「decompose」の検索結果

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。

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。

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。

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。

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。

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。

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。

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。

Autonomous agents are AI systems that can independently decompose goals, plan actions, execute tools, and self-correct without constant human guidance. The challenge isn't making them capable - it's making them reliable. Every extra decision multiplies failure probability.

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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。

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。

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。

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。

Use for Fermi estimation, or when a rough number is needed fast: "roughly how many...", "ballpark the cost", "estimate capacity or market size", "is this even feasible?". Decomposes, uses ranges and sanity-checks. Skip when exact data is available or a precise calculation is wanted.

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。

Prepare and risk-check the next rolling-wave slice for implementation. Use when the user wants to continue a rolling-wave project, draft one bounded slice in the main context by default, use a fresh planning author only when context or discovery cost warrants it, use a fresh preparation reviewer when semantic risk or uncertainty warrants independent pressure-testing, decompose parallel implementation chunks where appropriate, identify agent, human, either, or hybrid execution fit, write or update docs/rolling-wave/{project}/slices/NNN-slug.md, and mark that slice ready even when other slices are active.

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 systems into microservices and apply canonical distributed-systems patterns. Use this skill whenever the user wants to split a monolith into services, design service boundaries, choose between microservices and a modular monolith, implement sagas or the outbox pattern, set up CQRS or event sourcing, configure an API gateway or BFF, add resilience (circuit breakers, retries, timeouts), or implement distributed tracing with OpenTelemetry. Also trigger for distributed transactions, service discovery, event-driven architecture, contract testing with Pact, canary releases, and idempotent consumers.

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 epic into atomic parallelizable tasks, route each to the right skill, and keep the four delivery records straight — issues, STATUS, ROADMAP, CHANGELOG. Use as a meta-router when several skills could apply, and as the baseline for how delivery state is recorded. Trigger at the start of any multi-track epic, when the skill count exceeds ~12, or when the records have drifted from reality.

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。

Pursue an agreed goal to a working outcome without waiting: decompose it, re-prioritise as evidence arrives, branch, gate, get an independent review (a different model or a fresh reviewer subagent — the writer never approves), merge, prune, and bank the operator's decisions in STATUS.md instead of blocking on them. Trigger on "get it done", "don't wait for me", "proceed autonomously", or when an epic or roadmap objective is handed over. Not a close-out: this work set is expected to grow.

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。

大きな自然文構想を feature・architecture・機能間依存へマクロ分解したいとき、ready feature の system-dev-planner package を atomic 登録・binding 別投影したいときに使う。