3,758 Open Skills

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anthropics anthropics
from GitHub Development & Coding
数据 乐器 Python

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full ASM JSON, flattened CSV for easy import, and exportable Python code for data engineers. Common triggers include converting instrument files, standardizing lab data, preparing data for upload to LIMS/ELN systems, or generating parser code for production pipelines.

⬇0 ❤9.9K 14 days ago · Uploaded Detail →
Haaaiawd Haaaiawd
from GitHub Data & AI
Python 数据 文档

nexus-mapper

Generate a persistent .nexus-map/ knowledge base that lets any AI session instantly understand a codebase's architecture, systems, dependencies, and change hotspots. Use when starting work on an unfamiliar repository, onboarding with AI-assisted context, preparing for a major refactoring initiative, or enabling reliable cold-start AI sessions across a team. Produces INDEX.md, systems.md, concept_model.json, git_forensics.md and more. Requires shell execution and Python 3.10+. For ad-hoc file queries or instant impact analysis during active development, use nexus-query instead.

⬇0 ❤53 12 days ago · Uploaded Detail →
buluslan buluslan
from GitHub Tools & Productivity
挥发性有机 自动化 数据

review-analyzer-skill

AI驱动的电商评论深度分析工具，支持22维度智能标签、用户画像识别、VOC洞察和可视化看板生成。 当用户需要以下功能时触发： - 分析电商产品评论（Amazon/eBay/AliExpress等平台） - 从评论中提取用户画像、痛点和VOC（客户之声） - 生成产品洞察报告和机会点分析 - 创建专业的可视化分析看板 - 进行竞品分析和市场定位研究 触发关键词：电商评论分析、评论分析、竞品分析、用户洞察、VOC分析、产品优化、市场调研、评论数据挖掘 AI Agent 约束：必须通过 AskUserQuestion 收集分析数量、AI引擎选择、报告署名后再执行分析

⬇0 ❤36 16 days ago · Uploaded Detail →
bepuca bepuca
from GitHub Data & AI
数据 Python 运维

azureml-scaffolding

Scaffold, structure, and manage AI/ML projects that run on AzureML. Covers project initialization (uv workspaces, devcontainers, Makefile), Python packaging with explicit dependencies, local and cloud execution, experiment reproducibility, and extensibility patterns (pipelines, datasets, linting). Use this skill whenever the user asks to create, modify, run, test, or deploy an AzureML-based ML project — or when they need guidance on project layout, dependency management, or cloud job submission with Azure Machine Learning.

⬇0 ❤35 12 days ago · Uploaded Detail →
joylarkin joylarkin
from GitHub Testing & Security
安全 https 数据

openclaw-security-news

[！[誓言安全](https://img.shields.io/endpoint?url=https%3A%2F%2Faudit-engine.oathe.ai%2Fapi%2Fbadge%2Fjoylarkin%2Fopenclaw-security-news&style=for-the-badge&l ogo=数据：图像/svg%2Bxml;base64,PHN2ZyB4bWxucz0naHR0cDovL3d3dy53My5vcmcvMjAwM C9zdmcnIHZpZXdCb3g9JzAgMCAyNCAyNCcgZmlsbD0nd2hpdGUnPjxwYXRoIGQ9J00xMiAyQzkuMjQ gMiA3IDQuMjQgNyA3djNINmMtMS4xIDAtMiAuOS0yIDJ2OGMwIDEuMS45IDIgMiAyaDEyYzEumSAwIDItLjkgMi0ydi04YzAtMS4xLS45LTItMi0yaC0xVjdjMC0yLjc2LTIuMjQtNS01LTV6bTMgMTBIO VY3YzAtMS42NiAxLjM0LTMgMy0zczMgMS4zNCAzIDN2M3onLz48L3N2Zz4=&labelColor=000000&cacheSeconds=3600)](https://oathe.ai/report/joylarkin/openclaw-security-news)

⬇0 ❤20 16 days ago · Uploaded Detail →
wjt0321 wjt0321
from GitHub Tools & Productivity
自动化 数据 位专家独立

china-stock-analyst

A股短线营收分析助手，聚焦“短线交易信号 + 营收质量”双轨研判。支持8位专家独立分析与交叉质疑（含专家鉴别Agent），输出可复核证据链、双轨评分与明确交易条件。使用时机：(1) 用户询问A股短线机会，(2) 需要结合资金流和营收趋势做快决策，(3) 查询近5日资金与关键位，(4) 验证历史报告与最新数据差异

⬇0 ❤14 12 days ago · Uploaded Detail →
xiaods xiaods
from GitHub Data & AI
数据 金文 宇宙的

kingscript-plugin-dev

金蝶苍穹 Kingscript 插件开发专家。当用户需要为金蝶苍穹/cosmic 平台编写 Kingscript/KS 脚本插件时使用此技能，包括但不限于：操作插件、表单插件、列表插件、转换插件、报表插件、工作流插件、调度任务等。涵盖插件生命周期、ORM 数据访问、BigDecimal 财务计算、界面交互控制、F7过滤、页面弹窗传参、消息通知、DataSet统计报表。即使用户只提到"苍穹插件"、"kingscript"、"KS脚本"、"cosmic插件"、"单据插件"、"保存校验"、"字段联动"、"下推转换"、"单据下推"、"ORM查询"、"F7过滤"、"审批流"、"定时任务"、"入库出库"、"金蝶二开"、"苍穹二开"、"苍穹脚本"、"单据联动"、"列表过滤"、"报表取数"、"金蝶低代码"、"自定义操作"、"单据校验"等关键词，也应触发此技能。

⬇0 ❤11 17 days ago · Uploaded Detail →
aliyun aliyun
from GitHub Tools & Productivity
数据 自动化 分析

dms-data-agent

通过命令行调用阿里云瑶池 Data Agent for Analytics，帮助用户对企业数据库进行自然语言驱动的数据分析。 Data Agent for Analytics 是阿里云瑶池数据库团队推出的面向企业用户的数据分析智能体，可根据自然语言描述自动完成需求分析、数据理解、分析洞察及报告生成。 本工具支持：发现已托管在 DMS 的数据资源（实例/库/表）、发起问数或深度分析会话、实时跟踪执行进度、获取分析结论及生成的报告文件。

⬇0 ❤10 11 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