Results for “decompose”

Orchestrator - spawns an autonomous orchestrator agent that decomposes a task into subtasks and drives multiple Claude agents to completion. Use when: overmind, orchestrate, multi-agent, delegate, spawn agents, break down task, run agents.

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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.

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Split user stories into smaller, independently deliverable slices using the SPIDR method (Spike, Path, Interface, Data, Rules). Use this skill whenever the user asks to split a story, break down work, slice a story, asks "can this be split", mentions "SPIDR", says "story splitting", or wants to decompose a large ticket into smaller pieces. Also trigger when a user presents a story that seems too large for a single sprint and asks for help sizing or scoping it.

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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。

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Execute a pre-existing plan file by implementing tasks sequentially with test verification after each step. Use when plan/tasks are already decomposed and ready for implementation.

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Analyze task scope and recommend execution strategy (sub-agent, multi-session, or scope cut). Orchestrates existing skills like /decompose, /task-enrich, and /task-handoff.

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Use to decompose an ADR item into an authored OBPI brief.

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Search academic references for a manuscript and export them to a verified refs CSV, then optionally hand off to cec-sheet. Claims are decomposed into conditions that define when to stop; references are found by keyword search (Mode A targeted / Mode B agentic via Codex CLI, run as two independent search routes) and by citation chaining (Mode C — backward refs, forward citations, similar articles, same author, and reference lists of OA full text/PDF, imozuru-style). All paths end in bundled deterministic tools (scripts/pubmed_search.py, scripts/citation_chain.py) that fetch bibliography live from PubMed, so PMIDs are never fabricated, and each supporting sentence is machine-verified verbatim against the fetched abstract. Triggers: "論文検索", "文献検索", "文脈検索", "コンテキスト検索", "原稿の引用を探して", "原典を探して", "芋づる", "引用をたどって", "被引用", "孫引き", "refs.csv", "codex-refs", "/codex-refs".

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동일한 결과를 내는 여러 조건문(OR 나열·중첩 AND)을 하나로 통합하고 의미 있는 boolean 메서드로 추출. "조건문 합쳐", "같은 결과 반환하는 if 정리", "중첩 if 평탄화", "/consolidate-conditional" 요청 시 사용. 단, 여러 메서드에 흩어진 동일 조건을 호출자 쪽으로 올리는 것은 /lift-up-conditional, 복잡한 조건식·분기를 메서드로 쪼개는 것은 /decompose-conditional이 적합. /consolidate-conditional [commit-ref]로 호출.

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타입·상태별로 반복되는 switch/if-else 분기를 다형성(인터페이스 + 구현 클래스)으로 치환. "switch를 다형성으로", "타입 분기를 클래스로", "strategy 패턴", "/replace-conditional-with-poly" 요청 시 사용. 단, null 분기만이면 /introduce-special-case, 조건식 가독성만이면 /decompose-conditional이 적합. /replace-conditional-with-poly [commit-ref]로 호출.

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Knowledge base from NASA-STD-8729.1A — NASA's R&M (Reliability and Maintainability) standard covering spaceflight and support systems (Revision A, 2017-06-13). Use for NASA's objectives-driven R&M framework — the R&M objectives hierarchy (one Top Objective decomposed into four sub-objectives, paired with tailorable strategies), how R&M requirements are established inside the SMA Plan required by NPR 7120.5, the SMA Technical Authority concurrence/independent-evaluation governance, milestone vs. readiness review gates, the reliability/maintainability/availability vocabulary (Ai vs. Ao, the failure causal chain, risk as a triplet), and the R&M Evidentiary Methods catalogue (FMEA/FMECA, FTA, RBDA, RCM, LORA, plus the space-environment and parts-pedigree analyses). Scoped to NASA spaceflight and support systems and to assurance planning: it is an objectives-and-strategies standard, so it deliberately does NOT prescribe step-by-step analysis procedures (those live in the referenced NASA Preferred Reliability Practices) and is thin on facility R&M, detailed math/derivations, and non-NASA or commercial life cycles.

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Knowledge base from the DoD Test & Evaluation Enterprise Guidebook (OUSD R&E, August 2022). Use for DoD test and evaluation (T&E) across the Adaptive Acquisition Framework — the four test communities (contractor testing, developmental DT&E, operational OT&E, live-fire LFT&E); the five operational events (Operational Demonstration, EOA, OA, IOT&E, FOT&E) and the OTRR gate; the T&E Strategy / TEMP and its IDSK, with the upstream documents that feed it and the test plans that decompose it; the T&E organizations and oversight roles (USD(R&E), DOT&E, TRMC, the Service Executives and OTAs, the T&E WIPT, CDT); and pathway-specific T&E for all six acquisition pathways (Urgent Capability, Middle Tier, Major Capability, Software, Defense Business Systems). Statutory anchors include 10 U.S.C. §4171 (IOT&E) and §4172 (LFT&E/FUSL). This is guidance, not policy: it points outward to DoDI 5000.89 and the DoDI 5000-series for binding detail, and is thin on detailed test statistics/design-of-experiments methodology, cost estimating, the Acquisition of Services pathway, and any CUI/CAC-gated companion guides.

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Decomposes a large implementation plan into small-model-sized tasks executed by supervised worker agents in dependency-ordered parallel waves, with a plan-first approval gate inside every task, file-based state for context compaction, and a hard escalation ladder. Use when a master plan or multi-workstream feature is too large for one session and should be executed cheaply by many small agents under frontier-model supervision.

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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'...

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Create decomposition directory structure, IA shard skeletons, and all required layer indexes from approved decomposition plan

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Validate shard load/dependencies, create deep-dive skeletons, update ledger assignments, and generate pipeline tracker

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Orchestrate concurrent analysis or implementation across independent domains. Use when a task can be decomposed into independent workstreams (e.g., via simultaneous tool calls or background processes) — spec writing, code review, security audit, feature implementation.

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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. This skill covers agent loops (ReAct, Plan-Execute), goal decomposition, reflection patterns, and production reliability. Key insight: compounding error rates kill autonomous agents. A 95% success rate per step drops to 60% b

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当机制清单已经排好、需要给每个机制配数值锚点并合成一个站得住的量级结论时使用。把估算写成显式的因子分解，每个因子给低／中／高三值与证据档位，算出区间而不是装饰性的小数位，再与参考区间和守恒量核对。worksheet 的结构与算术由 scripts/check_estimate.py 校验。不负责发现或排序机制——那是 physics-mechanism-decomposer 的事。

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当面对一个开放现象、说不清"为什么会这样"时使用——IYPT 题目、实验里看到的奇怪行为、科研中待解释的观测。把现象拆成候选机制清单，用守恒量收支表检查有没有漏，为每个机制写出带指数的标度律，用同量纲的量级比较排出主导、次导与可忽略，再为每个机制配一个能真正分辨它的观测量（判据是指数或符号不同，不是系数不同）。产出的是可检验命题与实验建议，不是结论。

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Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.

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Generate the success criteria for a task or question, then review work against them. Given a task, goal, or open-ended question, decompose it into scenarios, evaluation perspectives, and fine-grained weighted YES/NO criteria using the Recursive Expansion Tree (RET) method; if work is supplied, score it criterion-by-criterion and surface what is missing or could be better. Use when asked to self-review or check your own work, judge whether a task is done well or completely, build a definition-of-done or completeness checklist, create an evaluation rubric or grading criteria, score or grade answers to a question, set up an LLM-as-judge rubric, or when the user mentions self-review, completeness check, success criteria, evaluation criteria, scoring rubric, Qworld, or the RET algorithm.

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PM planning workflow — analyze requirements, select tech stack, decompose into prioritized tasks with dependencies, and define API contracts

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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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