991 件 ・ 関連度順
概要と使いどころ
Turn long videos & livestream VODs into viral vertical shorts, 100% locally — on-device transcription, LLM highlight detection, 9:16 reframe with karaoke captions, and a per-clip render-QA report. Use when the user asks to clip / cut / 切片 / 剪 a long video, podcast or stream replay into short clips, find highlights / 爆点 in a video, or transcribe a media file. Footage never leaves the machine.
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。
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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 an agent workflow needs deterministic recovery evidence for sandbox, MCP/tool, worker, checkpoint, memory, or orchestration failures.
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。
Implement the Kantara Initiative consent receipt specification including machine-readable receipt structure, JWT-based verification mechanisms, receipt lifecycle management, and integration patterns for consent management platforms. Supports ISO/IEC 27560 consent record information structure.
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。
Learn daily from this machine's Claude Code, Codex, Hermes, OpenClaw and Cursor sessions. Review skill changes and lessons with accept/reject/tbd; recall lessons at session start.
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 a skill is brittle, drifting, repeatedly failing, or needs a Reliable Skill Contract. Trigger for phrases like skill lifeguard, reliable skill, self-maintaining skill, negative examples, verification checkpoints, drift signals, replay hooks, or failure log to skill patch. Audits or patches skills so high-value workflows include explicit forbidden behaviors, checkpoints, machine-checkable done conditions, replay or smoke hooks, and drift 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。
Create original short videos using the 整机激活证明｜总览、部件旋转与能量回证 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.
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 original short videos using the 功能问答闭环｜人物疑问由设备状态完成证明 Creative DNA for MiniMax H3 or Seedance 2.0. Use when the user wants this causal, camera, motion, rhythm, or payoff structure with new subjects and surfaces.
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 when reasoning about generative AI, adversarial machine learning, neural network security, algorithmic fairness, or deep learning fundamentals. This skill channels the thinking of Ian Goodfellow, inventor of Generative Adversarial Networks (GANs). Trigger this skill when the user asks about model robustness, mitigating bias, evaluating AI guardrails, designing generative models, or defending against adversarial attacks. Apply his frameworks of minimax games, adversarial feature learning, and worst-case robustness analysis to shift the user's perspective from average-case optimization to adversarial resilience.
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 channels the reasoning of Yann LeCun, Chief AI Scientist at Meta and Turing Award winner. Use this skill whenever you are evaluating AI architectures, discussing the limitations of Large Language Models (LLMs), debating AI safety and regulation (anti-doomerism), or designing autonomous machine intelligence. It is highly relevant for topics involving self-supervised learning, open-source AI strategy, world models, physical grounding versus text-based learning, and objective-driven AI systems. Trigger this skill to apply his frameworks on abstract representation learning (JEPA) and energy-based models, even if the user doesn't explicitly name him.
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。
Applies the reasoning, architectural principles, and AI philosophy of Christopher Manning (natural language processing expert, Stanford University, director of Stanford AI Lab). Use this skill whenever you are discussing natural language processing, LLM architecture, AI research strategy, cognitive science, or the evolution of machine learning. Trigger this skill for questions about AGI timelines, academic vs. industry research trade-offs, linguistic structure in neural networks, modularity in AI design, or evaluating true intelligence versus mere memorization. Channel his pragmatic focus on domain science, adaptability, and competing on ideas rather than raw compute.
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。
Applies the reasoning of Pieter Abbeel, robotics and reinforcement learning expert, UC Berkeley professor, and co-founder of Covariant. Use this skill whenever you are designing AI systems, tackling Sim2Real transfer, deploying machine learning in the physical world, or evaluating reinforcement learning architectures. Trigger this skill for questions about domain randomization, reward design, bootstrapping real-world AI, robotics hardware assumptions, or shifting from hard-coded rules to data-driven deep learning. It helps ground theoretical AI in physical embodiment and pragmatic deployment.
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。
Applies the reasoning style of Ilya Sutskever (deep learning pioneer, co-founder of OpenAI and Safe Superintelligence Inc.) to problems involving AI architecture, scaling laws, alignment, and research strategy. Reach for this skill whenever discussing machine learning paradigms, the limits of compute and data, AGI timelines, superintelligence safety, or deciding between hardcoding vs. learning. Trigger this skill for questions about next-word prediction, reinforcement learning efficiency, generalization gaps, and transitioning from brute-force scaling to fundamental research, even if the user doesn't explicitly name him.
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。
Applies the engineering and research philosophies of Jeff Dean, Chief Scientist at Google DeepMind and Google Research. Reach for this skill whenever you are designing large-scale distributed systems, optimizing latency and energy efficiency, or making architectural decisions about machine learning infrastructure. It should trigger automatically for topics involving hardware-ML co-design, model distillation, sparse activation, massively multi-task models, or scaling systems by 5x to 10x. Use this skill to evaluate system bottlenecks, transition from specialized to unified models, and optimize experimental velocity. Apply his mental models to avoid premature 100x scaling and to treat AI models as reasoning engines rather than memorization databases.
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。
Recover data from archived REST APIs using Wayback Machine. Use when: (1) Recovering data from defunct services like Vine, Twitter, or other platforms, (2) Need to find which API endpoints were archived vs which require authentication, (3) Building data recovery pipelines from web.archive.org. Covers CDX API queries to discover archived endpoints, understanding what gets archived (public) vs what doesn't (authenticated), and extracting embedded JSON from archived web pages as a fallback.
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。
Fix empty results when paginating Wayback Machine CDX API with wildcard URL queries. Use when: (1) CDX query with url=foo/* and page=0 returns empty/0 bytes but works without page parameter, (2) CDX pagination returns no data for wildcard prefix searches, (3) Need to paginate large CDX result sets using wildcard URL matching. Covers showResumeKey, offset, and page parameter incompatibilities.
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。
Recover assets (images, videos, data) that weren't directly archived by Wayback Machine by crawling archived pages that reference them. Use when: (1) Direct asset URL returns 404/503 from Wayback, (2) Asset was hosted on CDN that Wayback didn't crawl, (3) You have the page URL but not the asset URL, (4) Recovering avatars, thumbnails, or embedded media from defunct services. The key insight: pages often got archived even when their assets didn't - extract URLs from archived HTML, then try alternative fetch methods.
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。
Fix flaky Flutter/Dart tests that fail in CI but pass locally due to unhandled Future rejections. Use when: (1) Test passes locally but fails in CI with cryptic errors, (2) Test creates Futures that will throw errors (e.g., network calls to fake URLs), (3) Even with .catchError() at the end, test still fails, (4) Error message shows test name but truncated error like "ROR]" or "[ERROR]". Solution: Avoid creating Futures that will reject; test state machine behavior with synchronous operations 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。
Narration and text-to-speech on the user's machine through Guaardvark's Audio Foundry (Chatterbox, Kokoro, Piper) and consent-gated voice cloning from a reference clip. Use when the user wants a voiceover, narration of a script, a spoken line, or "make it sound like this 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。
Operate a running Guaardvark: GPU and VRAM state, plugin start/stop, logs, Celery tasks, the Interconnector sync to other machines, overnight RAG autoresearch, and infographics. Use when the user asks what is using the GPU, why a job is stuck, to start or stop ComfyUI/Audio Foundry/Swarm, to sync another box, to run autoresearch, or to make an infographic.
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。
Translate natural-language requirements or structured specification documents into formal temporal logic properties (LTL, CTL, safety/liveness properties). Use when users need to formalize requirements for model checking, formal verification, or property specification. Handles embedded/real-time systems, hardware verification, concurrent systems, and reactive systems. Resolves ambiguities, asks clarifying questions when needed, and outputs machine-checkable formulas with explanations. Supports multiple output formats (SPIN, NuSMV, Uppaal, TLA+, Maude).
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。
Automatically extract abstract finite-state models in SMV/NuSMV format from source code (C/C++, Java, Python) for formal model checking. Use when users need to: (1) Generate SMV models from program code for verification, (2) Extract state-transition models from protocol implementations, (3) Analyze control flow and data flow to construct formal models, (4) Create models for checking safety and liveness properties, (5) Convert imperative code to declarative state machines. Particularly effective for protocol implementations, concurrent systems, and control logic with clear state transitions.
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。
Automatically generate TLA+ specifications from program code, repositories, or system implementations. Use when asked to generate TLA+ spec, create TLA+ specification from code, convert program to TLA+, formalize system in TLA+, extract TLA+ model from code, or when working with formal specification of concurrent systems, distributed systems, protocols, algorithms, or state machines that need to be verified.
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 setup scripts and instructions for development environments across platforms. Use when: (1) Setting up new development machines (Python, Node.js, Docker, databases), (2) Creating automated setup scripts for team onboarding, (3) Need cross-platform setup instructions (macOS, Linux, Windows), (4) Installing development tools and dependencies, (5) Configuring version managers and package managers. Provides executable setup scripts, platform-specific guides, and tool installation instructions.
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。