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Import Skills

Composite Most Downloads Most Likes Most Comments Newest
tansoft tansoft
from GitHub Tools & Productivity
  • 📄 cloudperf_api.py
  • 📄 SKILL.md
data performance network

cloudperf

CloudPerf network performance testing tool. Query cloud network quality data including citysets, countries, cities, ASNs, and performance metrics between source and destination nodes. Use when the user asks about network performance, latency, cloud region comparison, or CloudPerf data.

⬇0 ❤7 17 hours ago · Uploaded Detail →
Mann1988 Mann1988
from GitHub Business & Operations
  • 📄 SKILL.md
mckinsey data ppt

mckinsey-consultant

McKinsey顾问式问题解决系统。从商业问题出发,通过假设驱动的结构化分析方法,生成McKinsey风格研究报告和PPT。融合Problem Solving方法论、MECE原则、Issue Tree拆解、Hypotheses形成、Dummy Page设计、智能数据收集和专业PPT生成能力。

⬇0 ❤34 7 days ago · Uploaded Detail →
vobase vobase
from GitHub Tools & Productivity
  • 📄 SKILL.md
automation data browser

agent-browser

Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.

⬇0 ❤43 13 days ago · Uploaded Detail →
BBuf BBuf
from GitHub Data & AI
  • 📄 SKILL.md
diffusion sglang data

h100-sglang-diffusion

SSH into host `h100_sglang`, enter Docker container `sglang_bbuf`, work in `/data/bbuf/repos/sglang`, and use the ready H100 remote environment for SGLang **diffusion** development and validation. Use when a task needs diffusion model smoke tests, Triton/CUDA kernel validation, torch.compile diffusion checks, or a safe remote copy for diffusion-specific SGLang changes.

⬇0 ❤40 10 days ago · Uploaded Detail →
cyberxuan-XBX cyberxuan-XBX
from GitHub Development & Coding
  • 📄 LICENSE
  • 📄 README.md
  • 📄 SKILL.md
ai data open-source

skill-sanitizer

First open-source AI sanitizer with local semantic detection. 7 layers + code block awareness + LLM intent analysis. Catches prompt injection, reverse shells, memory tampering, encoding evasion, trust abuse. 85% fewer false positives in v2.1. Zero cloud — your prompts stay on your machine.

⬇0 ❤35 8 days ago · Uploaded Detail →
tryopendata tryopendata
from GitHub Development & Coding
  • 📁 references/
  • 📄 SKILL.md
openchart data rendering

openchart

Generates OpenChart (https://github.com/tryopendata/openchart) chart, table, graph, and sankey specs from data, and guides editorial design decisions. Use when creating visualizations, building charts, rendering data tables, generating VizSpec JSON, creating network graphs, building sankey/flow diagrams, answering questions about OpenChart types and encoding rules, or making design decisions about chart type selection, color strategy, typography, annotations, and editorial framing. Also covers custom D3.js infographics for cases beyond declarative specs. --- # Data Visualization with OpenChart **Core concept:** Write a VizSpec JSON object, render with `<Chart>` / `<DataTable>` / `<Graph>` / `<Sankey>` (React/Vue/Svelte) or `createChart()` / `createTable()` / `createGraph()` / `createSankey()` (vanilla JS). The engine validates, compiles, and renders. Specs are plain JSON, no imperative drawing. See https://github.com/tryopendata/openchart for the rendering engine. **CSS is required.** OpenChart's stylesheet must be loaded for proper rendering (chrome, tables, tooltips, brand watermark). Framework imports handle this automatically, but CDN/standalone HTML needs an explicit `<link>`: ```html <link rel="stylesheet" href="https://esm.sh/@opendata-ai/openchart-vanilla/styles.css"> ``` See [rendering reference](references/rendering.md) for details. ## Chart Selection Decision Tree ``` Single value to highlight -> Use chrome.title as a big number display Temporal x-axis column? -> 1 series: line | 2-5 series: line + color | 6+: filter to top 5 Categorical + numeric? -> Ranked list: bar (horizontal) | Periodic (Q1, Jan): bar (vertical) | 2-6 composition: arc Two numeric columns? -> point (optional size/color for 3rd/4th dims) Categorical + series + num? -> stacked bar (use color for series) Distribution/spread? -> circle (strip plot) Nodes + edges / network? -> graph (force/radial/hierarchical layout) Flow between stages? -> sankey

⬇0 ❤40 11 days ago · Uploaded Detail →
0xE1337 0xE1337
from GitHub Tools & Productivity
  • 📁 references/
  • 📄 SKILL.md
支持两种输出格式 data app.diagrams.net

thesis-figure-skill

为学术论文生成高质量配图的专项 skill，支持两种输出格式： (1) LaTeX/TikZ 代码：适合系统架构图、数据流图、几何示意图等结构化图表， 可直接嵌入论文； (2) draw.io XML：适合技术路线图、科研展示图、学术汇报配图等装饰性强的 图表，支持渐变色、阴影、自由布局，可在 app.diagrams.net 打开编辑。 支持两种输出格式，统一工作流程：分析输入（文案/图片/论文）→ 画图指令 → 代码生成 → 编译验证 → 满分交付。 自动识别论文所属领域并以该领域专家身份进行配图设计。

⬇0 ❤40 14 days ago · Uploaded Detail →
AstrBotDevs AstrBotDevs
from GitHub Tools & Productivity
  • 📁 references/
  • 📄 SKILL.md
automation data browser

browser-automation

Gull runtime usage guide for browser automation sandboxes. Covers the agent-browser CLI passthrough model, snapshot-based element referencing, single vs batch execution strategies, artifact handling, and browser state persistence. Use this when working inside a Gull container to understand command syntax, critical rules for reliable automation, and common patterns for navigation, form filling, data extraction, and screenshot capture.

⬇0 ❤39 13 days ago · Uploaded Detail →
bostonaholic bostonaholic
from GitHub Data & AI
  • 📁 references/
  • 📄 SKILL.md
reflect data reviews

reflect

This skill should be used when someone needs to generate a brag document from GitHub activity, set up reflect for the first time, run reflect to fetch contributions, configure a GitHub token for reflect, filter contributions by organization or repository, choose between OpenAI and Anthropic providers, understand reflect output files, troubleshoot reflect not working, or debug brag doc errors. --- # Reflect Reflect is a CLI tool that fetches GitHub activity -- merged pull requests, closed issues, and PR reviews -- and uses LLM APIs to generate professional brag documents for performance reviews. It connects to the GitHub GraphQL API via Octokit to retrieve contribution data, then optionally passes that data through an LLM provider (OpenAI or Anthropic) to produce summarized and narrative-format documents. All output is written as structured Markdown files suitable for self-assessments, promotion packets, and manager reviews. ## First-Time Setup ### Prerequisites Ensure the following are available before running Reflect

⬇0 ❤36 9 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