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概要と使いどころ
Design a checking-for-understanding protocol with specific techniques for each lesson stage. Use when planning systematic comprehension checks during explicit or direct instruction.
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 need-clarification skill. Use only when the user explicitly invokes $task-clarifier. Once activated, keep asking until all three goals are met: the user fully understands their own needs, the AI fully understands the user's needs, and the user confirms the AI's understanding is correct. Do not intervene in task execution unless explicitly invoked.
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 a Check for Understanding (CFU) for a math topic or standard: 1–3 targeted formative items whose distractors come from documented student misconceptions, plus a teacher guide routing each likely response to a specific next instructional step. Load BEFORE asking any clarifying question about grade, standard, or topic. This skill is for math; for other subjects, help as you normally would and don't mention the skill to the user. Triggers on asks to create, write, generate, or design a check for understanding, formative check, quick check, or exit ticket for a math concept or standard, and on implicit signals like "I need to see who got it before I move on." Not a quiz, test, or practice set — a CFU is read for what it reveals, not scored, so a numbered quiz or a bare answer key is a different job. Does not create a lesson (k12-lesson-plan-creation, which brings its own exit ticket), tier one (k12-lesson-differentiation), or prepare a teacher to teach one (k12-lesson-prep).
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。
Quickly analyzes Python repositories to understand their purpose, structure, and setup requirements. Use when Claude needs to onboard to a new Python codebase, understand project structure, identify entry points, determine dependencies, or generate setup instructions. Trigger when users ask to "analyze this Python repo", "understand this codebase", "how do I run this project", "what does this repo do", or provide a Python repository path for quick start guidance.
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 an agent must make TwelveLabs do real video-understanding work: indexing footage, semantic/visual search across an archive, generating descriptions, summaries, chapters, highlights, and tags from video, producing multimodal embeddings, or wiring TwelveLabs into a media-production pipeline (NLE panels, logging, compliance review, metadata). Covers the current model families (Marengo for search/embeddings, Pegasus for video-to-text analysis), the v1.3 Video Understanding API (indexes, assets, tasks, search, analyze, embed), prompt construction for analysis, capability and format limits, pricing/quota math, output quality review (hallucination and timestamp accuracy), and privacy/rights obligations when footage shows real people. Not for generating or editing video pixels — this is analysis and retrieval.
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。
Expert in understanding and navigating legacy codebases. Covers code archaeology techniques, finding hidden knowledge, mapping dependencies, and extracting understanding from code without documentation. Knows how to read the stories that old code tells. Use when "legacy code, old codebase, understand this code, archaeology, what does this do, no documentation, ancient code, " mentioned.
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。
After the learner demonstrates understanding of a concept, present near-transfer and far-transfer challenges. Use to test whether learning is portable or task-specific — this is what separates understanding from familiarity.
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 a checking-for-understanding protocol with specific techniques for each lesson stage. Use when planning systematic comprehension checks during explicit or direct instruction.
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。
Expert knowledge of Folketrygdloven (National Insurance Act) processing in melosys-api. Use when: (1) Understanding FTRL bestemmelser (§2-5 to §2-13), (2) Debugging yrkesaktiv/ikke-yrkesaktiv/pensjonist processing flows, (3) Understanding trygdedekning options and combinations, (4) Investigating vilkår for each bestemmelse, (5) Understanding medlemskapsperiode generation logic, (6) Debugging frivillig vs pliktig membership determination.
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。
Guided codebase exploration for understanding a repo's architecture, patterns, and domain language before contributing. Not issue-specific. builds general understanding. Use when exploring a new repo, wanting to understand how a project works, or preparing to become a regular contributor. Not for learning a framework or language you have never used. Use oss-learn-stack for that.
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。
AI strength recognition — scanning for what is functioning well and understanding why. The complement to heal, which scans for drift and problems. Gratitude builds structural knowledge from working patterns: what you appreciate, you understand; what you understand, you can build on. Use after completing a task successfully, during heal when everything reads as healthy, when confidence is low and needs grounding in evidence, or periodically to counterbalance the natural bias toward problem detection.
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。
Help the user understand something well enough to explain it. Use when the user needs to understand something and it is not yet clear what kind of understanding they need.
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。
[Process] Use when the developer wants something explained or taught, or wants to know HOW TO REVIEW a change — by default the current working tasks + changes in context, or whatever the prompt names (a plan, a subsystem, a decision, a concept, a bug). AI derives WHAT to explain from the prompt and ALWAYS delivers a teacher-and-coach review guide, high level first then detail: what was done, a VISUAL MAP (system flowchart, domain ERD, sequence diagrams), the user stories and business rules with their REAL test-case IDs, a REVIEW PATH naming which files to open first and in what order with an exit criterion per stage, the technical concepts needed to follow it, how it works, why THIS solution, every alternative option with pros/cons, the trade-offs accepted, the blast radius, HOW TO TEST AND DEMO the change, the developer's decision levers, and written challenge prompts that provoke the reader to pressure-test the work. Works at ANY scope — from one line of code to the whole project: it SIZES the target into a scope tier, DECOMPOSES a large one into understanding groups, BREAKS the work into tasks before the first deep read, and ACCUMULATES the report group by group on disk so a huge explanation stays complete instead of thinning out. Writes the full report to a single combined markdown file (plans/reports/understand-*.md) that opens with a detailed summary section — or delivers it in full in chat when Step 3 finds no git-ignored directory for it — and always summarizes it in chat. Regardless of coding level. Never interrogates, never quizzes, never blocks.
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。
Review code for readability, then simplify it to be easy to follow and understand. Use this skill when the user wants to simplify code, improve readability, clean up for handoff, make code skimmable, reduce complexity, refactor for clarity, strip boilerplate, prepare code for future maintainers, or make a codebase easier to pick up. Trigger liberally — any request touching readability, simplification, cleanup, or "make this easier to understand" should use this skill. Also trigger when the user says things like "review this", "clean this up", "tidy this", "make this simpler", "reduce complexity", "flatten this", "strip the noise", or "I want to understand this code quickly next time I open it".
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。
Quickly understand an unfamiliar codebase or project by mapping its architecture, identifying key components and data flows, and surfacing complexity hotspots. Load when the user asks to understand a repo, explain how something works, map the architecture, onboard to a codebase, or explore how components connect. Also triggers on "walk me through this codebase", "how does this project work", "explain the architecture", "what does this repo do", "show me the structure", "onboard me", or any request to build a mental model of a codebase before making changes.
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。
[22] UNDERSTAND. Deep research from all sources — internal (code, git, logs) AND external (web, docs, best practices). Use when choosing libraries, investigating solutions, understanding legal/technical questions, comparing approaches, or anytime you need comprehensive knowledge from both project context and world knowledge. Triggers on "research", "investigate", "find best approach", "what do others do", "compare options", or complex questions requiring multiple sources.
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。
Socratic mentoring for junior developers and AI newcomers. Guides through questions, never answers. Triggers: "help me understand", "explain this code", "I'm stuck", "Im stuck", "I'm confused", "Im confused", "I don't understand", "I dont understand", "can you teach me", "teach me", "mentor me", "guide me", "what does this error mean", "why doesn't this work", "why does not this work", "I'm a beginner", "Im a beginner", "I'm learning", "Im learning", "I'm new to this", "Im new to this", "walk me through", "how does this work", "what's wrong with my code", "what's wrong", "can you break this down", "ELI5", "step by step", "where do I start", "what am I missing", "newbie here", "junior dev", "first time using", "how do I", "what is", "is this right", "not sure", "need help", "struggling", "show me", "help me debug", "best practice", "too complex", "overwhelmed", "lost", "debug this", "/socratic", "/hint", "/concept", "/pseudocode". Progressive clue systems, teaching techniques, and success metrics.
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。
OpenClawのDocker環境で一連の動作を確かめるテストを作成・変更するスキル。模擬サーバーと実サービスを使い分け、送信内容や最終応答まで検証します。
Use when you need a deep-dive explanation of a specific file, function, or module in the codebase
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。
Launch the interactive web dashboard to visualize a codebase's knowledge graph
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 a Figma file via the Figma REST API and generate an interactive design knowledge graph (pages, screens, components, component sets, instances, design tokens) with a kind:"design" dashboard.
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 you need to generate an onboarding guide for new team members joining a project
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 a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.
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。
Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.
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。