スキルを探す

GSD tutorial and command reference. Use when user is new to GSD or asks about 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。

Household-scale nutrition planning that treats meals as a weekly system, not a series of independent decisions. Covers macronutrient balance, the plate method, weekly rotation, leftover architecture, pantry-to-plate translation, and the food-safety floor that every meal plan must meet. Use when building a weekly menu, adapting a diet to a new household member, planning for a week of predictable meals on a budget, or teaching a learner how a kitchen feeds people over 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。

Understanding how algorithmic systems shape what users see, know, and do -- from recommendation feeds to search ranking to credit scoring to hiring software. Covers the mechanics of recommendation systems, algorithmic bias and its sources, personalization's effects on information diets, opacity and accountability, AI limitations (hallucination, confident wrongness), and the human-in-the-loop question. Use when a learner needs to think critically about why particular content reached them.

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。

Media literacy and critical analysis of media messages across platforms and formats. Covers media ecology (McLuhan), encoding/decoding (Hall), news literacy, digital media analysis, propaganda techniques, algorithmic curation, visual rhetoric, and ethical media consumption. Use when analyzing media messages, evaluating news sources, understanding how media technology shapes communication, detecting propaganda, or studying the relationship between medium and message.

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。

Transform a user's builder vision into a complete, executable GSD mission package. Use this skill whenever a user has described what they want to BUILD (a product, tool, feature, system, educational pack, or ecosystem component) and wants it structured for GSD execution. Triggers include: 'structure this for GSD', 'make this into a milestone', 'turn this vision into a mission', 'package this up for Claude Code', 'create the mission files', 'I want to hand this to GSD', 'make me a mission package', or any request to decompose a described system into wave-based executable tasks. Also trigger when the user has a vision doc already written and needs mission decomposition. Prefer this skill over generic document creation whenever the GSD ecosystem is mentioned alongside building something.

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。

Three-dimensional art and sculptural thinking for art education. Covers additive and subtractive sculptural processes, armature construction, modeling in clay, carving principles, casting and moldmaking, assemblage and found-object sculpture, installation art as expanded sculpture, and the conceptual transition from pictorial to spatial thinking. Use when working with three-dimensional media, analyzing sculptural form, understanding spatial composition, or investigating the relationship between sculpture and site.

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。

Observational drawing and visual perception techniques for art education. Covers contour drawing, gesture drawing, negative space, proportion and measurement, value mapping, spatial depth cues, and the cognitive shift from symbolic to perceptual seeing. Use when teaching drawing fundamentals, analyzing observational accuracy, or developing visual literacy in any medium.

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。

Package conversation research into a GSD-ready mission package. Produces a three-stage Vision → Research → Mission pipeline as a LaTeX PDF following GSD/NASA SE methodology, designed to be handed to gsd-skill-creator for execution. Use this skill when the user has been discussing, researching, or brainstorming a topic and then asks to turn it into a research mission, research pack, mission package, or says 'package this as a mission', 'make this a research pack', 'turn this into a mission for skill-creator', 'create a research mission from this', or 'use the research mission skill'. The skill harvests findings, sources, and structure from the current conversation and any prior research, then produces the complete pipeline document. Also trigger if the user asks to 'create a research mission on [topic]' cold — in that case, conduct web research first, then package.

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。

GSD tutorial, command reference, and workflow explainer. Explains what a GSD workflow command does before you run it. Activates when user is new to GSD, asks about commands, wants to understand workflows, or mentions 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。

Manages skill-creator integration with GSD workflows including skill loading, session observation, bounded learning guardrails, and pattern detection. Use this skill whenever: executing any GSD phase (to ensure relevant skills are loaded first), starting or resuming a work session (to check for pending suggestions), the user asks about skills, patterns, or skill-creator status, performing code changes that might represent repeating patterns, the user corrects Claude's output (highest-signal observation), or when skill refinement, creation, or suggestion review is discussed. Critical for maintaining the adaptive learning layer.

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。

Complex systems and emergent behavior — chaos theory, fractals, neural networks, cellular automata, and computability. Analyzes dynamical systems for chaos via Lyapunov exponents, classifies bifurcations, measures fractal dimension, and models emergent phenomena. Use when analyzing chaotic systems, computing Lyapunov exponents, classifying bifurcations, measuring fractal dimension, modeling neural networks, or studying emergent behavior from simple rules.

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。

账号定位阶段做战略选择时用：用不可能三角（内容深度/传播力/变现力不可兼得）决定放弃哪个角，选IP路线（深度+变现，抗周期/高商业化/复利）或泛流量路线（传播力，但转化差不值钱）。Trigger："追粉丝还是做IP""IP还是泛流量""定位怎么选""深度和传播怎么权衡""泛流量不值钱吗""IP vs generic traffic""build IP or chase followers"。不适用：已有定位后的内容生产/选题（→内容创作类skill）；已有粉丝问怎么变现（→x-monetization-pyramid）；找对标账号（→x-benchmark-research）。

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。

用户要找对标账号/分析竞品爆款/找差异化选题时;或问"怎么做AI工具赛道的差异化""how to find benchmarks / differentiate"时。按5步走:找3-5个低粉高爆对标(搜索from:xxx min_faves:10)→分析90天爆款抓隐性需求→学互动策略→拆变现模式→三公式变形(场景细化/人群聚焦/观点反转)出差异。不适用于:已有定位日常发内容(→x-short-content-craft)、纠结选IP还是泛流量(→x-positioning-tradeoff)、检查草稿(→x-five-piece-checklist)、纯信息查询。

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。

用户要起草一条 X 短内容(推文/帖子)、不知道用什么结构或类型时;或问"怎么发一条教人用 XX 的帖""怎么排版推文能让更多人收藏""what structure should I use for a tweet / how to write a tutorial post"时。先按目标指标选四类型之一(故事→涨粉 / 列表→收藏 / 观点→互动 / 教程→忠诚),再用 Hook-Body-CTA 三段骨架起草;资源/工具类可套爆款公式(工具+场景+三步)。不适用于:已有草稿想检查五件套完备性(→x-five-piece-checklist)、想把公告腔改写成帮助式语言(→x-three-translations)、想按读者功能分类预测传播上限(→x-content-archetypes)、纯信息查询(如推文字数上限)。

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。

Schmidhuber's compression progress as intrinsic curiosity reward for learning 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。

Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.

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。

Bengio's causal inference for AI: Interventional reasoning, counterfactuals, and System 2 deep learning. World models with causal 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。

Community knowledge from bivector.net - GA tutorials, discussions, and best practices

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。

Interactive assistant for intercepting, debugging, analyzing and reviewing Claude Code API requests using mitmproxy. Guides setup, certificate configuration, and active traffic inspection via web or CLI interface. Supports learning Claude Code internals, debugging issues, and optimizing API usage.

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。

GitHub interactome explorer for bmorphism/plurigrid ecosystem. Maps collaborations across AlgebraicJulia, Topos Institute, Anthropic, and MCP servers. Use for discovering cobordisms between research communities.

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。

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。

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。