This skill should be used when the user asks to "콘텐츠 만들어줘", "카드뉴스 만들어줘", "카드뉴스 영상 만들어줘", "리서치부터 영상까지", "콘텐츠 파이프라인", "content pipeline", "주제로 콘텐츠 만들어줘". 주제 하나로 리서치→카드뉴스→영상까지 풀 파이프라인을 자동 실행합니다. Make sure to use this skill whenever the user mentions content creation that involves research, card news, or video generation from a topic.
Read, search, send, and manage emails from the MailClaw inbox via the local CLI. Use when the user asks to check emails, read messages, search inbox, find emails from a sender, review recent correspondence, or send/reply to emails.
Build durable, fault-tolerant workflows using Azure Durable Functions with .NET isolated worker and Durable Task Scheduler backend. Use when creating serverless orchestrations, activities, entities, or implementing patterns like function chaining, fan-out/fan-in, async HTTP APIs, human interaction, monitoring, or stateful aggregators. Applies to Azure Functions apps requiring durable execution, state persistence, or distributed coordination with built-in HTTP management APIs and Azure integration.
Create, produce, and publish UGC-style short-form video reels at scale. Full pipeline: source UGC reaction hooks from DanSUGC, analyze app demos with Gemini AI, assemble reels with ffmpeg, publish via DanSUGC Posting (TikTok + Instagram), track performance and research viral formats/hooks via DanSUGC's built-in analytics proxy.
This skill encodes Emil Kowalski's philosophy on UI polish, component design, animation decisions, and the invisible details that make software feel great.
> 判断复杂度 → 系统化调研 → 选框架分析 → 输出结论
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Run vLLM performance benchmark using synthetic random data to measure throughput, TTFT (Time to First Token), TPOT (Time per Output Token), and other key performance metrics. Use when the user wants to quickly test vLLM serving performance without downloading external datasets.
The Skill plugin gives the agent a persistent, structured knowledge base. Knowledge is organised into named **skills**, each containing any number of **items** — individual pieces of information. Items are indexed semantically, so the agent can search across all skills by meaning rather than exact keywords.
Manages Architecture Decision Records (ADR) for tracking important architectural decisions
Applies Ralphify's official brand colors, typography, and visual style to any artifact — landing pages, slides, diagrams, social graphics. Use when Ralphify's look-and-feel should be applied.
Show the plugin source — name, version, and repo URL. Works from CLI or messaging. Triggers on /about, /version, /agent:about, /agent:version, "qué versión", "what version", "about the plugin", "about clawcode".
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
├─ ⭐ 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
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.
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.
Everything you need to know: what skills are, how they work, how to find/import them, and how to contribute.
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.
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.
Use these three together:
Note: file size for all methods should be within 10MB.
Typical paths (may vary by local setup):
One SKILL.md can usually be reused 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.
Some skills come from public GitHub repositories and some are uploaded by SkillWink creators. Always review code before installing and own your security decisions.
Most common reasons:
We try to avoid that. Use ranking + comments to surface better skills:
