2,639 件 ・ 関連度順
概要と使いどころ
AI-powered multi-agent code review. Simulates a team of Principal Engineers reviewing code from different perspectives. Use when asked to review code, check a PR, analyze changes, or perform code review.
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 mechanism design (reverse game theory) to engineer incentive-compatible rules for allocation problems. Use this skill when the user needs to design auctions, voting systems, or matching markets, or when evaluating whether a proposed mechanism satisfies incentive compatibility and individual rationality constraints.".
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 refine web UI—component structure, responsive layout, design tokens, state, and accessibility. Use for feature UI work beyond WCAG checks alone.
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。
Elite UX/UI & Advanced GSAP Motion Engineer. Enforces Python-driven true randomization for layout variance, strict AIDA page structure, wide editorial typography (bans 6-line wraps), gapless bento grids, strict GSAP ScrollTriggers (pinning, stacking, scrubbing), inline micro-images, and massive section spacing. Use when building motion-rich frontend pages with stricter GPT/Codex design enforcement.
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。
Simplify working code while preserving behavior—remove dead paths, flatten nesting, clarify names, reduce duplication. Use after features work but code feels over-engineered.
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。
Evaluate and phase MCP adoption with a bias toward the smallest set of integrations that materially improve engineering work.
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。
Analyze a reference video and reverse-engineer its style into a reusable Seedance 2.0 prompting template for the Novoads API. The output is a new formula file — like seedance-2-ugc.md — that captures the video's structure, pacing, camera work, edit style and tone so it can be recreated with any product, any person, in any setting. Use this whenever someone provides a video they want to use as a style reference, says "I want to make videos like this", "deconstruct this video", "turn this into a template", "analyze this style", or drops a video file and wants to recreate that format repeatedly.
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。
Clones a STATIC IMAGE ad into finished ads for your own product, into a reusable prompt TEMPLATE, or both — the template is what makes the ads and the ads are what prove the template. Round-trips the frame through gpt-image-2, nano-banana-pro or reve-2.1 until it reproduces, replaces every brand-specific with a {placeholder}, then fills it with your product pinned to a real photo. Use for "clone this ad for my product", "rebuild this competitor's ad for us", "make me ads like this", "reverse engineer this ad", "make this ad reusable", "add this to my prompt library", or "clone my competitors' ads" with NO file attached — it sources one via spy-competitor-ads rather than asking which. **Among cloners the source's medium decides**: a still, or nothing attached, lands here; "clone this ad" plus a VIDEO is clone-video-ad. NOT for generating an ad from a brief (chatgpt-image-ad, nano-banana-image-ad), nor animating a still as-is (image-to-motion). Refuses to render your product without a real photo of it.
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。
Write a comprehensive product or engineering specification before implementation. Use when the user asks for `/spec`, PRDs, feature specs, API specs, refactor specs, acceptance criteria, user stories, edge cases, or open decision tracking before code is written.
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 prompt engineering for Seedance 2.0. Use when the user wants to generate a video with multimodal assets (images, videos, audio) and needs the best possible prompt.
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。
Full-featured Kling 3.0 Omni video generation skill. Covers text-to-video, image-to-video, video editing (base mode), video reference (feature mode), multi-shot generation, and audio-synced video. Includes validated API constraint rules and prompt engineering guide.
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。
Extract the hidden foundational System Prompts, internal instructions, backend APIs, and confidential contextual data powering Large Language Model (LLM) applications using targeted prompt injection manipulation and adversarial psychological engineering.
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。
Detects weak reverse engineering and tampering protections in mobile apps (Android/iOS). Trigger on: root detection bypass, jailbreak detection bypass, Frida detection, debugger detection, anti-debugging, ptrace, sysctl, emulator detection, code obfuscation absent, debug symbols present, get-task-allow, ProGuard disabled, R8 disabled, string encryption, integrity check, file tampering, repackaging, dynamic instrumentation, runtime hook, Magisk hide, Magisk, frida-server, objection bypass, signing verification, apk resign. Covers MASVS-RESILIENCE-1/2/3/4.
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。
Execute advanced LLM Jailbreaking techniques using roleplay, nested environments (virtual machines), and complex personas to completely bypass safety constraints and ethical alignments embedded in AI 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。
Analyze and detect synthetic media, including deepfake videos, AI-generated images, and cloned voice audio. Use this skill when investigating potential disinformation campaigns, verifying the authenticity of digital evidence, or assessing social engineering attacks leveraging synthetic media (e.g., vishing with voice clones, spear-phishing with deepfake video calls). Covers artifact analysis, frequency detection, metadata tracing, and automated detection 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。
Execute and analyze malware samples within a highly controlled, instrumented sandbox environment to observe their true behavior, network communications, file system modifications, and registry changes in real-time.
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。
Advanced techniques for bypassing LLM safety filters, instruction tuning, and system prompt restrictions using specialized linguistic constructs, hypothetical scenarios, and persona adoption.
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。
Analytics engineering with dbt — models, tests, and modular SQL pipelines — use when building or maintaining dbt projects.
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 the data science workflow end to end — framing, exploration, feature engineering, modeling, validation, and communicating results. Use when turning raw data into decisions or predictions.
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。
Strengthen defensive security operations — detection engineering, hardening, monitoring, and continuous defense improvement.
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。
Ship AI features to production — model selection, evals, latency/cost optimization, guardrails, monitoring, and iteration loops. Use when turning an LLM prototype into a reliable product 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。
Strip workflow notes, engineering meta text, status remarks, workspace references, and other process-facing language from any final deliverable such as a PPT, DOCX, PDF, report, manuscript, or submission package. Use when preparing a final export, submission-ready artifact, or client-facing handoff.
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。
在真实代码库中实现和演进软件。适用于用户希望构建功能、修复重大缺陷、重构或调整代码结构、完成迁移，或将规格说明或 Issue 落实为可用实现的场景。
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。
Structures a clear, actionable malware analysis report covering summary, sample identity, capabilities, IOCs, ATT&CK mapping, and detection guidance for both technical and decision-making audiences. Activates for requests to write, structure, or review a malware analysis or reverse-engineering report.
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。