This skill should be used when crafting prompts for Nano Banana Pro (Gemini image generation). Use when users want help writing image generation prompts, need guidance on prompt structure, or want to optimize their prompts for better results.
Use when creating custom, interactive data visualizations with D3.js—building bar/line/scatter charts from scratch, creating network diagrams or geographic maps, binding changing data to visual elements, adding zoom/pan/brush interactions, animating chart transitions, or when chart libraries (Highcharts, Chart.js) don't support your specific visualization design and you need low-level control over data-driven DOM manipulation, scales, shapes, and layouts.
Use this skill when creating, improving, or optimizing prompts for Claude. Applies Anthropic's best practices for prompt engineering including clarity, structure, consistency, hallucination reduction, and security. Useful when users request help with writing prompts, improving existing prompts, reducing errors, increasing consistency, or implementing specific techniques like chain-of-thought, multishot prompting, or XML structuring.
Generate structured proof skeletons with tactics, strategies, and intermediate lemmas for theorems in Isabelle/HOL or Coq. Use when users need to: (1) Create proof outlines for theorem statements, (2) Generate proof structure with tactic placeholders, (3) Identify key lemmas needed for a proof, (4) Plan proof strategies (induction, case analysis, forward/backward reasoning), (5) Scaffold proofs with intermediate steps and subgoals, or (6) Convert theorem statements into detailed proof templates. Supports both Isabelle/HOL and Coq equally.
Draft and render a meta-analysis manuscript with Quarto using an IMRaD structure and embedded figures/tables. Use when preparing the final paper from analysis outputs.
Run statistical meta-analysis in R with renv, generate effect estimates, heterogeneity, and publication bias diagnostics, and export figures and tables. Use when analyzing extracted study data.
Recognize breakthrough moments, blocking resolutions, and design decisions worth preserving. Detect high-value insights that save future time. Suggest distillation at valuable moments, not routine work.
Run evaluations against task apps using Synth AI platform
Builds AI agents on Cloudflare using the Agents SDK with state management, real-time WebSockets, scheduled tasks, tool integration, and chat capabilities. Generates production-ready agent code deployed to Workers. Use when: user wants to "build an agent", "AI agent", "chat agent", "stateful agent", mentions "Agents SDK", needs "real-time AI", "WebSocket AI", or asks about agent "state management", "scheduled tasks", or "tool calling".
Run GEPA prompt optimization using Synth AI platform
Summarize diary entries for a given time period
Optimize prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts.
多AI协作编排技能 - 自动协调Claude、Codex、Gemini完成需求分析→代码生成→代码审查工作流。 【核心触发场景】 - 开发任务、写代码、实现功能、编程任务 - 新功能开发、功能实现、代码实现 - 重构、优化代码、代码改进 - Bug修复、问题修复、错误处理 - 系统设计、架构设计、模块设计 【显式触发词】 - "AI编排"、"ai编排"、"多AI协作"、"三引擎"、"三AI" - "codex"、"gemini"、"kim team"、"kim-team" - "协作开发"、"编排流程" 【任务类型触发】 - 实现XXX功能、开发XXX模块、写一个XXX - 创建XXX、构建XXX、搭建XXX - 添加XXX功能、新增XXX特性 - 修改XXX、更新XXX、升级XXX - 修复XXX、解决XXX问题 - 重构XXX、优化XXX性能 - 设计XXX架构、规划XXX系统 【技术任务触发】 - API开发、接口实现、后端开发、前端开发 - 数据库设计、表结构、CRUD操作 - 用户认证、登录注册、权限控制 - 文件处理、数据导入导出 - 第三方集成、SDK接入
Build LLM applications, RAG systems, and prompt pipelines. Use for LLM features, chatbots, or AI-powered applications.
Implement ML pipelines, model serving, and feature engineering. Use for ML model integration or production deployment.
Dutch volleyball data (Eredivisie, Topdivisie, Superdivisie, and the full Dutch pyramid) via the Nevobo API. Standings, schedules, results, clubs, tournaments, and news. Zero config, no API keys. Use when: user asks about Dutch volleyball, Eredivisie volleyball, Nevobo, volleyball standings, volleyball match results, volleyball schedules, or Dutch volleyball clubs. Don't use when: user asks about other sports — use football-data (soccer), nfl-data (NFL), nba-data (NBA), wnba-data (WNBA), nhl-data (NHL), mlb-data (MLB), golf-data (golf), cfb-data (college football), cbb-data (college basketball), tennis-data (tennis), or fastf1 (F1). For betting odds use polymarket or kalshi. For general news use sports-news.
Synthesize findings from multiple sources into coherent conclusions with uncertainty quantification.
You must use this when merging findings from multiple studies into a coherent narrative with grounded evidence.
