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cccskills

「decompose」の検索結果

179 件 ・ 関連度順

概要と使いどころ

plan-orchestrate

無料日本語概要

複数工程の計画書を読み、各工程に合うECCコマンドを貼り付け用に生成します。工程の識別情報や完了条件を含め、対象範囲を渡せないコマンドは保留として示します。

  • 実装計画をコマンドに変えたいとき
  • 追加・修正・整理のコマンド選び
  • 指定した工程だけを準備したいとき
affaan-m/ECC27.8万2026年10月12日 更新

ulw-loop

無料

A goal-like loop that decomposes work into systematic, evidence-bound ultrawork steps. Use when the user wants a goal loop or durable, checkpointed execution.

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。

code-yeongyu/oh-my-openagent7万2026年10月13日 更新

Full PR lifecycle in a fresh task-owned git worktree: implement via the ulw-loop skill with mandatory evidence-bound manual QA → reviewer-readable English PR → verification loop (CI + Cubic, where Cubic is skipped only when its quota is exhausted) → merge by default → worktree cleanup. Decomposes one task into the smallest atomic, independently-mergeable PRs and builds the independent ones concurrently via one worktree per PR driven by parallel subagents or a team. Unbounded loop: any failing gate sends you back to fix-and-re-QA inside that PR's worktree. Use whenever implementation work needs to land as a PR. Triggers: 'create a PR', 'implement and PR', 'work on this and make a PR', 'implement issue', 'land this as a PR', 'split into atomic PRs', 'parallel PRs', 'work-with-pr', 'PR workflow', 'implement end to end', even when user just says 'implement X' if the context implies PR delivery.

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。

code-yeongyu/oh-my-openagent7万2026年10月13日 更新

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.

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。

sickn33/agentic-awesome-skills4.8万2026年10月13日 更新

Decompose complex tasks, design dependency graphs, and coordinate multi-agent work with proper task descriptions and workload balancing. Use this skill when breaking down work for agent teams, managing task dependencies, or monitoring team progress.

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。

wshobson/agents4.1万2026年10月12日 更新

launch

無料

Shipyard's governed delivery pipeline — converge the mission, synthesize a durable spec, decompose vertical-slice tickets with blocking edges, run the frontier in parallel via team, close with verification, and report with a full decision log. Two entry gates — the yard gate (drydock audit) and the fog gate (an effort whose destination is unclear is routed to /ask-navigator before this pipeline starts). Humans own the checkpoints where there is no unique answer or the error cost is severe; agents continuously run everything repeatable and acceptable-by-evidence.

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。

Yeachan-Heo/oh-my-claudecode4万2026年10月13日 更新

Supply-chain bottleneck arbitrage. Given a super-trend (AI infra, energy transition, defense, semiconductor reshoring, space economy), decompose its physical supply chain down to Layer 2/3 choke points (optics, lasers, InP/SOI substrates, IC substrates, probe cards, specialty fiberglass...) and surface under-the-radar listed companies sitting on each bottleneck. Scores each link on 6 scarcity criteria, applies mandatory valuation gates (PS/PE/safety-margin) via the financial_rigor tool, and Munger-style reverse-validates. Outputs a ranked bottleneck opportunity board. Use when the user wants hidden beneficiaries of a structural trend rather than already-priced leaders.

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。

HKUDS/Vibe-Trading3.5万2026年10月12日 更新

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.

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。

davila7/claude-code-templates3.3万2026年10月12日 更新

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

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。

davila7/claude-code-templates3.3万2026年10月12日 更新

Decompose financial variances into drivers with narrative explanations and waterfall analysis. Use when analyzing budget vs. actual, period-over-period changes, revenue or expense variances, or preparing variance commentary for leadership.

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。

anthropics/knowledge-work-plugins2.9万2026年10月12日 更新

Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or commit/best-case/pipe-only board number — especially when the CRO needs to walk the board through funnel math + cohort ARR + per-stage conversion assumptions without the theatre of a single undefended number. Decomposes pipeline into commit, best-case, and pipe-only tiers; projects cohort-level NRR/GRR to surface leaky cohorts before they show up in the consolidated number; scores per-stage funnel confidence so soft-floor stages get treated differently from high-confidence ones. Every output explicitly names the conversion rate used, the data window, and the weighting choice. For Head of Commercial, RevOps, VP Sales, and CRO at quarterly forecast or board prep. NOT financial close (see finance/financial-analysis). NOT strategic CRO hiring/territory (see c-level-advisor/cro-advisor). NOT pricing (see sibling pricing-strategist).

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。

alirezarezvani/claude-skills2.8万2026年8月30日 更新

Decompose a concept into individual systems, map dependencies, prioritize design order, create the systems index.

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。

Donchitos/Claude-Code-Game-Studios2.6万2026年10月8日 更新

RedTeam

無料

Adversarial analysis deploying parallel expert agents to stress-test ideas, strategies, and plans — decomposes into atomic claims, attacks them, then steelmans and counter-argues, producing severity-ranked findings with remediation. USE WHEN red team, attack idea, counterarguments, critique, stress test, devil's advocate, find weaknesses, break this, poke holes, strongest objection. NOT FOR collaborative debate to find best path (use Council).

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。

danielmiessler/LifeOS1.9万2026年9月4日 更新

Turn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative Gemini review. Use when the user says "refine my approach", "帮我细化方案", "decompose this problem", "打磨idea", "refine research plan", "细化研究方案", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.

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。

wanshuiyin/Auto-claude-code-research-in-sleep1.7万2026年10月7日 更新

Turn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative GPT-6-Astra review. Use when the user says "refine my approach", "帮我细化方案", "decompose this problem", "打磨idea", "refine research plan", "细化研究方案", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.

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。

wanshuiyin/Auto-claude-code-research-in-sleep1.7万2026年10月7日 更新

Turn a vague research direction into a problem-anchored, elegant, frontier-aware, implementation-oriented method plan via iterative Claude review. Use when the user says "refine my approach", "帮我细化方案", "decompose this problem", "打磨idea", "refine research plan", "细化研究方案", or wants a concrete research method that stays simple, focused, and top-venue ready instead of a vague or overbuilt idea.

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。

wanshuiyin/Auto-claude-code-research-in-sleep1.7万2026年10月7日 更新

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.

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。

Orchestra-Research/AI-Research-SKILLs1.4万2026年10月11日 更新

Designs distributed system architectures, decomposes monoliths into bounded-context services, recommends communication patterns, and produces service boundary diagrams and resilience strategies. Use when designing distributed systems, decomposing monoliths, or implementing microservices patterns — including service boundaries, DDD, saga patterns, event sourcing, CQRS, service mesh, or distributed tracing.

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。

Jeffallan/claude-skills1.2万2026年10月4日 更新

Single-agent mission orchestrator. Decomposes a mission into tasks, spawns one worker per task using the default model, verifies exit criteria, and chains tasks with retry. No critic pattern — each worker self-verifies. Simple, fast, works with any model config.

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。

outsourc-e/hermes-workspace6,7152026年9月30日 更新

Decompose large tasks across parallel agents — use for migrations, multi-file refactors, or batch work

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。

nyldn/claude-octopus4,2192026年10月11日 更新

Decompose and execute large changes, migrations, or multi-issue fixes in parallel with quality gates

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。

nyldn/claude-octopus4,2192026年10月11日 更新

矛盾分析法:把复杂问题拆成若干对立面,找出规定其他矛盾的主要矛盾及其主要方面,判定对抗性 / 非对抗性,并据此选择处理方式。当问题头绪多、多个因素互相牵制、优先级不清、根因不明、反复修不好、trade-off 说不清时触发;直接执行类任务或用户已定方案时不触发。 English: Contradiction analysis. Decompose a tangled problem into opposing forces, isolate the principal contradiction and its dominant side, classify it as antagonistic or non-antagonistic, and choose the response accordingly. Trigger on unclear priorities, unknown root causes, recurring failures, or trade-offs that resist explanation; skip for direct execution or when the user has already chosen the solution.

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。

HughYau/qiushi-skill3,8132026年10月1日 更新

ulw-loop

無料

A goal-like loop that decomposes work into systematic, evidence-bound ultrawork steps. Use when the user wants a goal loop or durable, checkpointed execution.

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。

code-yeongyu/lazycodex3,7672026年10月13日 更新

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.

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。

foryourhealth111-pixel/Vibe-Skills3,6762026年8月31日 更新