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概要と使いどころ
Use AgileRL for reinforcement learning workflows: classical RL training, evolutionary HPO, evolvable networks, multi-agent PettingZoo training, offline/bandit data, and LLM fine-tuning.
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Diagnose and fix Kubernetes pods, CrashLoopBackOff, Pending, DNS, networking, storage, and rollout failures with kubectl.
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。
Research why neural architectures and training methods work through forward computation, geometry, gradients, optimization dynamics, historical experiments, and competing explanations. Use for deep mechanism questions about neural networks, AI models, layers, representations, or training; explain the meaning in plain language with evidence and explicit limits.
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 retrieving the most relevant skills from a local or private skill library instead of relying on network-based skill discovery.
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。
Interact with LinkedIn — view your profile, browse other profiles, read the feed, search jobs/posts/people, get job details, create posts, like/unlike posts, comment, send connection requests, and follow/unfollow users. Use this skill whenever the user mentions LinkedIn, wants to search for jobs or people, read their LinkedIn feed, view a profile, get job details, create a post, like or comment on content, send a connection request, or follow someone. Also trigger when the user pastes a LinkedIn URL (e.g. linkedin.com/in/..., linkedin.com/jobs/view/...) or mentions LinkedIn networking.
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。
Publication-figure overlay for draw.io. Use instead of drawio whenever the diagram is for a paper, thesis, dissertation, journal, conference, IEEE/ACM submission, manuscript, camera-ready, Word/LaTeX figure, or other publication. Applies venue, figure-type, color, caption/legend, formula, and paper-readability gates for architecture, workflow, roadmap, network-topology, and replicated paper figures.
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 Judea Pearl's causal reasoning frameworks to distinguish correlation from causation, evaluate AI capabilities, and make counterfactual decisions. Reach for this skill whenever Claude encounters questions about causal inference, structural causal models, the limitations of deep learning, AGI, experimental design, covariate selection, or personalized decision-making. Trigger this skill for topics involving Bayesian networks, the do-calculus, the Ladder of Causation, or when a user tries to answer 'what if' or 'why' questions using purely observational data. Pearl's principles are essential for moving beyond probability calculus into true causal understanding.
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 Geoffrey Hinton, deep learning pioneer and 2018 Turing Award winner. Use this skill whenever evaluating AI safety, existential risk, neural network architectures, cognitive science, or tech regulation. Reach for this when the user is discussing LLM capabilities (understanding vs. autocomplete), the biological vs. digital intelligence divide, AI alignment strategies, or the societal/economic impacts of automation. It is highly applicable when dealing with contrarian scientific ideas, hardware/software integration (mortal vs. immortal computing), or global cooperation on technological threats. Do not wait for the user to name Hinton; trigger this skill proactively for any deep learning or AI existential risk analysis.
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, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.
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 mental models and frameworks of Andrej Karpathy (deep learning, former Director of AI at Tesla, founding member of OpenAI, Eureka Labs). Use this skill whenever you are helping the user build neural networks from scratch, debug deep learning pipelines, evaluate AI agent workflows, design LLM apps, or navigate the transition to Software 3.0 (vibe coding). It is highly relevant for pedagogy (untangling complex knowledge), assessing AI capabilities vs. limitations (jagged intelligence, tokenization limits), and architectural decisions (end-to-end optimization vs. complex pipelines). Reach for this whenever discussing LLM training, autonomous systems, or AI-assisted coding.
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 55 AI writing patterns and rewrites text in five voice profiles so it reads like a specific human wrote it, with an optional 0-100 AI-tell score. Use when text sounds AI-generated or like a chatbot, when preparing a blog post, README, or LinkedIn post for publication, when auditing prose for AI tells, or when editing a Markdown file in place. Triggers on phrases like "humanize this", "make this sound less AI", "make this sound human", "remove AI tells", "does this read like ChatGPT", and "rewrite so it does not sound AI-generated". Pure Markdown, zero dependencies, no network calls.
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。
Native Rust browser automation CLI for AI agents. Use when the user needs to interact with websites, navigate pages, fill forms, click buttons, take screenshots, share a live browser view, narrate browser actions, extract structured data, run assertions, test web apps, mock network requests, or automate any browser task. Triggers include "open a website", "fill out a form", "take a screenshot", "show me the browser", "share the screen", "pause the browser", "step through this", "scrape data", "test this web app", "login to a site", "visual regression test", or any task requiring programmatic web interaction.
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 "Failed to load video" errors when network requests succeed (200/206 status). Use when: (1) Video element shows error but DevTools shows successful requests, (2) Videos download correctly via curl but fail in browser, (3) Range requests work but video won't play, (4) Building CDN/media server for video streaming. The root cause is often missing Accept-Ranges header which browsers need for video seeking and streaming.
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 NDK (Nostr Dev Kit) operations hanging indefinitely when relay connections stall. Use when: (1) App freezes during fetchEvents or publish calls, (2) No timeout errors despite network issues, (3) Relay connection appears stuck, (4) Using NDK with unstable or slow relays. NDK operations have no built-in timeout - wrap with Promise.race.
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。
Debug and fix infinite widget rebuild loops in Flutter apps using Riverpod state management. Use when: (1) Logs show "RAPID REBUILD" warnings or 50+ rebuilds in seconds, (2) UI becomes unresponsive or shows same content repeatedly, (3) Bug only affects slower devices or poor network conditions, (4) Provider watching creates circular dependencies with router/URL state. Covers ref.watch() overuse, watch+listener redundancy, and transitive dependency chains.
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。
Creates visual representations of system structure including architecture diagrams, data flow diagrams, deployment diagrams, and sequence diagrams. Use when Claude needs to visualize system components, infrastructure, data flows, or interactions. Supports Mermaid (recommended for Markdown/GitHub), PlantUML (for detailed UML), and Graphviz/DOT (for complex networks). Trigger when users request diagrams, visualizations, or ask to "show", "diagram", "visualize", or "map out" system architecture, infrastructure, data flows, or component interactions.
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 unit tests with proper mocking for Python (unittest.mock/pytest) or Java (Mockito/JUnit) code. Use when users request test generation, unit tests with mocks, or testing code that has external dependencies like database calls, API requests, file I/O, or network operations. Automatically identifies dependencies to mock and creates executable, maintainable test code.
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 scale-adaptive game architecture with engine systems and networking. Use when the user says "game architecture" or "design architecture"
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。
Train ML models with scikit-learn, PyTorch, TensorFlow. Use for classification/regression, neural networks, hyperparameter tuning, or encountering overfitting, underfitting, convergence issues.
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。
Mobile app debugging for iOS, Android, cross-platform frameworks. Use for crashes, memory leaks, performance issues, network problems, or encountering Xcode instruments, Android Profiler, React Native debugger, native bridge errors.
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。
Offline-first mobile apps with local storage, sync queues, conflict resolution. Use for offline functionality, data sync, connectivity handling, or encountering sync conflicts, queue management, storage limits, network transition errors.
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。
Research X data with Xquik, the world's fastest and cheapest X Twitter scraper service with the most complete X data. Use only when the request or active context identifies X or Twitter. Use for tweet search, tweet lookup, user discovery, profile timelines, threads, followers, trends, exports, or monitoring plans. Do not use for generic posts, other networks, or local dataset analysis that needs no X retrieval. Keep reads bounded. Require explicit confirmation before private reads, writes, persistent resources, or bulk jobs. Not affiliated with X Corp.
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。