1万 件 ・ 関連度順
概要と使いどころ
Use when the user wants to design or redesign the Dataverse schema and connector plan for an existing mobile app, or has an ER diagram (image, Mermaid, or text) to apply. Skip when the user is creating a brand-new app — /create-mobile-app handles the data model inline.
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。
把一条视频重建成「只有画面和声音」的干净文件——**源片的元数据一概不搬**： GPS、设备型号、账号 ID、创建时间、章节、GoPro 的遥测轨，全部留在原地。 走的是白名单而不是黑名单：不列要删什么，只说带什么过去（画面一条流、声音一条流）， 隐私保证来自结构，不来自枚举。 难点不在容器 tag，在**看不见的那几层**：x264 把完整编码参数写成 SEI 塞在码流里、 AAC 把版本号写进 DSE、avc1 的 compressorname 里还有一份——`ffprobe` 一个都看不见。 五个藏身处逐一堵死，每一处都有实测。 默认 `--mode copy`：**画面逐字节照搬**（cmp 验证过），只重编音频，53 秒的片子 1.3 秒跑完； `--mode encode` 完整重编码，多杀掉码流域的东西。 验收不靠声称：把源片所有元数据字符串当「针」，在输出文件里做**字节级扫描**， 扎到一根就红。12 道门全部由脚本确定性检查。 零依赖、零 API key，只要 node 和 ffmpeg。 Use when asked to 清元数据、去元数据、抹掉视频信息、视频隐私、去水印信息、 strip video metadata、remove exif from video、scrub video。
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。
Best practices for implementing Android ViewModels using Kotlin 2.3+ Explicit Backing Fields, StateFlow for UI state, and SharedFlow for one-off events.
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。
Optimize content and site structure for AI-driven search experiences including AI overviews, large language model citations, generative answer engines, and AI assistants. Use this skill whenever the user wants to optimize for AI search, get cited by language models, appear in AI overviews, build llms.txt, structure content for AI extraction, or future-proof their SEO for the shift from blue links to AI answers. Triggers on AEO, GEO, AI search, AI SEO, AI overview, generative search, LLM optimization, llms.txt, AI citation, ChatGPT search, Perplexity, Gemini, Claude search, AI assistant optimization, answer engine. Also triggers when the user expresses concern about AI eating their organic traffic or wants to understand how to remain visible as search shifts.
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。
How to read paid media dashboards without fooling yourself. Attribution models, platform reporting quirks, multi-platform reconciliation, ROAS vs LTV horizon traps, statistical noise in performance metrics, incrementality testing, and the failure modes that produce expensive lessons. Triggers on read paid media dashboard, attribution analysis, ROAS vs LTV, multi-platform reconciliation, ad incrementality, geo holdout, conversion lift study, ghost bidding, paid media reporting, board-deck paid media metrics, blended CAC, MMM, MTA, last-click attribution. Also triggers when a marketer is about to scale, kill, or rebudget a campaign based on platform metrics, or when reconciling platform reports against warehouse revenue.
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 or audit a design system including component library, design tokens, naming conventions, contribution model, and documentation. Use this skill whenever the user wants to build a design system, audit an existing system, define design tokens at the system level, structure a component library, or set up design system governance. Triggers on design system, component library, design tokens, atomic design, atoms, molecules, organisms, design system documentation, Storybook, Figma library, system governance, design contribution model. Also triggers when teams are inconsistent across products and a system is the answer.
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 measurement frameworks including event taxonomy, KPI hierarchy, dashboard architecture, attribution models, and analytics implementation strategy. Use this skill whenever the user wants to plan analytics, design dashboards, build event taxonomies, define KPIs, set up tracking, or audit existing measurement. Triggers on analytics strategy, measurement plan, event taxonomy, tracking plan, KPI framework, dashboard design, north star metric, attribution model, conversion tracking, GA4 setup, Mixpanel setup, analytics audit. Also triggers when the user has data but no clear way to use it, or wants to make decisions but doesn't know what to track.
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。
Grade a ForgeCAD or CAD-as-code model against a requirement, brief, prompt, reference, or acceptance criteria with evidence and a 0-10 score.
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 or edit a manufacture-realistic `.forge.js` model in a project, then validate it with run, render, inspect, and export evidence.
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, write and measure ads inside AI assistants and AI search (ChatGPT Ads, Google AI Overviews and AI Mode, Microsoft Copilot). Use when testing ChatGPT ads, answer-adjacent copy, or incrementality for AI placements.
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。
Marketing analytics covering campaign analysis, attribution and marketing mix modeling, ROI measurement, and performance reporting. Use when analyzing campaign ROI, comparing attribution models, or optimizing budget allocation.
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。
People analytics across workforce metrics, predictive modeling, and employee insights. Use when building turnover models, analyzing engagement surveys, running pay equity regressions, or scoring flight risk.
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 tokens, light/dark theming, and WCAG contrast validation for HTML documents and decks, compiled into one inlinable CSS bundle. Use when theming a report or deck, auditing contrast, or fixing dark-mode color drift.
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。
Computer vision engineering for object detection, segmentation, and visual AI, covering CNN and Vision Transformer architectures and ONNX/TensorRT deployment. Use when building detection pipelines, training models, or optimizing inference.
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。
MLOps across model deployment, ML pipelines, monitoring, and feature stores. Use when deploying models to production, building training pipelines, setting up drift detection, configuring feature stores, or automating ML CI/CD workflows.
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 "scan AI systems for security threats", "check for prompt injection vulnerabilities", "assess model security posture", "detect data poisoning risks", or "audit AI/ML pipeline security".
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。
Revenue leadership for B2B SaaS: forecasting, sales model design, pricing, and net revenue retention. Use when designing the revenue engine, setting quotas, modeling NRR, evaluating pricing, or scaling sales teams.
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。
Ultra-compressed communication mode (lite / full / ultra) that cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Use when the user requests "caveman mode", "less tokens", "be brief", or when output budget is tight.
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。
Protein language model analysis using Meta ESM models
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。
Eng manager-mode plan review. Scope challenge, architecture review, code quality review, detailed test coverage diagram, performance review. Produces test plan artifact for downstream QA. Optional cross-model outside voice.
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。
Live-site visual QA audit with fix loop. 10-category checklist (~80 items) including AI Slop Detection (10 blacklisted patterns). Atomic commits per fix. Before/after screenshots. A-F grading. Parallel cross-model code-level design audit. Modes: Full, Quick, Deep, Diff-aware, Regression.
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。
Multi-AI second opinion via OpenAI Codex CLI. Three modes: Review (pass/fail gate), Challenge (adversarial edge-case finder), Consult (freeform with session continuity). Read-only with streaming output and thinking traces. Cross-model analysis when review has already run.
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。
YC Office Hours — product idea diagnostic before writing code. Two modes: Startup (6 forcing questions with rigorous pushback) and Builder (generative brainstorming). Outputs design doc. Includes landscape research, premise challenge, alternatives generation, visual wireframe sketching, and optional cross-model second opinion.
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。
Fine-tunes LLMs and trains custom models using LoRA/QLoRA adapters, JSONL training datasets, hyperparameter configuration, RLHF, DPO, and model quantization.
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。