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Use when explaining a technical concept and adapting depth, pacing, and examples to the learner's readiness.

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 logging, scoring, and triaging a learner's persistent weak areas to drive intervention 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。

Use when guiding a learner through real project implementation while preserving their ownership and teaching through the build process.

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 learner asks to learn from a repository and the agent must map curriculum structure and prerequisites before teaching.

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 structuring a multi-session learning roadmap with milestones, gates, and pacing calibrated to learner level and goal.

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 generating personalized practice challenges calibrated to the learner's weak areas, level, and project context.

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 verifying whether the learner truly understands a concept through reasoning and application, not recall trivia.

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 capturing or restoring a learner's persistent profile to personalize teaching across sessions.

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 concept is too abstract for the learner's current level and needs analogy-first teaching before complexity re-injection.

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。

把书课程化并作为「专业教师」逐课带人类学完：课前备课→详尽费曼教学→练习闸门→间隔复习→背诵作业→自进化。默认处理使用者已提供的本地书（PDF/EPUB/DJVU/MOBI/AZW/DOCX/TXT/MD/CBZ）；**当使用者无本地书且明确授权时，可代为联网检索公开来源并用项目抓取工具获取**。当用户说「学这本书 <本地路径> / 把这本 PDF 课程化 / 教我这本书 / 继续学 <书> / 复习 <书> / 考考我 <书> / 帮我学 X」时调用。教学引擎已单包自包含，无需再加载其他技能。

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。

Web search with full page content extraction. Use this skill whenever the user asks to search the web, find articles, research a topic, look something up, find recent news, discover sources, or says "search for", "find me", "look up", "what are people saying about", or "find articles about". Returns real search results with optional full-page markdown — not just snippets. Provides capabilities beyond Grok's built-in WebSearch.

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。

Chrome DevTools MCPを使ってWeb performanceを分析する。Core Web Vitals（LCP、INP、CLS）とFCP、TBT、Speed Indexを計測し、render-blocking resources、network dependency chains、layout shifts、cache、accessibilityの問題を特定する。表示速度の監査・計測・デバッグ・最適化、Lighthouse scoreの改善で使用する。既定スタックの必須スキルではないoptional群であり、要件で明示された場合、またはapp-orchestratorがv1前の計測として指示した場合のみロードする。事前学習の知識より最新の公式ドキュメントを優先する。

過去の仕訳日記帳から仕分けパターンを確認・更新し、wikiのパターンページを保守する手順

Analyze folders of Markdown/text research notes, Obsidian vaults, paper cards, invention notes, or memory exports as a practical knowledge map. Use when asked to find center topics, noisy or low-signal notes, possible contamination, overgrown topic groups, orphan notes, cleanup actions, or a useful public knowledge-hygiene workflow.

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。

Lightweight multi-agent research orchestration using one coordinator, three domain leads, and three lightweight research agents. Use for medium-size research, market scans, competitor comparisons, prior-art style exploration, report planning, and any task that benefits from parallel domain decomposition without running a full high-cost research lab.

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。

Teach software development through active project work while still producing a working result, maintaining bilingual English and Chinese project documentation, protecting private context, and publishing reviewed updates to GitHub only after confirmation. Use when Codex helps the user create, build, extend, debug, refactor, document, publish, or understand a software project and the user's goal includes learning, mentorship, guided implementation, pair programming, understanding the code, or becoming able to do similar work independently.

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。

Conduct reproducible web searches and decision-oriented research across official sites, search engines, news, papers, forums, social platforms, and public datasets. Use when Codex must investigate a person, organization, product, market, technical resource, reputation, career choice, or other current topic; compare candidates; verify claims; collect dated citations; distinguish facts from inference and anecdote; or write an evidence-backed research report.

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 research workflow that starts from a GitHub repo URL/slug, pulls upstream intel, then expands to Reddit + open web for community reception. Use whenever Ahmad gives a GitHub link and asks to understand it, see updates, audit the community, or compose a report. Trigger phrases include "research this repo", "report on this github", "what's happening upstream", "community reception", "compose a report on <repo>", and any message that contains a github.com URL plus a research/report ask.

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。

Multi-angle deep-research workflow over Ahmad's OpenMark knowledge graph (13k+ bookmarks). Use whenever Ahmad asks anything that requires more than one search to answer well — "what's the landscape of X in my saves", "do a deep dive on Y", "research X across my bookmarks", "compare what I saved about X vs Y", "give me everything on X". Also use when fast-search returned thin results and Ahmad pushed back ("dig deeper", "more results", "actually research it"). Pairs Neo4j graph search with WebFetch on top picks to bring back fresh page content. Triggers on "deep research", "dig deeper", "research", "landscape", "everything on", "thorough", "comprehensive".

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。

Manus-style file-based planning for multi-step work. Keep task_plan.md, findings.md, progress.md in your run workspace so the filesystem is your working memory. Use for any research/build with 5+ tool calls. Adapted for OpenMark sub-agents from Ahmad's planning-with-files v3.0.0.

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。

You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.

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。

Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code spe...

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。

Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.

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。

Agente que simula Andrej Karpathy — ex-Director of AI da Tesla, co-fundador da OpenAI, fundador da Eureka Labs, e o maior educador de deep learning do mundo.

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。