703 件 ・ 関連度順
概要と使いどころ
DuckDB is an in-process analytical database that runs embedded inside your application with zero external dependencies. It can query CSV, Parquet, and JSON files directly without loading them into tables first, making it ideal for local data exploration, ETL pipelines, and analytical workloads where spinning up a server is overkill.
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You are an expert in Cline, the autonomous AI coding agent for VS Code that can read files, write code, run terminal commands, and use the browser — with human-in-the-loop approval at each step. You help developers use Cline for complex multi-file refactoring, feature implementation, debugging, and codebase exploration where the AI plans and executes while the developer reviews and approves.
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ClickHouse is a columnar OLAP database built for real-time analytical queries over massive datasets. It supports a familiar SQL interface with powerful aggregation functions, materialized views for pre-computed rollups, and high-throughput inserts that make it ideal for event tracking, time-series analytics, and ad-hoc exploration of billions of rows.
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Node-based graphical interface for Stable Diffusion workflows. Build complex image generation pipelines by connecting nodes visually. Supports custom nodes, ControlNet, LoRA, upscaling, and advanced workflows with full control over the diffusion process.
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Expert in creating rich backstories, fictional universes, and ARGs (Alternate Reality Games). Covers world-building, mystery construction, community-driven storytelling, and transmedia narratives. Knows how to create depth that rewards exploration without overwhelming. Use when "lore, world building, backstory, ARG, mythology, fictional universe, hidden story, deep canon, " mentioned.
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The craft of weaving story into interactive systems where the player is both audience and author. This skill covers the unique challenges of game narrative: branching structures that respect player agency, environmental storytelling that rewards exploration, dialogue systems that feel alive, and the delicate balance between authored experience and emergent narrative. Unlike linear media, games don't tell stories TO players - they create the conditions for stories to emerge WITH players. The narrative designer's job is to build the scaffolding that supports infinite player-authored moments while maintaining coherent themes and emotional arcs. This skill synthesizes lessons from Disco Elysium's internal monologue as gameplay, Hades' death-as-narrative-engine, interactive fiction's branching mastery (Inkle, Twine), and procedural narrative pioneers like Dwarf Fortress. It addresses ludonarrative harmony, bark systems, cinematics that respect agency, and the "and therefore" test that separates plot from story. Use when "narrative design, game story, game writing, dialogue system, branching narrative, player choice, environmental storytelling, bark system, quest design, lore delivery, ludonarrative, interactive fiction, inkle, twine, dialogue tree, player agency, cinematic design, voice direction, localization, procedural narrative, narrative, game-writing, dialogue, branching, player-agency, environmental-storytelling, quest-design, voice-acting, localization, interactive-fiction, procedural-narrative, ludonarrative" mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
World-class level design expertise - spatial storytelling, player flow, blockout methodology, and the invisible hand that guides players without them knowingUse when "level design, blockout, graybox, whitebox, player flow, level layout, combat arena, exploration space, map design, multiplayer map, pacing, weenie, landmark, gating, metroidvania, open world design, linear level, environmental storytelling, teaching through design, playtesting, heatmap, metrics, sightlines, level-design, game-development, spatial-design, blockout, graybox, player-flow, pacing, environmental-storytelling, combat-design, multiplayer-maps, open-world, linear-design, metroidvania" mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
Expert in entertainment industry concept art workflows, from initial ideation through final production-ready deliverables. Combines traditional art fundamentals with modern digital workflows used at AAA studios and film production houses. Draws from methodologies of industry masters: Feng Zhu's efficient design process, Scott Robertson's perspective and vehicle design systems, Sparth's graphic shape language, and Syd Mead's industrial design thinking. Use when "concept art, visual development, vis dev, keyframe, color script, production design, mood board, thumbnail sketch, environment concept, character concept, prop design, callout sheet, orthographic views, design exploration, photobash, speedpaint, concept-art, visual-development, game-art, film-art, pre-production, ideation, design" mentioned.
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Master AI-powered game asset pipelines using ComfyUI, Stable Diffusion, FLUX, ControlNet, and IP-Adapter. Creates production-ready sprites, textures, UI, and environments with consistency, proper licensing, and game engine integration. Use when "AI game art, generate game assets, ComfyUI game, stable diffusion sprites, AI texture generation, character consistency AI, procedural art generation, SDXL game assets, FLUX textures, train LoRA game, AI tileable texture, spritesheet generation, " mentioned.
日本語の概要は準備中です。原文の説明を表示しています。
Create polished HTML slide decks and PDF-ready documents for consulting deliverables. Uses the RRBC design system with warm light mode, dark mode cover pages, Lora/Inter/Roboto Mono typography, and data visualization palette. Trigger on 'deck', 'slides', 'presentation', 'pitch deck', 'keynote', 'report', or 'PDF'.
日本語の概要は準備中です。原文の説明を表示しています。
游戏设计 (Game Design) — 设计游戏的规则 / 系统 / 机制 / 体验 的从业者认知操作系统 (以电子游戏为主, 含桌游 / 卡牌 / 棋盘游戏): 决定「玩家做什么、为什么有意义、每一刻的体验如何」的学科 —— 区别于游戏程序 (引擎 / 代码)、游戏美术 (视觉 / 音频)、游戏制作 (排期 / 预算 / 团队)。覆盖 (a) 核心设计理论与框架 (MDA 机制-动态-美学 Hunicke/LeBlanc/Zubek + Schell 的「透镜」+ Koster「乐趣即学习」理论 + Sid Meier「一连串有趣的决策」+ Salen & Zimmerman「有意义的游玩」/「二阶设计」/「魔法圈」+ Costikyan 论不确定性 + Suits/Huizinga 游戏哲学), (b) 系统与机制设计 (核心循环 + 游戏经济 sink/source/产出-消耗 + 正负反馈环 + 资源与成长系统 + 涌现 vs 脚本 Will Wright「可能性空间」+ Machinations 经济建模 Dormans + 支配 / 退化策略 + 正交设计 Garfield), (c) 关卡与遭遇设计 (灰盒 blockout + 节奏与张力 + 预告 telegraphing + 主线 vs 探索 + 环境叙事 + 锁钥 gating + 难度曲线), (d) 游戏手感与「juice」(Steve Swink《Game Feel》+ 即时响应 + 屏震 / 反馈 / 打磨 + 输入与镜头 + 「手感」是设计出来的不是附带的), (e) 玩家心理与 UX (Celia Hodent《The Gamer's Brain》+ 心流通道 挑战 vs 技能 Csikszentmihalyi/陈星汉 Jenova Chen + Bartle 玩家类型 + 动机与奖励节奏 + 新手引导 FTUE / 教学化 + 认知负荷 + 无障碍), (f) 平衡与调优 (Ian Schreiber《Game Balance》+ 对称 / 非对称 + 动态难度 DDA + 传递 / 非传递平衡 + nerf/buff + 消除支配策略 + 测试驱动调优), (g) 叙事与任务设计 (ludonarrative 失调 / 和谐 + 环境与系统叙事 + 分支 vs 线性 + Amy Hennig 角色驱动 + 任务结构), (h) 以玩家为中心的迭代流程 (Tracy Fullerton《Game Design Workshop》playcentric + 纸面原型 + 垂直切片 + 测试协议「看而不说 / 你不是你的玩家」+ 快速迭代 + game jam), (i) 类型与形态工艺 (roguelike/roguelite 与程序生成 Derek Yu + 平台跳跃 + RPG 系统 + 策略 4X + 解谜设计 Jonathan Blow + 多人 / PvP 平衡 + 桌游 / 卡牌设计 Reiner Knizia/Richard Garfield/Brenda Romero), (j) F2P / 长线运营 / 商业化设计 (核心 / 元 / 经济循环 + 抽卡 gacha / 开箱 loot box / 战令 battle pass 机制 + 留存 D1/D7/D30 + 转化 + 鲸鱼 + live ops 内容节奏 —— 并诚实处理掠夺式暗黑模式 / 强迫循环 / 剥削式商业化的伦理), (k) 设计师角色与工艺职业 (系统 / 关卡 / 叙事 / UX 设计师之分 + 通才 vs 专才 + 独立作者 vs 3A 团队 + 设计文档 + 传达设计意图 + GDC 工艺社区)。跨 3A / 独立 / 手游 F2P / 桌游。不含 游戏程序 / 引擎 (Unity/Unreal 编码 / 图形 / 网络同步) / 游戏美术 / 动画 / 音频 / 游戏制作 / 项目管理 / 排期 / QA / 游戏作为生意 / 发行 / 市场 / 电竞运营为终点 / 泛泛「我想做游戏」的 game-dev 教程 —— 本 skill 是「设计」学科: 规则 / 系统 / 机制 / 体验。 (Game Design — the cognitive operating system of practitioners who design the rules, systems, mechanics, and experiences of games (video games primarily, plus tabletop/board/card games): the discipline of deciding WHAT the player does, WHY it is meaningful, and HOW the moment-to-moment experience feels — distinct from game programming (builds the engine/code), game art (visuals/audio), and game production (schedule/budget/team). Covers (a) core design theory & frameworks (MDA Mechanics-Dynamics-Aesthetics Hunicke/LeBlanc/Zubek, Schell's Lenses, Koster's theory of fun = learning, Sid Meier 'a series of interesting decisions', Salen & Zimmerman 'meaningful play'/'second-order design'/'magic circle', Costikyan on uncertainty, Suits/Huizinga play philosophy), (b) systems & mechanics design (core loops, game economy sink/source/faucet-drain, feedback loops positive/negative, resource & progression systems, emergence vs scripting Will Wright 'possibility space', Machinations economy modeling Dormans, dominant/degenerate strategy, orthogonal design Garfield), (c) level & encounter design (blockout/greybox, pacing & tension, telegraphing, critical path vs exploration, environmental storytelling, gating & lock-and-key, difficulty curve), (d) game feel & juice (Steve Swink 'Game Feel', moment-to-moment responsiveness, screenshake/feedback/polish, input & camera, the 'feel' is designed not incidental), (e) player psychology & UX (Celia Hodent 'The Gamer's Brain', flow channel challenge-vs-skill Csikszentmihalyi/Jenova Chen, Bartle player types, motivation & reward schedules, onboarding/FTUE/tutorialization, cognitive load, accessibility), (f) balancing & tuning (Ian Schreiber 'Game Balance', symmetry/asymmetry, dynamic difficulty adjustment DDA, transitive/intransitive balance, nerf/buff, dominant strategy elimination, playtesting-driven tuning), (g) narrative & quest design (ludonarrative dissonance/harmony, environmental & systemic storytelling, branching vs linear, Amy Hennig character-driven, quest structure), (h) player-centric iterative process (Tracy Fullerton 'Game Design Workshop' playcentric design, paper prototyping, vertical slice, playtesting protocol 'watch don't talk / you are not your play
日本語の概要は準備中です。原文の説明を表示しています。
Exhaustive problem space exploration using the MIT Synthetic Neurobiology "tiling tree" method. Partitions a problem into MECE (Mutually Exclusive, Collectively Exhaustive) subsets recursively via parallel subagents, then evaluates leaf ideas against specified criteria. Use when users say "tiling tree", "tile the solution space", "exhaustively explore approaches to", "what are all the ways to", or request a MECE breakdown of a problem. Requires orchestrating-chat-agents skill.
日本語の概要は準備中です。原文の説明を表示しています。
Select the right Python charting library (seaborn, matplotlib, graphviz) and produce publication-quality static visualizations. Use when creating charts, plots, graphs, diagrams, heatmaps, visualizations from data, or when choosing between matplotlib/seaborn/graphviz. Also triggers for network diagrams, flowcharts, dependency trees, state machines, and entity-relationship diagrams. For interactive browser-rendered charts or uploaded data exploration, defer to charting-vega-lite instead.
日本語の概要は準備中です。原文の説明を表示しています。
Exploratory data analysis. Use when users upload .csv/.xlsx/.json/.parquet files or request "explore data", "analyze dataset", "EDA", "profile data". Small files get ydata-profiling HTML/JSON reports; large files (over 200MB or 5M rows) get fixed-memory DuckDB/sketch profiling. Also covers near-duplicate row detection, cross-file key overlap ("can these join?"), dataset drift vs a stored baseline, and time-series profiling.
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Exploratory spike on a throwaway branch — answer a named question with disposable code. Never merges; exits to a findings note or /ca-feature.
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Exploratory spike on a throwaway branch — answer a named question with disposable code. Never merges; exits to a findings note or $ca-feature.
日本語の概要は準備中です。原文の説明を表示しています。
Route HugeGraph MCP structured graph query tasks to stable read-only tools. Use when the user asks graph questions, asks to generate Gremlin, provides Gremlin to explain, or needs read-only graph exploration.
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Create a structured user interview script with warm-up, core exploration, and wrap-up sections. Use when preparing for user research interviews to ensure consistent, insightful conversations.
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Builds end-to-end predictive modeling pipelines on tabular datasets—data exploration, preprocessing, baseline modeling, and mandatory optimization—producing valid saved predictions in the required output format. Use for any supervised machine learning or data modeling task, including classification and regression on tabular data, Kaggle-style competitions or submission-file challenges, training a model to predict a target from features, or requests to fit/tune/evaluate models with scikit-learn, XGBoost, Random Forest, Logistic Regression, and similar non-deep-learning methods. Runs fully automated end-to-end without asking the user for intermediate decisions.
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Draft a reply or a value-add quote tweet for a specific X (Twitter) tweet from its URL. Use to reply in a thread, answer a creator, or quote-tweet with added value. Parses the tweet URL, reads pasted context, decides reply vs quote tweet, and drafts 1-3 variants in your voice. X has no LinkedIn-style 2-level thread flattening, so a reply is just a tweet. Publora has no reply endpoint, so the draft is returned as a copy-paste block to post yourself. Not for top-level posts (use x-post-writer).
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Repurpose existing content into a native X (Twitter) post or thread. Take a LinkedIn post, blog, YouTube script, or newsletter and rewrite it for X: re-hook for the no-fold first line, refit to 280 chars or a thread, strip off-platform artifacts, run the humanizer, publish via Publora on approval. Not for writing from scratch (use x-post-writer or x-thread-builder), not for auditing a draft (use x-humanizer --mode audit).
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Draft a single tweet or short auto-thread for X (Twitter) using a 2026 X hook formula (one-liner contrarian, data-point, build-in-public, mini-list, relatable cold-open), picked by goal (replies, reposts, likes, bookmarks). Respects the 280-char limit (25,000 on Premium), runs the humanizer pass, and publishes via Publora on approval. Use to write a tweet from notes. Not for long threads (use x-thread-builder) or auditing a draft (use x-humanizer --mode audit).
日本語の概要は準備中です。原文の説明を表示しています。
Build a long-form X (Twitter) thread (tweetstorm) using a 2026 thread formula (listicle-thread, story thread, curiosity-gap opener, how-I teardown), picked by goal (bookmarks, likes, reposts, replies). Structures tweet 1 as a promise plus open loop, paces one beat per tweet, and closes for the repost. Publishes via Publora, which auto-splits long content into a numbered (1/N) thread. Not for single tweets (use x-post-writer) or auditing a draft (use x-humanizer --mode audit).
日本語の概要は準備中です。原文の説明を表示しています。
Plan, draft, audit, and publish posts and threads for X (Twitter). Use when the user wants to write a single tweet or an auto-numbered thread, build a long-form tweetstorm, remove AI tells from a draft, reverse-engineer the hook from a viral tweet, draft a reply or quote tweet, or plan a week of X content. Tweets and threads publish via the Publora API, which auto-splits long content into a numbered thread. User provides notes or a tweet URL, the skill drafts, the user approves, then it publishes.
日本語の概要は準備中です。原文の説明を表示しています。