varch
無料Decompose an IP requirement into submodules, define interfaces, and generate an architecture document plus per-module requirement files and a top-level skeleton
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。
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概要と使いどころ
Decompose an IP requirement into submodules, define interfaces, and generate an architecture document plus per-module requirement files and a top-level skeleton
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。
Decompose an approved implementation plan into self-contained beads issues with zero guesswork. Use when breaking down a plan file into actionable beads tasks.
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。
Decompose an approved implementation plan into self-contained Linear issues with zero guesswork. Use when breaking down a plan into actionable Linear tasks via the MCP server.
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 analysis of competitor patent claims: element decomposition, scope interpretation, equivalents prediction, prosecution history estoppel analysis. Automatically converts PDF/DOCX/DOC patent files to structured Markdown via patent-pdf-to-md skill before analysis. Invoke when user asks to analyze/parse competitor patent claims, decompose claim elements, assess claim scope, or perform 权利要求解析/要素分解/保护范围解读. 竞品专利权利要求深度解析:要素分解、保护范围解读、等同范围预判、审查历史限制分析。自动调用patent-pdf-to-md技能将PDF/DOCX/DOC专利文件转换为结构化Markdown后再进行分析。当用户要求解析竞品专利权利要求、要素分解、保护范围时触发。
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。
Refine a draft prompt before sending to a coding agent: decomposes goal/context/constraints, flags ambiguity, emits a model-agnostic rewrite. NOT for prompts from scratch (mk:brainstorming).
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。
Loads, lists, composes, or validates graded evaluation rubrics: PASS/WARN/FAIL grading with weighted criteria and balanced anchor examples for evaluator-style review.
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。
Set up a parallel agent team for COMPLEX tasks: creates worktree structure, an ownership map, and an initialized task queue. Use when a task is decomposed into parallel subtasks.
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。
Set up parallel agent team for COMPLEX tasks. Creates worktree structure, generates ownership map, and initializes task queue. Use when orchestrator decomposes a task into parallel subtasks, or when asked to "set up team", "parallel setup", or "configure worktrees".
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 refining a draft user prompt before sending it to a coding agent. Decomposes goal/context/constraints/acceptance/output-format, detects ambiguity and model-coupled framing, then emits a model-agnostic rewrite. Supports --analyze, --score, and --deep. NOT for prompts from scratch (mk:brainstorming), plans/reviews (mk:elicit), implementation plans (mk:plan-creator), or general codebase scouting (mk:scout).
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 loading, listing, composing, or validating graded evaluation rubrics — PASS/WARN/FAIL grading with weighted criteria and balanced PASS/FAIL anchor examples. Triggers on /mk:rubric, "load rubric", "compose rubric preset", "validate rubric", or evaluator-style code review that needs anchor-grounded judgments.
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。
ubuntu1 배포/재배포/롤아웃, second-brain 이미지 빌드·compose up·마이그레이션 배포를 할 때 사용. rsync 경로·이미지 태그·실행 이미지 ID 대조·백업까지 체크리스트로 강제한다.
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。
Auto-decompose large tasks into DAG-compatible parallel subtasks
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。
Draft professional emails for various contexts including business, technical, and customer communication. Use when the user needs help writing emails or composing professional messages.
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。
Decompose DAGs into execution chains using width bounds, stratification, virtual nodes, and matching. Use when minimizing workflow lanes in dependency graphs. NOT for cyclic graphs, generic scheduling, or one-off queries with no preprocessing payoff.
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。
Choose, compare, and decompose work into the right coordination topology: dag, workflow, manager-driven team, swarm, blackboard, team-builder, or recurring loop. Use when the coordination shape is unclear, when you need topology-native breakdown instructions instead of a generic DAG, or when planning topology and runtime topology may differ. NOT for actual execution, node-level skill matching, or framework/tool installation.
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。
Builds, validates, schedules, and dynamically modifies DAG execution graphs. Decomposes problems into nodes with dependencies, performs topological sorting, detects cycles, resolves conflicts, and schedules wave-based parallel execution. Use when designing a DAG structure, validating dependencies, planning execution order, or modifying a DAG at runtime. Activate on "build DAG", "plan workflow", "DAG dependencies", "topological sort", "schedule execution", "modify DAG", "replan". NOT for executing DAGs (use dag-runtime), validating outputs (use dag-quality), or matching skills to nodes (use dag-skills-matcher).
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。
Capstone/orchestration skill — build an M-Agent + N-Human cooperative IDE in Rust gpui (the Harbor): many agents and humans co-editing the same files as co-equal CRDT replicas, governed by claims/guard/salvage, across LAN/shared/remote harbors. The INDEX that dispatches into the sibling rust skills. Use when building the collaborative editor, the agent-fleet console, multiplayer editing with agents-as-peers, or any slice of the Harbor. Trigger on: cooperative IDE, collaborative editor, multiplayer editor, agents and humans co-editing, gpui IDE, Loro CRDT editor, harbor editor, claims/salvage, "build the cooperative IDE". NOT for: a single non-collaborative gpui screen (compose the siblings directly), web editors, or non-editor apps.
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。
Android background task specialist for WorkManager, Foreground Services, Doze mode, and battery optimization. Activate on: WorkManager, background task Android, Foreground Service, Doze mode, battery optimization, periodic work, background sync Android, JobScheduler. NOT for: iOS background tasks (use swiftui-data-flow-expert), React Native background (use mobile-offline-sync-architect), UI architecture (use jetpack-compose-navigation-expert).
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。
Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.
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。
Compose multi-panel publication figures with patchwork, cowplot, gridExtra (R), or matplotlib GridSpec/subfigures (Python) including shared axes/legends/guides collection, panel labels in Nature/Cell convention, and journal-spec sizing. Covers patchwork ≥1.2.0 axes='collect' feature, Type-42 font embedding, and the cairo_pdf save path. Use when composing 2+ subpanels into a single figure for journal submission.
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。
Decompose total effects into direct and indirect paths through mediators using mediation, CMAverse 4-way, HIMA/HIMA2 high-dimensional, BAMA, two-step / MVMR mediation, or double-ML medDML. Use when testing whether a molecular phenotype (expression, methylation, protein) mediates a treatment-outcome relationship, decomposing exposure-mediator interaction via VanderWeele 4-way, screening high-dimensional EWAS mediators, or running MR-based mediation when sequential ignorability is implausible.
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。
Decompose an approved spec into a dependency-ordered swarm plan — one task per component, each with an explicit write_set. Produces `.claude/state/swarm/<slug>.json` with tasks + waves. The wave scheduler guarantees pairwise-disjoint write_sets within each wave so parallel dispatch is provably conflict-free.
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。
Derive an execution roadmap from a project's vision/spec docs by first principles, then diff that fresh derivation against the existing roadmap to prove the task ORDER is correct. Use this whenever the user wants to review, validate, re-derive, or sanity-check an execution roadmap or delivery plan; asks whether the tasks/epics are "in the right order"; suspects a task is scheduled before its prerequisite (a producer-after-consumer / dependency-inversion error like "OpenAPI after the web client that consumes it", OR a cross-cutting inherited default like observability/auth/tenancy scheduled after the feature work that inherits it — a seam-after-consumer error); wants to find missing tasks, circular dependencies, or over-fragmented epics; or wants to compact a plan to raise velocity. Also trigger on "review the roadmap", "re-derive the plan from the spec", "is this the right build order", "check the dependency graph of our epics", "which task should come first". It reads the vision + spec corpus, decomposes actors → use-cases → modules → epics → typed tasks, identifies cross-cutting inherited-default seams, builds a real dependency graph with hard + soft (seam) edges (deterministic helper), breaks cycles, topologically orders, compacts, and emits a derived roadmap PLUS a delta report against the current one — it never blindly overwrites a hand-crafted roadmap.
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。
Decompose an MVP vision into a sprint manifest — a prioritized feature list where every feature carries explicit done-criteria (a done-record reference, named edge tests, and a wiring test). Produces the sprint manifest that `sprint-oracle` checks for completeness. Use when planning a sprint of parallel work (Slice A of the sprint-mode epic). Not a workflow phase; the manifest it writes is the completeness contract a sprint is held to.
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。