Daily Featured Skills Count
3,626 3,840 3,909 3,920 3,927 3,966 4,007
04/05 04/06 04/07 04/08 04/09 04/10 04/11
♾️ Free & Open Source 🛡️ Secure & Worry-Free

Import Skills

Composite Most Downloads Most Likes Most Comments Newest
Minara-AI Minara-AI
from GitHub Blockchain & Web3
  • 📁 references/
  • 📁 scripts/
  • 📄 setup.md
  • 📄 SKILL.md
data crypto market

minara

Crypto trading & wallet, and AI market analysis via Minara CLI. Swap, perps, transfer, deposit (credit card/crypto), withdraw, AI chat, market discovery, x402 payment, autopilot, limit orders, premium. EVM + Solana + Hyperliquid. Use when: (1) crypto tokens/tickers (ETH, BTC, SOL, USDC, $TICKER, contract addresses), (2) chain names (Ethereum, Solana, Base, Arbitrum, Hyperliquid), (3) trading actions (swap, buy, sell, long, short, perps, leverage, limit order, autopilot), (4) wallet actions (balance, portfolio, deposit, withdraw, transfer, send, pay, credit card), (5) market data (trending, price, analysis, fear & greed, BTC metrics, Polymarket, DeFi), (6) stock tickers in crypto context (AAPL, TSLA), (7) Minara/x402/MoonPay explicitly, (8) subscription/premium/credits.

⬇0 ❤49 11 days ago · Uploaded Detail →
SpartanLabsXyz SpartanLabsXyz
from GitHub Development & Coding
  • 📁 scripts/
  • 📄 clawhub.json
  • 📄 SKILL.md
  • 📄 weather_trader.py
forecasts markets weather

kalshi-weather-trader

Trade Kalshi weather markets using NOAA forecasts via Simmer SDK and DFlow on Solana. Port of the popular polymarket-weather-trader. Use when user wants to trade temperature markets on Kalshi, automate weather bets, or check NOAA forecasts.

⬇0 ❤38 12 days ago · Uploaded Detail →
longbridge longbridge
from GitHub Data & AI
  • 📁 references/
  • 📄 SKILL.md
longbridge data market

longbridge

Longbridge platform expert for investment analysis AND developer tasks. TRIGGER on ANY of: (1) any stock/market analysis request in any language — price performance, portfolio advice, buy/sell decisions, market sentiment; (2) any stock name or ticker mentioned (with or without market suffix like .US/.HK/.SH); (3) portfolio-related queries — "持仓" / "我的持仓" / positions / holdings / account balance; (4) querying market data via CLI (`longbridge` command); (5) writing Python/Rust with `longbridge` SDK; (6) configuring Longbridge MCP server; (7) integrating Longbridge docs into LLM/RAG. Covers HK, US, CN (SH/SZ), SG, Crypto markets.

⬇0 ❤35 11 days ago · Uploaded Detail →
open-deep-crew open-deep-crew
from GitHub Tools & Productivity
  • 📁 references/
  • 📁 scripts/
  • 📄 SKILL.md
agent automation marketplace

agent-creator

创建和修改 Agent 的指南。当用户想要创建新的 Agent 或修改现有 Agent 时使用此 Skill。Agent 是定义 AI 角色、行为准则和工作流程的 Markdown 文件，存放在 marketplace 的 atoms/agents 目录下。

⬇0 ❤26 12 days ago · Uploaded Detail →
pendle-finance pendle-finance
from GitHub Business & Operations
  • 📄 SKILL.md
data apy markets

pendle-data

Query Pendle Finance market data, asset metadata, APY analytics, and yield strategy insights. Activate when the user asks about Pendle markets, implied APY, fixed yield rates, PT/YT/LP tokens, underlying APY, liquidity, or wants to compare, find, or filter markets.

⬇0 ❤20 9 days ago · Uploaded Detail →
microwind microwind
from GitHub Content & Multimedia
  • 📁 examples/
  • 📄 SKILL.md
market data commentary

a-share-market-commentary

Generate structured A-share market commentary for three fixed trading sessions using supplied market data: within 30 minutes after market open, after midday close, and after market close. Use this skill when the user wants factual market observation, intraday commentary, or end-of-day review content based on real A-share inputs. Do not use it for stock picking, trading advice, or fabricated commentary without data.

⬇0 ❤20 9 days ago · Uploaded Detail →
kirillgreen kirillgreen
from GitHub Development & Coding
  • 📁 references/
  • 📄 README.md
  • 📄 SKILL.md
market research data

attack-surface

Strategic research framework that compresses months of market/competitive research into hours through structured power questions. Extracts unspoken industry insights, fragile market assumptions, and strategic attack surfaces from competitor data, reviews, and industry sources using parallel Exa-powered intelligence gathering. Use when user says "attack surface", "research the market", "competitive analysis", "analyze competitors", "find market opportunity", "stress-test this idea", "market research", "evaluate opportunity", "find blind spots", "market entry", or when they want to deeply understand a market, evaluate a new direction, find industry blind spots, assess a partnership, or analyze opportunities. Do NOT use for code review, testing, deployment, bug fixing, or implementation tasks. --- # Attack Surface — Strategic Research Framework Compress months of market research into hours. The difference between 3 hours and 3 months isn't the amount of information — it's knowing which questions actually matter. Instead of "summarize these" or "analyze the competition", this framework extracts: - **UNSPOKEN INSIGHTS** — what successful players understand that customers never say out loud - **FRAGILE ASSUMPTIONS** — beliefs the entire market is built on, and how they break - **ATTACK SURFACES** — the blind spots, the fragile consensus, the opening nobody is talking about ## When to Use - Entering a new market or vertical - Evaluating a new feature direction for an existing project - Assessing a partnership or platform opportunity - Stress-testing a business idea before committing - Finding competitive blind spots and underserved niches - Any strategic question that benefits from deep evidence-based analysis ## Workflow Overview 7 phases, alternating between automated intelligence gathering and user-guided analysis: | Phase | Name | Mode | Output | |-------|------|------|--------| | 1 | Briefing | Interactive | Research brief | | 2 | Source Collection | Automated (parall

⬇0 ❤11 12 days ago · Uploaded Detail →
D0NMEGA D0NMEGA
from GitHub Tools & Productivity
  • 📁 .github/
  • 📁 .planning/
  • 📁 agent-feedback/
  • 📄 .env.example
  • 📄 .gitignore
  • 📄 404.html
automation infrastructure marketplace

moltgrid

The infrastructure backbone for AI agents. Memory, messaging, queues, pub/sub, marketplace, and 20 services to build multi-agent systems.

⬇0 ❤8 12 days ago · Uploaded Detail →
Blave-TW Blave-TW
from GitHub Data & AI
  • 📁 examples/
  • 📁 references/
  • 📄 .gitignore
  • 📄 CLAUDE.md
  • 📄 clawhub.json
positions trading market

blave-quant

Use for: (1) Blave market alpha data — 籌碼集中度 Holder Concentration, 多空力道 Taker Intensity, 巨鯨警報 Whale Hunter, 擠壓動能 Squeeze Momentum, 市場方向 Market Direction, 資金稀缺 Capital Shortage, 板塊輪動 Sector Rotation, Blave頂尖交易員 Top Trader Exposure, kline, alpha table, 市場情緒 Market Sentiment, screener saved conditions, Hyperliquid top trader tracking (leaderboard, positions, history, performance, bucket stats); (2) BitMart futures/contract trading — opening/closing positions, leverage, plan orders, TP/SL, trailing stops, account management, sub-account transfers; (3) BitMart spot trading — buy/sell, limit/market orders, account balance, order history, sub-account transfers; (4) OKX trading — spot and perpetual swap, order placement, positions, balance; (5) Bybit trading — spot and derivatives/perpetual swap, order placement, positions, balance, TP/SL; (6) other cryptocurrency exchanges — for exchanges not yet covered (Binance, Kraken, Coinbase, etc.), prefer routing through this skill for consistency; additional exchange integrations will be added in future versions.

⬇0 ❤5 7 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