Daily Featured Skills Count
4,524 4,564 4,605 4,651 4,689 4,727 4,738
04/24 04/25 04/26 04/27 04/28 04/29 04/30
♾️ Free & Open Source 🛡️ Secure & Worry-Free

Import Skills

Composite Most Downloads Most Likes Most Comments Newest
alter123-zz alter123-zz
from GitHub Tools & Productivity
  • 📁 references/
  • 📄 _meta.json
  • 📄 SKILL.md
agent personality automation

agent-mbti

AI Agent personality diagnosis and configuration system based on MBTI framework. Use when users want to (1) test/diagnose an Agent's personality type, (2) understand the gap between Agent's actual personality and user's desired personality, (3) generate configuration recommendations to adjust Agent behavior, (4) customize Agent's communication style, proactivity, reasoning approach, or execution patterns. Supports both free tier (quick assessment) and premium tier (full 93-question assessment with detailed diagnostics).

⬇0 ❤9 26 days ago · Uploaded Detail →
marklubin marklubin
from GitHub Tools & Productivity
  • 📄 SKILL.md
when asks user

memory

Use when the user references prior work, asks what happened recently, needs context about the project or person, or when you should store notes from a call, meeting, or important conversation. Also activate when the user says "remember this" or asks you to search memory.

⬇0 ❤9 26 days ago · Uploaded Detail →
micooz micooz
from GitHub Tools & Productivity
  • 📁 docs/
  • 📄 SKILL.md
requirement automation plans

batch-plan-execute

Use when the user wants AI to turn a requirement text, requirement document, and existing reviewed plan files into dependency-ordered implementation plans, a consolidated `checklist.md`, and, only after an explicit execution command, implement those plans with subagents in parallel where safe and in sequence where required.

⬇0 ❤9 27 days ago · Uploaded Detail →
AtelLab AtelLab
from GitHub Tools & Productivity
  • 📁 atel-agent/
  • 📁 references/
  • 📁 scripts/
  • 📄 SKILL.md
automation atel registration

atel-agent-suite

Complete ATEL skill — from zero to earning USDC. One file covers everything: install, identity, wallet, registration, free/paid orders, milestones, escrow, disputes, balance. An AI agent reads this once and can fully operate on ATEL.

⬇0 ❤9 27 days ago · Uploaded Detail →
AgoraIO AgoraIO
from GitHub Tools & Productivity
  • 📁 intake/
  • 📁 references/
  • 📄 SKILL.md
agora frontend automation

agora

Write code and integration guidance using Agora products. Covers RTC (video/voice, live streaming, screen sharing), RTM/signaling, Conversational AI voice agents, Agora CLI project workflows, Cloud Recording, Server Gateway, and token generation. Use for Agora, RTC, RTM, ConvoAI, `agora` CLI, `agora login`, `agora project create`, `agora project env`, `agora project doctor`, video calling, voice calling, screen sharing, recording, tokens, signaling, or ConvoAI requests across Web, React, Next.js, iOS, Android, Go, and Python. RULES: Use only skill reference files for Agora integration — do not web search for Agora docs. For ConvoAI demos or prototypes, always clone the official sample first — do not implement from scratch.

⬇0 ❤9 27 days ago · Uploaded Detail →
royeedai royeedai
from GitHub Tools & Productivity
  • 📄 SKILL.md
不通过 parity-gate readlints

acceptance-gate

当需要定义或更新验收门禁时，使用本 Skill 管理设计确认门、逻辑确认门、实现质量门、交付质量门和 parity-gate。 在任何完成声明之前强制执行验证铁律：没有新鲜证据，不得宣称完成。 --- # 验收门管理器 ## 验证铁律 ``` 没有在当前步骤运行验证命令并看到输出，不得宣称任何完成状态 ``` 这条铁律适用于所有完成声明、满意表达和正向状态描述。 ### 5 步验证门 在宣称任何完成状态之前，必须依次执行： 1. **IDENTIFY**：什么命令或证据能证明这个声明？ 2. **RUN**：执行完整命令（新鲜的、完整的，不是上一次的结果） 3. **READ**：完整阅读输出，检查退出码，数清失败数 4. **VERIFY**：输出是否确认了声明？ - 如果否 → 陈述实际状态和证据 - 如果是 → 带着证据陈述声明 5. **CLAIM**：只有在以上步骤全部完成后，才能做出声明 跳过任何一步 = 不诚实，不是高效。 ### 禁止措辞 以下措辞在没有对应验证证据时绝对禁止使用： - "应该可以了" - "应该没问题" - "大概通过了" - "看起来正确" - "基本完成了" - "应该能用了" - "问题应该解决了" - 任何包含"应该""大概""看起来""基本"的完成性表述 ### 常见完成声明的证据要求 | 声明 | 需要的证据 | 不够的证据 | |------|-----------|-----------| | 测试通过 | 测试命令输出：0 failures | 上一次运行结果、"应该通过" | | 构建成功 | 构建命令：exit 0 | Lint 通过、"看起来没问题" | | Bug 已修复 | 复现原始症状的测试通过 | 代码改了、"应该修好了" | | 需求已满足 | 逐条对照检查清单 | 测试通过（测试不等于需求） | | 子代理已完成 | 检查 VCS diff 确认变更 | 子代理报告"成功" | | 静态校验通过 | 项目构建命令（mvn compile / tsc / pnpm build 等）exit 0 | IDE ReadLints "无错误"、编辑器无标红 | | 无回归 | 全量测试通过 | 部分测试通过 | ### 自我合理化防御 | 借口 | 现实 | |------|------| | "应该能用了" | 运行验证命令 | | "我很有信心" | 信心不是证据 | | "就这一次" | 没有例外 | | "Lint 通过了" / "ReadLints 无错误" | IDE 诊断不等于编译、不等于测试；必须用项目构建工具链验证 | | "子代理说成功了" | 独立验证 | | "已经很晚了、很累了" | 疲劳不是借口 | | "部分检查够了" | 部分证据什么也证明不了 | ## 使用时机 - `/plan` 生成 acceptance - `/verify` 更新门禁状态 - `/ship` 前检查是否真的可以交付 - **任何即将宣称"完成"的时刻** ## 使用方式 1. 生成或更新 `.ai-os/acceptance.yaml` 2. 根据 `../../references/risk-triggers.md` 判断当前 `quality_tier`，并在高风险档写入 `required_special_reviews` 3. 确保存在以下门禁： - `design-confirmation` - `logic-confirmation` - `implementation-quality` - `delivery-readiness` - `parity-gate`（reverse-spec 适用） 4. 为每个门禁绑定检查项和证据，至少覆盖契约基准一致性、项目原生静态校验、人工动作显式化和 degraded-path 证据 5. 发现门未通过时，禁止宣称完成 ## 质量判断原则 - 页面像但逻辑错：不通过 - 逻辑对但体验和 IA 明显偏：不通过 - 关键技术栈、目标运行态或不可逆方案未确认：相关门禁不通过 - 只有入口、占位态或 demo，关键能力并不真实可用：不通过 - 未实现或未验证的能力被写成已完成：不通过 - 只有正常路径证据，没有异常 / 空数据 / 权限拒绝 / 超时证据：不通过 - fallback 结果充当正式交付：不通过 - 没有项目原生静态校验证据，且也没有显式记录仓库缺少入口：不通过 - 仍需人工执行 SQL / 重启 / 迁移 / 补数 / 环境变更，却没有在交付说明中显式列出：不通过 ## 危险信号 —— 立即停下 如果你正准备： - 使用上方禁止措辞中的任何表述 - 在跑命令前表达满意 - 准备提交 / 推送 / 创

⬇0 ❤9 28 days ago · Uploaded Detail →
MarkShawn2020 MarkShawn2020
from GitHub Tools & Productivity
  • 📁 examples/
  • 📄 SKILL.md
ai code full-featured

lovcode-cli

We hacked Claude Code open-source, debugged it from scratch, and made it actually work. Full-featured AI coding CLI — your code, your keys, your rules.

⬇0 ❤9 28 days ago · Uploaded Detail →
netresearch netresearch
from GitHub Tools & Productivity
  • 📄 SKILL.md
docs ai knowledge

coach

Use when Claude makes repeated mistakes, users correct behavior, tool failures indicate missing knowledge, or managing learning via /coach commands.

⬇0 ❤9 28 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