHub Docs & Knowledge
  • 📁 references/
  • 📄 SKILL.md
docs ai architectural

continuous-learning

Extracts reusable knowledge (debugging discoveries, architectural decisions, conventions) from work sessions and saves them as structured memory files in .claude/memories/. Also use when the user asks to "run a retrospective", "extract learnings", or "save what we learned" from the current session.

⬇0 ❤21 12 days ago · Uploaded Detail →
tenequm tenequm
from GitHub Data & AI
  • 📄 SKILL.md
recording audio quality

audio-quality-check

Analyze audio recording quality - echo detection, loudness, speech intelligibility, SNR, spectral analysis. Use when the user wants to check a recording's quality, detect echo or duplication in audio files, measure speech clarity, compare original vs processed audio, diagnose why a recording sounds bad, or analyze audio tracks from Blackbox or any call recording app. Triggers on audio quality, recording analysis, echo detection, check recording, sound quality, analyze audio, speech quality, PESQ, STOI, loudness, SNR, audio diagnostics, recording sounds bad, echo in recording, audio duplication.

⬇0 ❤24 24 days ago · Uploaded Detail →
DasDigitaleMomentum DasDigitaleMomentum
from GitHub Docs & Knowledge
  • 📄 SKILL.md
  • 📄 tpl-archive-legacy-docs-prompt.md
  • 📄 tpl-legacy-summary.md
docs docs-legacy generating

archive-legacy-docs

Normalize legacy repositories by moving scattered documentation into docs-legacy/ (git-aware) and generating a docs-legacy/summary.md before generating new docs/plans.

⬇0 ❤24 25 days ago · Uploaded Detail →
hummer98 hummer98
from GitHub Tools & Productivity
  • 📄 SKILL.md
cmux 環境変数 結果回収

using-cmux

cmux ターミナル内での操作スキル。ペイン分割、サブエージェント起動・監視・結果回収、コマンド送信、画面読み取り、通知に使用。CMUX_* 環境変数が存在する場合にトリガーされる。

⬇0 ❤24 25 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 25 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