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Import Skills

Composite Most Downloads Most Likes Most Comments Newest
tlancas25 tlancas25
from GitHub Tools & Productivity
  • 📁 docs/
  • 📁 examples/
  • 📁 scripts/
  • 📄 .gitignore
  • 📄 install.py
  • 📄 LICENSE
automation execution data

context-saver

Token-saving execution layer for OpenClaw v2.0. Runs skill commands in sandboxed subprocesses where only compact summaries enter the context window. Provides session continuity via SQLite event tracking that survives conversation compaction. Supports intent-driven filtering, batched multi-skill execution, and progressive memory loading. Includes automated installer that wires context-saver into AGENTS.md, TOOLS.md, and cron jobs with a single command. Use this skill to wrap any data-heavy operation to reduce token consumption by 70-98%.

⬇0 ❤10 13 days ago · Uploaded Detail →
raddue raddue
from GitHub Content & Multimedia
  • 📄 break-it-prompt.md
  • 📄 SKILL.md
implementation failure unknown

adversarial-tester

Use after completing implementation to find unknown failure modes. Reads implementation diff and writes up to 5 tests designed to make it break. Triggers on 'break it', 'adversarial test', 'stress test implementation', 'find weaknesses', or any task seeking to expose unknown failure modes.

⬇0 ❤10 13 days ago · Uploaded Detail →
EasyCode-Obsidian EasyCode-Obsidian
from GitHub Tools & Productivity
  • 📁 agents/
  • 📁 assets/
  • 📄 LICENSE
  • 📄 README.md
  • 📄 SKILL.md
multi-phase execution user

requirement-to-delivery

Use this skill when the user wants to turn a requirement — from a small bug fix to a multi-phase project — into tracked execution. The skill automatically classifies the task as quick-fix, single-phase, or multi-phase and selects the appropriate execution depth without user intervention.

⬇0 ❤8 9 days ago · Uploaded Detail →
franktsai2008-eng franktsai2008-eng
from GitHub Tools & Productivity
  • 📁 actions/
  • 📁 prompts/
  • 📁 scorers/
  • 📄 .gitignore
  • 📄 config.yaml
  • 📄 install.sh
skill-update-team 執行 null

skill-update-team

Skill Update Team — 自動化 AI 工具研究與安裝 Agent。 掃描 GitHub、Reddit、X/Twitter、YouTube、Anthropic changelog，發現新的 MCP servers、 Claude Code plugins、AI 工具，經安全檢查後推薦安裝。 Plugin 架構：sources / scorers / actions 均可獨立擴展。 觸發方式：使用 /skill-update-team 指令觸發（如 "/skill-update-team"、"/skill-update-team approve <id>"） --- # Skill Update Team (SUT) 你是 SUT Orchestrator。收到使用者指令後，調度 subagent 完成工具掃描、安全審查、安裝。 **SUT_HOME**: `~/skill-update-team` ## 指令對照 | 使用者說 | 動作 | |---------|------| | `/skill-update-team` | → 執行 **SCAN** + **REPORT** | | `/skill-update-team report` | → 執行 **REPORT**（僅顯示上次報告） | | `/skill-update-team check <id>` | → 執行 **SECURITY AUDIT** | | `/skill-update-team approve <id>` | → 執行 **APPROVE** | | `/skill-update-team reject <id>` | → 執行 **REJECT** | | `/skill-update-team defer <id>` | → 執行 **DEFER** | | `/skill-update-team rollback` | → 執行 **ROLLBACK** | | `/skill-update-team health` | → 執行 **HEALTH CHECK** | | `/skill-update-team trust <source/type>` | → 加入 auto-trust 清單 | | `/skill-update-team untrust <source/type>` | → 移除 auto-trust | | `/skill-update-team adjust-weights` | → 手動調整 scorer 權重 | | `/skill-update-team adopt-meta <id>` | → 採納 meta discovery，自動生成 YAML plugin | --- ## SCAN 流程 ### Step 1: 準備 context 用 Bash / Read / Glob 收集以下資訊： ```bash # 日期 TODAY=$(date +%Y-%m-%d) SEVEN_DAYS_AGO=$(date -v-7d +%Y-%m-%d) THIRTY_DAYS_AGO=$(date -v-30d +%Y-%m-%d) # 已安裝的 MCP claude mcp list 2>/dev/null | grep "✓ Connected" # 偏好歷史（最近 20 筆） tail -20 ~/skill-update-team/state/preferences.jsonl 2>/dev/null # 已安裝的 skills（列出 ~/.claude/skills/ 下的目錄） ls ~/.claude/skills/ 2>/dev/null # Tech stack detection OS_VERSION=$(sw_vers -productVersion 2>/dev/null || echo "unknown") NODE_VERSION=$(node --version 2>/dev/null || echo "not installed") PYTHON_VERSION=$(python3 --version 2>/dev/null | awk '{print $2}' || echo "not installed") CLAUDE_VERSION=$(claude --version 2>/dev/null || echo "unknown") TECH_STACK="macOS $OS_VERSION / Node $NODE_VERSION / Python $PYTHON_VERSION / Claude Code $CLAUDE_VERSION" # 偵測 Fi

⬇0 ❤7 7 days ago · Uploaded Detail →
dnviti dnviti
from GitHub Development & Coding
  • 📄 SKILL.md
docs implementation announcement

crazy

[BETA] Fully autonomous end-to-end project builder. Takes a project description and orchestrates the entire CodeClaw pipeline: ideas, tasks, releases, implementation, docs, and social announcement.

⬇0 ❤9 13 days ago · Uploaded Detail →
c9r-io c9r-io
from GitHub Development & Coding
  • 📁 agents/
  • 📄 SKILL.md
tests after implementation

align-tests

Execute unit-test alignment after large refactors or broad code changes. Trigger when the user explicitly uses the command "-- 对齐测试 --" or asks to align/fix tests after massive modifications. Run relevant unit tests, analyze failing test cases, update tests and/or implementation, and iterate until stable.

⬇0 ❤9 13 days ago · Uploaded Detail →
kweaver-ai kweaver-ai
from GitHub Development & Coding
  • 📄 SKILL.md
implementation collaborative fully-formed

brainstorming

Use when creating or developing, before writing code or implementation plans - refines rough ideas into fully-formed designs through collaborative questioning, alternative exploration, and incremental validation. Don't use during clear 'mechanical' processes

⬇0 ❤7 10 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