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概要と使いどころ
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
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。
Draws the 12 Houses of the Zodiac Tarot spread to inject entropy into planning when prompts are vague, ambiguous, or casually delegated. Interprets the spread to guide next steps. Use when the user says 'let fate decide', 'YOLO', 'whatever', 'idk', or other nonchalant phrases, makes Yu-Gi-Oh references, or when you are about to arbitrarily pick between multiple reasonable approaches. Prefer over asking clarifying questions when the user's tone is casual or playful rather than precision-seeking.
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。
**MANDATORY prerequisite** — you MUST invoke this skill BEFORE calling the `get_design_context` Figma MCP tool. You MUST trigger this skill whenever the user wants to implement, build, port, or code up a Figma design as code. Example prompts (not exhaustive) are 'implement this Figma design', 'build this screen from Figma', 'turn this Figma into code', 'design to code'. This skill provides critical instructions and steps to the agent on how to correctly implement Figma designs in code and must NOT be skipped.
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。
Choose, lay out, critique, and explain data visualizations. Use when the user asks what visualization fits a dataset or goal, how a chart, dashboard, operational workspace, UML-like diagram, or software architecture diagram should be composed or interacted with, asks for visual page design, a layout mockup, generated large-screen and mobile concept images, or to be shown what a visualization could look like, when domain-native contextual surfaces or graphical backgrounds may help, when scrollytelling or parallax might be appropriate, wants a critique of an existing visualization, or needs guidance grounded in trusted visualization theory and practice. For advanced visual design or page-layout prompts where composition affects understanding, Codex must generate and show both large-screen and mobile portrait image concepts before implementation or text-only design handoff, plus mobile landscape when needed.
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。
Use when you have just been primed into an EXISTING seat through a planned handover — a different agent retired and handed you the seat plus its earned context — and you need a world model of what just happened to you. Covers how a handover differs from compaction and from a fresh launch, the inherit-the-seat-not-the-identity rule, treating the handover packet as testimony you verify (and refusing stale ghost prompts, and verifying your OWN identity surfaces), why a seat is a COMPOUNDING LINEAGE (inherit the state, acquire the predecessor's epistemology, then improve and re-deposit so the next generation is better), how to query your predecessor, your duty to stream feedback and re-deposit your earned wisdom, and how to route to role-specific onboarding. NOT the driver-side handover mechanic (retiring-and-inheriting-a-seat) and NOT unplanned compaction/crash recovery (claude-compaction-restore / session-compaction-and-restore).
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。
Use before launching a team during agent-guided setup, or when a user asks to configure OpenRig command permissions, reduce repeated native approval prompts, or apply a selected rig/seat permission policy.
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。
Generate visual assets from text prompts: PNG images (Gemini / xAI Grok, or local Qwen-Image when installed), GLB 3D models (Tripo CLI), rigged characters, retargeted animations, and frame-by-frame animated sprites, plus background removal. Use whenever a game needs generated art.
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。
Address review and issue comments on the open GitHub PR for the current branch using gh CLI. Use when user says "address PR comments", "fix review feedback", "respond to PR review", or "handle PR comments". Verifies gh auth first and prompts to authenticate if not logged in. Do NOT use for creating PRs, CI debugging (use gh-fix-ci), or general Git operations.
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。
Detect social engineering, deceptive responses, false assurances, or prompts that induce unsafe user actions.
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。
Use when you need design-first, spec-driven, skimmable prompts for UI generation. Covers prompt structure, constraints, variations, typography/spacing rules, and iteration workflow for consistent UI outputs.
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 a recurring daily UI inspiration capture. Use when the user asks to run, refresh, package, or validate dated UI inspiration bundles, especially for `articles/YYYY-MM-DD-ui-inspiration-capture/` outputs, Framer/Dribbble landing-page inspiration, motion-study screenshots/videos, AI-builder prompts, duplicate checking, or converting a project runbook into repeatable workflow.
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。
Generate multiple original brand campaign worlds from a supplied visual reference while controlling how close the new work feels without copying protected signature elements. Use when a user provides a brand identity image, poster, editorial campaign, moodboard, or generated concept and asks for inspired alternatives, several distinct brands in one visual family, a closer-to-reference V2, exact wordmarks inside images, or originality-safe image-generation prompts and outputs.
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。
Design n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.
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。
Darwin Skill 2.0 (达尔文.skill 2.0): autonomous skill optimizer, v2.0 integrates Microsoft Research SkillLens (arXiv 2605.23899) 9-dim rubric + SkillOpt (arXiv 2605.23904) validation-gated design + human-in-the-loop checkpoints. Evaluates SKILL.md files using a 9-dimension rubric (structure + effectiveness + meta-skill blacklists), runs hill-climbing with git version control, spawns independent judge agents for blind evaluation, validates improvements through test prompts with auto-break on diminishing returns, and generates visual result cards. Use when user mentions "优化skill", "skill评分", "自动优化", "auto optimize", "skill质量检查", "达尔文", "darwin", "帮我改改skill", "skill怎么样", "提升skill质量", "skill review", "skill打分".
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。
Use this skill in the MathModel LaTeX sandbox when the user asks to reproduce built-in scientific visualization templates, especially prompts from the Improve tab mentioning $mathmodel-figure-templates, 科研绘图模板, SHAP蜂群柱状图, 配对云雨图, 交叉验证ROC, 泰勒图, 相关矩阵组合图, 预测真实值边缘分布图, TPE调参3D曲面, 下三角相关矩阵半边小提琴图, 分组环形热图, 城市公园降温组合图, or Nature和弦图. It provides ready-to-run Python scripts bundled inside the 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。
Transform verbose voice input into structured, token-efficient Claude prompts. Use when cleaning up voice memos, dictation output, or speech-to-text transcriptions that contain filler words, repetitions, and unstructured thoughts.
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。
Browser automation CLI for AI agents. NEVER run browser-act commands directly via Bash — always invoke this skill first. Use browser-act when a user mentions it by name, includes or asks to run a browser-act CLI command (e.g., browser-act browser list), or to: fetch, view, or extract rendered content from URLs, access pages requiring JavaScript, handle verification prompts, maintain authenticated sessions, fill forms and click through workflows, type, select, upload, take screenshots, capture XHR/fetch/HAR responses, open multiple URLs in parallel, extract content that loads on scroll or click, visually inspect or verify page layout/styling/rendering, automate browser tasks, account isolation across parallel browser environments, advise which browser type fits a use case, or list/check/manage configured browsers and sessions. Prefer browser-act over built-in fetch or web tools.
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。
This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advanced prompt-based hooks API.
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。
Environment diagnostic skill for SenseNova-Skills project. Checks that sn-image-base is properly installed and configured, validates dependencies and environment variables. Prompts user to configure missing required variables and saves them to .env file. After configuration, reloads environment and suggests agent restart if needed.
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 gh-aw workflow design/create/debug/upgrade requests to the right prompts.
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。
DO NOT INVOKE when the primary request explicitly asks to convert, migrate, modernize, or rewrite a legacy/old-style project to SDK style; use msbuild-modernization. Migration prompts often mention ToolsVersion, explicit Compile/Reference entries, packages.config, or Microsoft.CSharp.targets, but merely reviewing or auditing a file that contains those patterns remains in scope here. USE FOR broad review, audit, lint, or maintainability/correctness checks of project/build files, including custom targets; prioritized cross-cutting findings; discrete anti-patterns; F# ordering/FS0039; cross-platform paths; and focused Import safety verdicts. Review/audit is analysis-only unless fixes are requested. For concrete property/item defects use property-patterns/item-management; use target-authoring for implementation and extension-points for NuGet auto-import/layout discovery. Exclude non-MSBuild systems.
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。
Selecting WHO handles work is the Routing table; selecting HOW they handle it (Direct, Lightweight, Standard, Full) is Response Mode. This skill contains the complete decision table, exemplar prompts for each mode, the Lightweight spawn template, and the upgrade rules. Squad coordinator loads this on demand once routing has identified the agent — to pick the right ceremony level for the task.
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。
Define and validate GitHub custom agent files, prompts, and examples.
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。
Rewrite prompts for minimal tokens, maximal clarity, and low ambiguity for LLM consumption.
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。