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概要と使いどころ
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
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 and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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。
Capture the current frame's timing info (delta time, FPS, frame counts, runtime). Snapshot only — historical frames live in Unity's Profiler window.
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。
Return current frame timing, FPS, vsync, target frame rate, threading mode, and graphics device type from Unity Time / QualitySettings / SystemInfo.
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。
Get information about the current Selection in the Unity Editor — active object, active transform, selected GameObjects, transforms, instance IDs, and asset GUIDs (each enrichment is opt-in). Pair with 'editor-selection-set' to change the selection.
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。
Get detailed information about a specific Component on a GameObject — type, enabled state, and (optionally) serialized fields and properties. Supports token-saving path-scoped reads via `paths` or `viewQuery`. Use 'gameobject-find' to list components first.
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。
Present options with trade-offs for informed decision-making — use when choosing between approaches
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。
剪映 (JianYing) AI自动化剪辑的高级封装 API (JyWrapper)，提供开箱即用的 Python 接口，支持录屏、素材导入、字幕生成、Web 动效合成及项目导出。全面适配 MacOS (Apple Silicon/Intel) 与 Windows，支持 v5.9+ (draft_info.json) 架构、工程自修复、智能配音字幕及录屏变焦。
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 editing or creating CLI output, logging, warnings, error messages, progress indicators, or diagnostic summaries in the APM codebase. Activate whenever code touches console helpers (_rich_success, _rich_warning, _rich_error, _rich_info, _rich_echo), DiagnosticCollector, STATUS_SYMBOLS, CommandLogger, or any user-facing terminal output — even if the user doesn't mention "logging" or "UX" 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。
Activate when code touches token management, credential resolution, git auth flows, GITHUB_APM_PAT, ADO_APM_PAT, AuthResolver, HostInfo, AuthContext, or any remote host authentication -- even if 'auth' isn't mentioned 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 when the user asks Codex to research, find learning materials, process "素材：" links, "请你读" / "精读" a material, build a material radar, or use multi-source information retrieval for career learning and Agent infra tracking. Do not use the career/Agent-infra routing bias for user-directed `整理笔记` into a named note.
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。
Automates Apple-platform apps (iOS, tvOS, macOS) and Android devices. Use when navigating apps, taking snapshots/screenshots, tapping, typing, scrolling, extracting UI info, collecting logs/network/perf evidence, or planning agent-device CLI commands.
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。
Comprehensive citation management for academic research. Search Google Scholar and PubMed for papers, extract accurate metadata, validate citations, and generate properly formatted BibTeX entries. This skill should be used when you need to find papers, verify citation information, convert DOIs to BibTeX, or ensure reference accuracy in scientific writing.
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。
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
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 experiments and studies BEFORE data is collected — choosing a design, randomizing, blocking, and laying out treatment combinations so the results will actually be interpretable. Use whenever someone is planning a study, asks how to assign subjects/samples to groups, mentions randomization, blocking, stratification, controls, factorial or fractional-factorial designs, design of experiments (DOE), screening many factors, response-surface optimization, crossover or repeated-measures or split-plot designs, cluster/group randomization, Latin squares, plate layouts, batch/run-order effects, replication vs. pseudoreplication, or sequential/adaptive/group-sequential designs. Trigger this even for informal phrasings like "how should I set up this experiment", "how do I avoid confounding", "what's the best way to test these 6 factors", or "assign these mice to conditions". For computing the sample size or power once the design is chosen, use statistical-power; for analyzing data already collected, use statistical-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。
Problem-solving strategies for entropy in information theory
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。
Problem-solving strategies for source coding in information theory
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。
Problem-solving strategies for channel capacity in information theory
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 PinMe project (Worker TypeScript) needs to integrate user authentication — creating email/password users, verifying id_tokens, querying user info, or listing users via Identity Platform auth proxy APIs.
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。
Convert Markdown to WeChat Official Account HTML. Use this whenever the user wants WeChat article formatting, article preview, WeChat draft upload, image generation for articles, cover or infographic generation, image-post creation, writer-style drafting, title suggestions, AI trace removal, or current discovery of supported providers, themes, prompts, and layout modules. Also use for unpublished Zhihu, CSDN, Toutiao, or Tencent Cloud Developer Community drafts through a browser. Use for product introductions, encyclopedia articles or entry drafts, and writing tailored to platforms or search targets.
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。
Domain routing and boundary guidance for authorized reconnaissance and information gathering, including subdomain enumeration, port scanning, directory discovery, fingerprinting, and OSINT. Use when a task belongs to the recon domain and needs scope, evidence, pivot, or exit criteria.
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。
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib 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。
Core cheminformatics toolkit for SMILES/SDF/InChI parsing, descriptors (MW, LogP, TPSA), fingerprints, ECFP/Morgan fingerprints, substructure search, 2D/3D generation, similarity, reactions, and datamol-style molecule standardization when no separate wrapper skill is routed.
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 working with reinforcement learning tasks including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use this skill for implementing PPO training, creating PufferEnv environments, optimizing RL performance, or developing policies with CNNs/LSTMs.
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。