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概要と使いどころ
Expert guide for complex form handling with React Hook Form, server-side validation (useActionState + Zod), multi-step wizards, and accessible form patterns / Panduan ahli penanganan formulir kompleks dengan React Hook Form, validasi server-side, wizard multi-langkah, dan pola formulir aksesibel.
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 a Tutorial class with interactive steps and track registration
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 a DemoScript with steps and registration for the Datagrok demo application
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。
Answer a "how do I …" question about the platform with a Gherkin scenario on @datagrok-libraries/bdd that is filmed into a how-to video (grok-bdd guide), so the answer is demonstrated, sent as a video plus numbered steps, and kept as a regression test; the same scenarios, filmed in help mode, are the GIFs of the help pages
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 a Datagrok Compute2 workflow (pipeline configuration with steps, links, and actions)
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。
Practical argument analysis and construction -- how to identify, evaluate, reconstruct, and build real arguments in natural language. Covers argument identification (premises, conclusions, intermediate steps), charity and steel-manning, argument mapping, the distinction between deductive validity and inductive strength, burden of proof, and the rhetorical context in which arguments operate. Use when the goal is to engage with real-world argument rather than to formalize 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。
Systematic materials selection for engineering design using performance indices, Ashby charts, multi-constraint optimization, and case-based reasoning. Covers the five-step Ashby method — function, objective, constraint, free variable, index — with worked examples for stiffness-limited beams, strength-limited ties, thermally shocked vessels, and minimum-cost components. Use when choosing among metals, polymers, ceramics, and composites under competing objectives.
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。
Extract Gherkin scenarios from story markdown files into runnable .feature files and generate step definition stubs. Use when syncing stories to test suites.
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。
发帖前自检"这条内容帮读者省了几步路"时使用;用户问"值不值得发/有没有价值/该不该发这条"时激活。不适用于:已决定要发只需检查结构完备性(用 x-five-piece-checklist)、需要分类内容原型(用 x-content-archetypes)、需要改写表达方式(用 x-three-translations)。Trigger 词:"值不值得发""省了几步""内容价值""worth posting""content value""should I post this""save steps"。
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。
当用户需要从零写一篇完整的销售型文案，或需要诊断一篇已有文案"为什么不卖货"时，调用此 skill。它是元框架，统领标题抓眼球、激发购买欲、赢得信任、引导马上下单四个不可跳过的步骤，并负责组合调度其他子 skill。当用户只需要优化某个局部环节（如只想写好标题）时，不调用此 skill，而是直接调用对应的子 skill。关键触发信号：用户提到"写文案卖货""文案转化率低""帮我写一篇推文/详情页/广告""文案不知道哪里出了问题"。
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 live visual Kanban board and continuity layer for long-running agentic tasks. Nobody wants to read a markdown plan or stare at raw HTML while an agent grinds for an hour, so Plandeck turns the plan into a board that organizes itself. Use it when a task is multi-step, long-running, or needs visual planning. Break the work into cards, declare which card depends on which, and Plandeck computes the critical path, auto-promotes cards to Ready as their dependencies clear, rolls up story-point estimates into an honest percent complete, and names the one next action. Plans, observed transitions, and last-known-good snapshots stay as plain files on disk, so they survive /clear and context resets. After a reset, run plandeck next to recover the single next move and its recent history. If the plan stops parsing, run plandeck doctor before using git. Renders a live board in the browser over SSE. Triggers on plan a project, break down a task, kanban board, task board, visual plan, long-running agent, agentic task, track dependencies, critical path, story-point estimates, what is next, mission journal, recover plan, plandeck.
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。
Compressive Vasomotion Hypothesis (CVH) — vasomotion as a fast compression sweep that collapses ambivalent neural resonances (the Bayesian-blur problem) into a definite state. Use when modeling the ~100ms taṇhā 'grab', precision-weighting as compression forcefulness, or the generative collapse step of vasocomputational active inference.
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 biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.
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。
Store instructional prose once and reference it from multiple skills or agents instead of duplicating it. Use when the same steps, rules, or reference material appear in two or more SKILL.md or agent files in one plugin, when deciding whether a shared doc belongs in the plugin-root docs directory or a single skill's references directory, or when tempted to use a symlink or a ../ path to share a file between components.
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。
Start or complete a specific refactoring task from a task file. Use when a sub-agent needs to pick up a refactoring task, update its status, implement acceptance criteria, and run verification steps.
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。
Synthesis step in the multi-angle technical research pipeline. Receives structured outputs from all four research angles (api-state, ecosystem-research, impact-measurement, codebase-auditor), applies cross-angle signal weighting and conflict resolution, and produces a single synthesized Research section. Invoked by the technical-researcher orchestrator after all angle skills complete. Returns content to the orchestrator — does not write to the backlog.
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。
Works backward from the stated goal through its prerequisite chain — what must exist to reach the goal, then what each of those requires, back to the current state — classifying each prerequisite as AVAILABLE, DERIVABLE, or MISSING, and blocking planning until nothing is MISSING. Use before creating plans, delegating to agents, or defining acceptance criteria. Activates on specs, PRDs, tickets, RFCs, architecture designs, and multi-step engineering tasks. Always pass `#N` or an inline goal — invoking with no argument and no inline context returns BLOCKED. Integrates with CoVe-style planning pipelines.
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。
Where a dispatched step puts its output, and how it signals state upstream.
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。
One-line definitions of development-harness (dh) plugin terminology — RT-ICA, ARL, SAM, the S1-S7 pipeline stage names, Impact Radius — each with a pointer to its canonical source file. Use when a dh skill, agent, or workflow step references a term without defining it, before guessing what the term means from its observed outputs, or when asked what a dh concept or acronym stands for.
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。
Local codebase analysis research angle — derives behavioral contracts, coding conventions, SKILL.md flow insertion points, and agent data availability maps from actual source files. Use when the blocking question is answered by reading the repository: what does this function actually do, what pattern does the codebase use for X, where in this workflow does a new step go, or what data does this agent already have.
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。
Walks a user from one rough content idea to a finished post, platform-adapted variants, and repurposed follow-on assets using a 7-step sequential prompting workflow. Use when the user wants to turn a half-formed idea, frustration, story, or client situation into publishable content quickly and consistently — covering idea extraction, hook generation, outline, draft, humanizer pass, platform adaptation (LinkedIn, Reddit, X/Twitter, newsletter, Telegram), and repurpose engine.
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。
Activates Standard of Excellence enforcement for the current session. Load before starting any task to apply completion standards: finish the whole thing, fix the root cause, ship the complete working result. Blocks partial solutions, workarounds, deferred threads, and invented content limits when the permanent solve is within reach. Triggers: 'do the whole thing', 'boil the ocean', 'standard of excellence', 'finish it completely', before starting any implementation, refactoring, or multi-step task where partial output is a risk. Does NOT apply to: read-only queries, one-line typo fixes, pure knowledge questions, or single-output summarize requests.
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。
Send feedback or bug reports to the Deepline team. Use when the user asks to report feedback or share a session, and proactively when a Deepline Play is disproportionately slow, appears stuck, or an ordinary outcome requires avoidable product steps.
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 controlling the AltirraSDL Atari emulator programmatically — driving the simulator from a script, capturing screenshots from a running Atari, injecting joystick/keyboard input, frame-stepping for deterministic testing, reading or writing CPU/memory/chip state, setting breakpoints or watchpoints, profiling 6502 code, loading symbols, or building automated tests against an Atari binary. Triggers on phrases like "AltirraBridge", "AltirraSDL --bridge", "AltirraBridgeServer", "Altirra Python SDK", "headless Atari emulator", "screenshot the Atari", "automate AltirraSDL", "drive the emulator from Python", "Atari debugger over a socket", "altirra_bridge package", as well as on tasks involving the bridge protocol, the Python `altirra_bridge` package, or the C `altirra_bridge.h` SDK.
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。