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概要と使いどころ
Analyze the current session and propose improvements to skills. **Proactively invoke this skill** when you notice user corrections after skill usage, or at the end of skill-heavy sessions. Also use when user says "reflect", "improve skill", or "learn from this".
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。
Daily morning research note for A-share markets. Summarizes overnight developments, pre-market sentiment, trade ideas, and key events for the trading day. Adapted from the original morning-note skill for Chinese market conventions. Triggers on "早报", "晨会纪要", "今日策略", "morning note", "morning meeting", or "daily A-share preview".
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。
Triage improvement work and run or resume accepted measured loops in a local project. Architecture, documentation, UX, product study, open research, taste, and one-shot fixes stay direct unless the user explicitly requests repeated measurement with a complete experiment contract.
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 wants to learn Godot while building — teaching mode that explains the concept, the editor setup, and what to verify, instead of just delivering code. Triggers on "teach me", "explain as we go", "I'm learning Godot", "guide me", "walk me through", "help me understand".
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。
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
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。
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems.
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 adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.
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 persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。
visionOS widget patterns including mounting styles, glass/paper textures, proximity-aware layouts, and spatial widget families. Use when creating or adapting widgets for visionOS.
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。
Deep market analysis for iOS/macOS apps including market sizing (TAM/SAM/SOM), growth trends, market maturity, entry barriers, distribution channels, and revenue potential. Use when user asks for market research, market size, market opportunity, growth potential, TAM/SAM/SOM, or market trends.
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。
Deep competitive analysis for iOS/macOS apps including feature comparison, pricing analysis, strengths/weaknesses, market positioning, and differentiation opportunities. Use when user asks for competitive analysis, competitor research, feature comparison, market positioning, or wants to understand competition in detail.
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 wants to optimize configurable system parameters against a measurable scalar objective, especially for model training, inference, quantitative strategies, reinforcement learning, scientific workflows, or other expensive black-box evaluations where reading the project can improve trial 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。
ProteoDisco is an R package to facilitate proteogenomics studies. It houses functions to create customized (variant) protein databases based on user-submitted genomic variants, splice-junctions, fusion genes and manual transcript sequences. The flexible workflow can be adopted to suit a myriad of research and experimental settings.
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。
A post hoc cell type classification tool to fine-tune cell type annotations generated by any cell type classification procedure with semi-supervised learning algorithm AdaSampling technique. The current version of scReClassify supports Support Vector Machine and Random Forest as a base classifier.
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。
A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST API. Only for academic use by academic users belonging to academic institutions (see <https://www.kegg.jp/kegg/rest/>). Note that KEGGREST
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。
Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide Association Study. The "ggmanh" package aims to keep the generation of these plots simple while maintaining customizability. Main functions include manhattan_plot, qqunif, and thinPoints.
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。
Codex-only — use when the user asks to run claude-smart commands in Codex, including claude-smart show, learn, restart, dashboard, clear-all, or slash-like requests such as /claude-smart:learn. In Claude Code the native plugin slash commands handle these; do not invoke this skill there.
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。
系统化学习材料生成器。给一个新领域/技术/概念，AI 自主调研、搭体系、产出 HTML 学习材料（含骨架/案例/工程化/争议+盲区）。当用户说"我要学 X / 帮我系统拆解 X / 我想吃透 X 这个领域 / 给我整理 X 的全貌 / 深度调研 X 给我系统讲讲 / 把 X 搞透"时触发。不适用于"X 行不行/为什么 Y"（使用可用的调研工具直接研究）、"X 有哪些好玩案例"（用 case-radar 给散点）、"把这堆素材整理成 HTML"（用 readable-output 处理已有素材）、文章写作（用 writing-assistant）、整页界面方案（用 page-solution-design）。
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。
带用户精读一篇文章/文档并真正学透（不是出摘要）。五步：抽干货 → 切讲次 → 每讲跑「学-考-讲」循环 → 对号入座 → 实操+测验+讲错题+蒸馏。每讲产出 HTML 课件 + 笔记落盘；讲完必考一次，用户复述后必须挑不精确处拧紧；抽象概念上可交互演示（能点能跑）；全程用用户自己的业务场景当案例；学完出多题型自动判分测验卷，最后把收获蒸馏回用户的工具。核心触发条件是用户要的是"学会"而不是"要一份结果"，例如"我们一起学这篇文章/这个链接"、"带我学"、"精读"、"我想学会 X"、"这篇我看不懂你给我讲讲"。有具体材料（链接、本地文件、PDF，或用户自己的 skill/文档/代码）时直接开跑；只有学习意图而没材料时仍走本 skill，但开工第一件事是和用户一起把材料定下来，禁止凭记忆开讲。不适用于：只要一份总结/摘要/教程长文，用户读完就完、不需要答题（用 readable-output）、只是搜集资料做调研（使用可用的网页/平台取材工具）、帮我写 PRD/测试用例（用 prd-test-writer）、以及用户其实是想让你直接把活干了（那就直接做）。
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 creating tailored resumes for job applications - researches company/role, creates optimized templates, conducts branching experience discovery to surface undocumented skills, and generates professional multi-format resumes from user's resume library while maintaining factual integrity
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。
Digest of the most interesting new posts on [REPLACE: TOPIC] from RSS feeds and the open web
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。
Cross-platform social research - narrative-first intelligence on what people are saying about a topic across Reddit, X, HN, Polymarket, and the web over the last 30 days
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。
Evolve a skill by generating variations, evaluating them, and updating the best version
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。