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Import Skills

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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 20 days ago · Uploaded Detail →
voidborne-d voidborne-d
from GitHub Data & AI
  • 📁 claude-code/
  • 📁 evals/
  • 📁 examples/
  • 📄 .gitignore
  • 📄 package.json
  • 📄 README.md
academic python detection

humanize-chinese

Detect and humanize AI-generated Chinese text. 20+ detection categories, weighted 0-100 scoring with sentence-level analysis, 7 style transforms (casual/zhihu/xiaohongshu/wechat/academic/literary/weibo), sentence restructuring, context-aware replacement. Academic paper AIGC reduction for CNKI/VIP/Wanfang (知网/维普/万方 AIGC 检测降重), 10 academic detection dimensions, 120+ scholarly expression replacements, hedging language injection. Pure Python, no dependencies. v2.1.0

⬇0 ❤23 21 days ago · Uploaded Detail →
sjdv1982 sjdv1982
from GitHub Development & Coding
  • 📁 references/
  • 📄 SKILL.md
execution backend python

seamless-adoption

Assesses whether an existing Python, bash, or hybrid pipeline is a good fit for Seamless (content-addressed caching, reproducible execution, local-to-cluster scaling). Triggers when wrapping scripts or functions without rewriting them, avoiding recomputation, comparing workflow frameworks (vs Snakemake, Nextflow, CWL, Airflow, Prefect), migrating a pipeline, or setting up remote/HPC execution. Covers direct/delayed decorators, seamless-run CLI, nesting, module inclusion, scratch/witness patterns, deep checksums, and execution backends (local, jobserver, daskserver). Provides safe guidance on remote execution and determinism — avoids naive "copy code to server" suggestions.

⬇0 ❤20 22 days ago · Uploaded Detail →
NVIDIA-AI-IOT NVIDIA-AI-IOT
from GitHub Development & Coding
  • 📁 references/
  • 📄 SKILL.md
python integration video

deepstream-dev

NVIDIA DeepStream SDK 9.0 development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.

⬇0 ❤20 22 days ago · Uploaded Detail →
ds1sqe ds1sqe
from GitHub Development & Coding
  • 📁 development/
  • 📁 getting-started/
  • 📁 guide/
  • 📄 index.md
  • 📄 SKILL.md
python typedb expressions

type-bridge

Use the type-bridge Python ORM for TypeDB. Covers defining entities, relations, attributes, CRUD operations, queries, expressions, and schema management. Use when working with TypeDB in Python projects.

⬇0 ❤18 16 days ago · Uploaded Detail →
wzj177 wzj177
from GitHub Content & Multimedia
  • 📁 assets/
  • 📁 references/
  • 📁 scripts/
  • 📄 .gitignore
  • 📄 LICENSE
  • 📄 README.md
scripts output_dir python

ai-tryon

AI 虚拟试穿 Agent。用户提供服装信息（图片或文字描述均可）， Agent 全程引导完成：服装图预处理 → AI 生成模特 → 虚拟试穿合成 → 生成展示视频。 支持阿里云百炼试衣 API、豆包 Seedream 生图、豆包 Seedance 生视频。 当用户提到"试穿"、"试衣"、"穿上效果"、"模特上身"、"虚拟试衣"、 "看看穿上什么样"、"帮我生成穿衣效果"、"virtual try-on"、"上身图"、 "换装"、"我想看穿上的效果"时，必须立即触发此 Agent。 --- # AI 虚拟试穿 Agent ## 职责 引导用户完成虚拟试穿全流程，输出试穿效果图和展示视频。 不涉及上架、文案、定价。有上架需求告知使用 shopify-quick-listing。 --- ## 配置说明（告知用户时必须按此说明） **.env 文件的唯一标准位置是 `scripts/` 目录：** ``` ~/.claude/skills/ai-tryon/scripts/.env ← 正确位置 ~/.claude/skills/ai-tryon/.env ← 错误，不要放这里 ``` 告知用户配置的标准话术： > 请在 Skill 的 scripts 目录下创建 .env 文件： > ```bash > cp ~/.claude/skills/ai-tryon/scripts/.env.example \ > ~/.claude/skills/ai-tryon/scripts/.env > # 然后编辑填入 Key > ``` 不要让用户在 `ai-tryon/` 根目录或其他位置创建 .env。 --- ## 输出目录约束（最高优先级规则） **所有脚本调用都必须传 `--output-dir`，绝对禁止省略。** 输出目录的唯一真实来源是 `.env` 中的 `TRYON_OUTPUT_DIR` 环境变量： ```bash # .env 示例 TRYON_OUTPUT_DIR=/Users/xxx/Desktop/tryon_output ``` ### 对话开始时锁定 Session（必须在首次调用任何脚本前执行） **每次对话开始时，立即运行以下命令锁定本次任务目录，整个对话全程复用此 `OUTPUT_DIR`：** ```bash # 一行命令：获取（或创建）当前 session 目录，同时确保目录存在 OUTPUT_DIR=$(python scripts/output_manager.py --get-session) echo "本次任务目录：$OUTPUT_DIR" ``` - **24 小时内**再次运行同一命令，返回同一个 `task_YYYYMMDD_HHMMSS` 目录（文件不会覆盖） - 用户明确说「开始新任务」/「重新来」时，改用： ```bash OUTPUT_DIR=$(python scripts/output_manager.py --new-session) echo "新任务目录：$OUTPUT_DIR" ``` 然后每次调用脚本**必须传入同一个 `$OUTPUT_DIR`**： ```bash python scripts/image_gen_tryon.py --desc "..." --output-dir "$OUTPUT_DIR" python scripts/tryon_runner.py --garment g.jpg --output-dir "$OUTPUT_DIR" python scripts/video_gen.py --image img.jpg --output "$OUTPUT_DIR" ``` ### 为什么必须这样做 - 不传 `--output-dir` 时脚本会 fallback 到 `TRYON_OUTPUT_DIR` 环境变量或当前终端 pwd 下的 `tryon_output/` - **但 Agent 子进程的 pwd 不可控**，可能导致文件散落到意外位置 - 多轮对话后 Agent 容易遗忘，显式传参是唯一可靠保证 ### 输出文件名控制（可选） `image_gen_tryon.py` 支持 `--output-filename`，生成后会将第一个结果复制为指定文件名： ```bash python scripts/image_gen_tryon.py --desc "..." --output-dir "$OUTPUT_DIR" \ --output-filename model_ruyan_custom.jpg ``` ### 目录结构 每次对话/试穿任务自动创建独立的 session 子目录（以日期

⬇0 ❤17 19 days ago · Uploaded Detail →
booklib-ai booklib-ai
from GitHub Development & Coding
  • 📄 skill.md
python booklib data

booklib-skills

BookLib — curated skills from canonical programming books. Covers Kotlin, Python, Java, TypeScript, Rust, architecture, DDD, data-intensive systems, UI design, and more. Install individual skills via npx skillsadd booklib-ai/booklib/<name>.

⬇0 ❤17 20 days ago · Uploaded Detail →
gccszs gccszs
from GitHub Tools & Productivity
  • 📁 diskcleaner/
  • 📁 docs/
  • 📁 references/
  • 📄 AGENT_QUICK_REF.txt
  • 📄 disk-cleaner.skill
  • 📄 INSTALL.md
python intelligent automation

disk-cleaner

Cross-platform disk space management toolkit with intelligent optimization. REQUIREMENTS: Python 3.7+. UNIVERSAL COMPATIBILITY: Works with ALL AI IDEs (Cursor, Windsurf, Continue, Aider, Claude Code, etc.). PLATFORM-INDEPENDENT: Works at any location - global, project, or user level. SELF-CONTAINED: No pip install needed, includes intelligent bootstrap. KEY FEATURES: (1) PROGRESSIVE SCANNING: Quick sample (1s) + Progressive mode for large disks, (2) INTELLIGENT BOOTSTRAP: Auto-detection of skill location and auto-import of modules, (3) CROSS-PLATFORM ENCODING: Safe emoji/Unicode handling on all platforms, (4) DIAGNOSTIC TOOLS: check_skill.py for quick verification, (5) OPTIMIZED SCANNING: 3-5x faster with os.scandir(), concurrent scanning, intelligent sampling. AGENT WORKFLOW: (1) Check Python, (2) Find skill package (20+ locations auto-detected), (3) Run diagnostics, (4) Use progressive scanning for large disks. The skill package includes all optimization modules - no features are lost!

⬇0 ❤16 19 days ago · Uploaded Detail →
greekr4 greekr4
from GitHub Development & Coding
  • 📄 skill.md
advooster python advooster_electron

advooster-analyze

AdVooster_Electron 프로젝트(/Users/tk/AdVooster_Electron)의 Python 코드를 분석하여 viruagent-cli에 포팅할 비즈니스 로직, API 엔드포인트, 인증 흐름, 데이터 구조를 추출한다. 'AdVooster 분석', '카페 API 분석', '카페 가입 분석', 'AdVooster에서 가져와', 'advooster', '기존 코드 분석' 등을 언급하면 이 스킬을 사용할 것.

⬇0 ❤14 21 days ago · Uploaded Detail →
workos workos
from GitHub Development & Coding
  • 📁 references/
  • 📄 SKILL.md
frontend widgets python

workos-widgets

Build, integrate, or migrate WorkOS Widgets in modern web apps. Use this skill when implementing User Management, User Profile, Admin Portal SSO Connection, or Admin Portal Domain Verification widgets across Next.js, React Router, TanStack Router, TanStack Start, Vite, SvelteKit, Ruby, Python, Go, PHP, or Java stacks. Detect the active stack, auth/token strategy, data-layer style, and UI conventions; then implement widget integration with correct access-token flow and API calls based on the bundled Widgets OpenAPI spec.

⬇0 ❤13 21 days ago · Uploaded Detail →
ClickHouse ClickHouse
from GitHub Development & Coding
  • 📄 SKILL.md
nerve frontend python

Nerve Development

Nerve backend (Python) and frontend (React/TS) development and code contribution. Use when writing Python code for Nerve, fixing bugs, adding features, reviewing Nerve PRs, building the frontend, running tests, or working with the Nerve codebase. Triggers on "nerve code", "nerve PR", "fix nerve", "nerve feature", "nerve test", "build nerve UI", "nerve migration".

⬇0 ❤12 21 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