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Generate 5 different narrative scripts for visual slideshows from a single positioning angle. Each script uses a different storytelling framework (AIDA, PAS, BAB, Star-Story-Solution, Stat-Flip) producing genuinely different stories, not layout variations. Make sure to use this skill whenever the user wants slideshow scripts, TikTok content scripts, carousel copy, narrative frameworks for visual content, or says anything about writing scripts for slides or social media storytelling. Even 'write me some TikTok content' or 'I need carousel copy' should trigger this. Outputs structured YAML content specs that chain directly to /paper-marketing for visual design.

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Generate visual asset briefs, ad copy variants, AI image prompts, video scripts, and storyboards. Full creative production system with 5 specialized modes: product photos, product video, social graphics, talking heads, and ad creative. Make sure to use this skill whenever the user mentions any visual or creative marketing need — ad creative, image prompts, video scripts, thumbnails, banners, social graphics, product photography, storyboards, or marketing visuals of any kind. Even if they just say 'I need images for my campaign' or 'make something visual', this is the skill. Owns thumbnail and banner briefs plus the creative direction. Includes platform-specific dimensions, AI anti-slop techniques, and Remotion composition templates. For rendering the actual image file, see /image-gen; for AI-generated marketing/brand videos, see /higgsfield-generate; for stitching slides into an MP4, see /video-content.

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。

根據使用者的文章或論文草稿，自動用 Semantic Scholar、Crossref、arXiv API 搜尋相關文獻，並查核既有引用(文獻是否真實存在、書目欄位是否正確、內容是否支持文中論點)，也能從主題出發寫出「每個宣稱都有真實文獻支撐」的帶引用文章；另含研究生工具組：文獻矩陣、領域地圖、research gap 偵測、閱讀筆記卡、引用完整性檢查、中英術語一致性、口試/審稿預演、新文獻追蹤、引用需求標記(annotate)、反面證據搜尋(counter)、證據強度評級(strength)、claim-evidence 總表(claims)、撤稿查詢(retract)。凡是使用者提到找文獻、找 paper、補參考文獻、查核引用、檢查 citation、驗證參考文獻、related work、literature review，貼文章要求配文獻、問「這段話有沒有文獻支持」、要求「寫一篇帶參考文獻的文章」、要整理文獻比較表、問已指名兩概念的交集有沒有人做過、要準備口試文獻答辯時，都要使用本 skill。本 skill 的 gap 指令只查證使用者已指名的 X 與 Y 交集；若使用者連題目都還沒有、需要生成並逐一淘汰候選方向，那是題目生成，不在本 skill 範圍(環境裝有 research-gap-hunter 時交給它，沒有就如實說明本 skill 只能查證已指名的交集)。本 skill 的 map 指令盤的是**文獻**——奠基文獻是哪幾篇、關鍵作者是誰、近三年往哪走；若使用者要的是「這個領域現在有哪些**做法**、每一種買到什麼付出什麼、哪些已經飽和」，那是技術盤點不是文獻盤點，交給 research-gap-hunter 的 landscape，沒有那個 skill 就如實說明本 skill 的 map 只回答文獻層的問題。

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Audit, finish, or port PCB placement and routing using circuit intent, mechanical constraints, sourcing evidence, native DRC/connectivity, rendered views, and manufacturing feedback. Use before routing, after autorouting, before fabrication release, when adapting a proven board to another connector, enclosure, platform, or regional variant, or when reviewing layers, GND references, power distribution, decoupling, buses, controlled interfaces, RF keepouts, vias, cost-driving features, opens, or DRC findings.

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How to use the Appwrite CLI well. Covers when to use appwrite.config.json vs `appwrite client`, pull/push vs one-shot service calls, type-safe SDK and model generation, query flags, CI auth, Cloud regional endpoints, function/site variables, and traps that `--help` will not mention. Does not catalog commands — run `appwrite <command> --help` for flags. Use whenever running `appwrite`, deploying or pulling functions/sites/tables, generating application code from a table schema, initializing or linking a project, scripting Appwrite, or when the user mentions the Appwrite CLI, appwrite.config.json, `appwrite generate`, `appwrite types`, or `appwrite client`.

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Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

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 installing or running the Inngest CLI and Dev Server for local development, local testing, serve endpoint debugging, Docker or Docker Compose setup, MCP configuration, self-hosted `inngest start`, or deployment workflow checks. Covers `inngest dev`, `inngest start`, auto-discovery, config files, environment variables, `@inngest/test`, local event sending, platform gotchas, and production/self-hosted server flags.

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Use when adding durable execution to a TypeScript project — building retry-safe webhook handlers, background jobs that survive crashes, scheduled tasks, or long-running workflows that outlive a single request. Covers Inngest SDK installation, client config, environment variables, serve endpoints (Next.js, Express, Hono, Fastify), connect-as-worker mode, and the local dev server.

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Use when building, migrating, or debugging Agent Evals on Inngest: scoring AI agent or workflow outcomes, deferred scorers, sessions, traces, step experiments, experiment variant attribution, Insights queries, or production eval loops for prompts, models, tools, providers, and agent behavior. Covers TypeScript SDK v4 scoring beta APIs, `scoreMiddleware`, `step.score`, `inngest.score`, `createScorer`, `defer`, `group.experiment`, `experimentRef`, `meta.sessions`, and when to use durable workflow primitives for outcome-based evaluation.

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。

Web (CSS/JS/React + GSAP/Motion): classify UI motion as communicative (sequence is the content) or decorative (decorating a result), then fix or apply prefers-reduced-motion so meaning survives under reduce. Use when auditing existing animation, gating new motion, verifying both preference states, catching a dead reduced-motion branch, snapping to an end state, or collapsing a transition without nuking essential cues. Not for React Native, full WCAG encyclopedias, keyboard traps, color contrast, focus management, or ARIA pattern audits.

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 the user wants a complete SPSS or SPSS-MCP empirical research workflow: organize source data, prepare datasets, design variables and models, run SPSS analyses, export Chinese result paragraphs and tables, write a Chinese LaTeX paper, and compile the final 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。

Use this skill whenever the user wants to use AI agents to work with Penpot design files via the Penpot MCP Server. Triggers include: using Penpot through an AI agent, design files, design systems, design tokens, Penpot MCP, design-to-code, generating UI from design, auditing a design system, creating components/variants, renaming layers, exporting assets from Penpot, adding flows, interactions, animations, overlays, or prototyping in Penpot, or prompting an AI agent to read/modify a Penpot file. Also triggers when the user wants to set up Penpot MCP, connect any MCP-compatible AI agent or IDE to Penpot, or produce production-ready HTML/CSS/React from a Penpot design. Use this skill for Penpot-agent workflows — design, code, audit, prototyping, or setup.

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Use this skill when evaluating VCSDD grading dimensions during adversarial review. Provides concrete evaluation checklists for each of the 5 dimensions and convergence signal detection.

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。

Run Phase 4 (feedback routing) after an adversarial review FAIL. Routes adversary findings to the earliest affected phase, checks iteration limits, and escalates to human if limits are exceeded.

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。

Run Phase 3 (adversarial review) for the active VCSDD feature. Spawns a fresh vcsdd-adversary agent with zero Builder context to review implementation against spec. Produces binary PASS/FAIL verdict per dimension.

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 challenging a prevailing analytical judgment, the user asks "what is the opposing case?" / "argue the other side" / "play devil's advocate", or wants an assessment stress-tested before release. Takes the team's own lead judgment and builds the strongest case that it is wrong. A contrarian technique. It does not model what an adversary would do next, which is Red Team Analysis. Formerly /red-team-analysis.

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Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.

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 custom AI agents in Claude Code from a user's problem statement. This skill analyzes the user's use case, asks smart clarifying questions, researches the internet for similar agents (GitHub repos, blogs, Claude Code community patterns), and then architects and builds production-ready Claude Code agents — including subagents, skills, hooks, slash commands, MCP integrations, and CLAUDE.md configuration. Use this skill whenever the user wants to create an agent, build an automation workflow, set up a Claude Code subagent, design a multi-agent system, or says things like 'build me an agent for...', 'automate this with Claude Code', 'I want a subagent that...', 'help me create a workflow', 'set up an agent pipeline', or any variation of wanting Claude Code to do something specialized. Also trigger when the user mentions agent architecture, agent SDK, agentic workflows, or task delegation in Claude Code — even if they don't use the word 'agent' explicitly.

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 whenever the user asks for an HTML-first visual design deliverable or design judgment: interactive prototype, slide deck, motion demo, infographic, dashboard, landing page, whitepaper, changelog, business card, social cover, brand system, design critique, multi-variant exploration, or export to MP4, GIF, PPTX, PDF, or SVG. It is optimized to make AI agents do the routing, template selection, verification, and export prep so humans spend less time prompt-engineering. Do not use for production web apps, SEO sites, backend systems, or pure copy edits.

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 and review accessible web products using WCAG 2.2 AA. Use when implementing or reviewing forms, dialogs, navigation, keyboard flows, focus management, ARIA, color contrast, responsive UI, or framework-specific accessibility in React/Next.js, Angular, and Vue.

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。

"Apply rigorous survey design principles including construct operationalization, Likert scale development, reliability and validity assessment, and common method variance control. Use this skill when the user designs questionnaires, develops measurement items, needs to evaluate Cronbach's alpha or AVE, or when they ask 'how do I operationalize this construct', 'is my scale reliable', or 'how do I control for CMV'.".

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"Design shotgun: generate multiple AI design variants, open a comparison board, collect structured feedback, and iterate. (gstack)".

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"Apply Structural Equation Modeling (SEM) to test hypothesized causal structures by combining measurement models (CFA) and structural models (path analysis). Use this skill when the user needs to validate latent constructs, test mediation or moderation paths, assess model fit with CFI/TLI/RMSEA/SRMR, or when they ask 'do these variables form a causal chain', 'how do I test my theoretical model', or 'is my measurement model valid'.".

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"Apply panel data analysis with fixed effects, random effects, and dynamic GMM to exploit longitudinal variation and control for unobserved heterogeneity. Use this skill when the user has repeated observations over time for multiple entities, needs to choose between FE and RE via Hausman test, or when they ask 'how do I control for firm-specific effects', 'fixed or random effects', or 'how to handle endogeneity in panels'.".

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。