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Import Skills

Composite Most Downloads Most Likes Most Comments Newest
vigorX777 vigorX777
from GitHub Content & Multimedia
  • 📁 assets/
  • 📁 references/
  • 📁 scripts/
  • 📄 .env.example
  • 📄 .gitignore
  • 📄 OPTIMIZATION.md
collect screenshot bitable

content-collector

Collect social media content (X/Twitter, WeChat, Jike, Reddit, etc.) into Feishu bitable. **Use this skill whenever the user shares a link from any social platform, sends a screenshot, or mentions "收藏", "保存", "collect", "save this article".** Even if they don't explicitly ask to collect, trigger this skill proactively. --- # Content Collector Auto-collect social media content → AI summarize → Save to Feishu bitable. ## Quick Reference | Trigger | Action | |---------|--------| | X/Twitter link | `x-tweet-fetcher` skill | | WeChat article | `web-content-fetcher` (Scrapling) | | Other platforms | `defuddle` → fallback to `baoyu-url-to-markdown` | | Screenshot | OCR → extract URL → collect | ## Workflow ``` Link/Screenshot → Platform Detect → Dedupe → Extract → Summarize → Save to Bitable ``` ### Step 1: Platform Detection ```bash python3 scripts/extract_content.py "<url>" ```

⬇0 ❤202 12 days ago · Uploaded Detail →
Feng-H Feng-H
from GitHub Docs & Knowledge
  • 📁 assets/
  • 📁 system/
  • 📄 .gitignore
  • 📄 .npmignore
  • 📄 CLAUDE.md
pdca bitable smart

pdca

AI-driven PDCA project management with Feishu/Lark integration. Use for: project setup (new), active project tracking (ongoing), experience retrieval (achieve), PDCA cycles, SMART goal validation, quality improvement (OEE, defects), manufacturing optimization, or structured problem-solving with Feishu Bitable + docs. Also use for project transitions, proactive AI alerts, or template-based experience reuse. --- # PDCA 项目管理系统 基于 PDCA 循环的结构化问题解决系统，由 AI 驱动实现主动巡检、SMART 目标校验和飞书工具链集成。 ## 何时使用 | 触发症状 | 不适用场景 | |---------|-----------| | 问题需要结构化分析（5W1H、鱼骨图、5Why） | 简单单次任务（直接用任务管理工具） | | 需要量化目标和可衡量指标 | 纯技术研究（无需流程闭环） | | 需要主动进度监控和预警 | 紧急故障处理（先修复，后复盘） | **触发示例**： - "启动一个 PDCA 项目来降低产品缺陷率" - "用飞书 Bitable 管理我们的质量改善项目" - "我需要 SMART 目标校验，目标是将 OEE 提升到 85%" ## 系统依赖 **必需平台**： - **OpenClaw**：AI CLI 框架（https://github.com/open-claw/open-claw） - **飞书插件**：提供 `feishu-bitable`、`feishu-create-doc` 等 API 本 skill 通过这些 API 创建 Bitable 应用、Wiki 文档、任务和日程。 **项目存储位置**：所有项目统一存储在 Wiki 知识空间「PDCA」下。 ## 核心工作流 1. **评估与启动 (new)**：评估问题是否适合立项，在 Wiki 知识空间创建项目文档 + Bitable 应用 + 项目甘特图 2. **计划与校验 (Plan)**：执行 SMART 校验与因果逻辑审查 3. **执行与巡检 (Do)**：AI 通过 Bitable 数据记录主动巡检并汇总进展 4. **检查与评估 (Check)**：分析数据偏差 5. **决策与沉淀 (Act)**：生成标准化 SOP 并归档经验 ## 全局交互规范：AskUserQuestion 选项设计 **适用范围**：PDCA **每个阶段** 中 AI 主动发起的 AskUserQuestion 对话，不限于项目启动阶段。 设计选项时遵循三大原则： ### 1. MECE 原则 — 基于框架设计选项 选项必须"相互独立、完全穷尽"。根据当前对话的问题类型，选择对应的 MECE 框架，用其维度作为选项基础： | 问题类型 → 框架 | |------| | 生产/制造 → 4M1E | | 个人健康 → TREND | | 软件/技术 → PPTD | | 销售/营销 → 5P | | 学习/教育 → COMET | | 财务/投资 → 3RL-TD | | 团队协作 → GRCT | | 客户服务 → 5S | | 个人效率 → TIME | | 流程/服务 → SIPOC | | 其他管理/组织 → 5P2E | 每个框架的逐维度详细说明见 [mece-frameworks.md](assets/references/mece-frameworks.md)。 ### 2. 多选优先 — 原因/因素往往不止一个 问题涉及"哪些方面"、"什么原因"、"什么因素"时，使用 `multiSelect: true`。 ### 3. 允许自定义 — 必须有 Other 选项 你无法预先覆盖所有情况，所有问题必须包含 "Other" 选项。 ### AskUserQuestion 模板 ```yaml

⬇0 ❤6 8 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