发现最适合你需求的 AI 技能
|
|
Autonomous AI coding pipeline that breaks any task into a structured Spec → Plan → Code → QA loop and executes it via Claude Code. Use when: (1) starting any new feature, refactor, or bug fix, (2) given a GitHub issue to implement, (3) asked to "run FlowForge", "forge this", "plan and build", or "auto-implement". Routes ALL heavy work through Claude Code. Supports multi-account rotation to handle rate limits automatically.
>
Create or revise standalone HTML/SVG architecture diagrams, runtime flow diagrams, sequence diagrams, and PPT-like technical visuals. Use when a user wants human-readable publication-ready diagrams instead of Mermaid, or wants help improving node layout, arrow accuracy, spacing, overlap handling, Chinese labels, or overall visual style for architecture and flow charts.
Trustless token swaps for AI agents on Base. Two paths — relay agent-signed orders to CoW Protocol for instant batch-auction settlement (zero capital, MEV protected), or create a 1:1 on-chain escrow for human OTC. Both atomic, no middleman.
|
flight price monitor, airfare price alert, fare tracking, cheap flight China, round-trip one-way, price drop notification, scheduled flight search, FlyAI CLI, search-flight, bookable links.
Generate concise, user-friendly flight price summaries with buy/wait recommendations. Requires SerpAPI key for real-time price data. Use when users ask about flight prices, purchase timing, or price trends. Designed for C-end chat interfaces.
Check airline baggage compliance, carry-on vs checked rules, excess baggage, and the cheapest compliant packing plan. Use for questions about overweight luggage, extra baggage allowance, power banks, lithium batteries, liquids, aerosols, drones, camera gear, sports equipment, and whether an item can be taken on a flight.
智能行程规划师，整合航班、酒店、景点数据，一键生成完整旅行方案。当用户需要规划旅行行程、制定旅游计划、安排度假方案或询问"去哪里玩""怎么安排行程"时使用此 skill。
Embeddings with nomic-embed-text, mxbai-embed, and snowflake-arctic-embed across your device fleet. Fleet-routed via Ollama for RAG, semantic search, and vector similarity. Batch embed thousands of documents across nodes instead of bottlenecking on one machine. Use when the user needs to create embeddings, build a knowledge base, or set up semantic search.
