Results for “decompose”

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。

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Decompose an IP requirement into submodules, define interfaces, and generate an architecture document plus per-module requirement files and a top-level skeleton

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Goal-like loop that uses ultrawork mode to decompose work into systematic, evidence-bound steps.

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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.

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Breaks work into ordered, parallel-dispatchable tasks with an execution DAG. Output format is consumable by CI matrices and parallel agent runners — each task has a stable ID, declared file writes, conflict edges, and branch suffix. Use when you have a spec, brief, or shaper output and need to decompose it into implementable units. Use when a task feels too large, when scope spans multi-repo or multi-week work, or when parallel execution across multiple agents is on the table.

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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.

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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).

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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.

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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.

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Decompose an approved implementation plan into self-contained beads issues with zero guesswork. Use when breaking down a plan file into actionable beads tasks.

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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.

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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.

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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).

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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.

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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".

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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).

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Auto-decompose large tasks into DAG-compatible parallel subtasks

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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.

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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.

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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.

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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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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.

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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.

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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.

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