Hypertree decomposition approach to planning problems for reduced computational complexity
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Hypertree decomposition approach to planning problems for reduced computational complexity
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Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.
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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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Volatility-based decomposition review. Trigger when: - Evaluating module boundaries against axes of change and business volatility. - Prompt contains: /lowy, volatility, axis of change, design decomposition, boundary.
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Create decomposition directory structure, IA shard skeletons, and all required layer indexes from approved decomposition plan
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Entry point for new work. Determines the best action path or work decomposition (update existing spec, create new spec, mixed decomposition, or no spec needed) and refines ideas through structured dialogue.
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Validate process decomposition and architecture design quality before execution begins. Load when the setup-evaluator agent fires (automatic for agent-chain tasks), or when user says "evaluate this setup", "check the decomposition", "validate the architecture", "is this plan sound", "review the agent design". Catches structural errors, missing knowledge, unrealistic step ordering, and topology mismatches. Does NOT modify — only evaluates.
中文简介正在准备中。当前显示原文说明。
Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.
中文简介正在准备中。当前显示原文说明。
Apply practical DAG decomposition, transitive-edge reduction, and reachability indexing to dense dependency graphs. Use when low width and repeated queries justify preprocessing. NOT for cyclic graphs, one-off graph checks, or exact-minimum-chain requirements.
中文简介正在准备中。当前显示原文说明。
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 jury_rig-architect), specific tool implementation.
中文简介正在准备中。当前显示原文说明。
为主要由AI智能体实施的开发工作制定完成条件,拆分可独立验证的任务,并比较实施前后的评估结果;同时按难度选择模型,记录费用、耗时与重试情况。
Modernize the claude-flow v3 command-line interface by splitting large command files, adding interactive prompts, connecting hooks that run on execution events, and organizing multiple commands into automated workflows with progress reporting.
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Modernize the claude-flow v3 command-line interface by splitting large command files, adding interactive prompts, connecting hooks that run on execution events, and organizing multiple commands into automated workflows with progress reporting.
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Guides code cleanup and restructuring by mapping dependencies, assessing test coverage, and planning small changes. Supports tasks such as extracting functions or renaming symbols, with checks after each step and recovery when verification fails.
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Create diagrams that explain systems, workflows, and data relationships using a consistent brand style. The skill guides diagram selection, layout, and readability, with HTML, SVG, and PNG output and support for importing existing diagram formats.
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Assess whether bonds are expensive, cheap, or fairly priced by comparing yields with government rates and credit risk benchmarks. Combine spread breakdowns, historical context, and interest rate scenarios into tables and a recommendation.
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Explain portfolio performance by separating the effects of sector allocation, stock selection, market factors, and timing. Compare results with a benchmark, assess risk-adjusted returns, and prepare a report showing where excess returns came from.
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Design autonomous AI agents that break goals into tasks, use tools, and review their own work. Covers reasoning and action loops, separate planning and execution, and reliability measures such as cost limits, validation, and logging.
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Find information across connected workplace sources by turning a question into targeted searches. The skill adapts queries for each source, ranks and deduplicates results, and combines findings into an answer about decisions, status, documents, or policies.
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Plan customer retention and customer success staffing for a startup. Break down renewal and churn patterns, group customers by value and fit, and compare service models to guide resource allocation and hiring.
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Review domain code for classes that hold data while business logic lives elsewhere. Using domain-driven design patterns, diagnose problems and refactor objects to keep business rules, state changes, and validation together, with before-and-after diffs.
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Guide feature development from a human-reviewed plan to testable requirements, implementation, and independent verification. Record each requirement with a concrete proof, then have a separate agent check the work and produce a verification report.
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Plan and implement features with a workflow that adapts to their complexity. It connects testable requirements to tasks, tests, and small commits, then checks the finished work against the specification through an independent verification pass.
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Plan complex Blazor UI features by decomposing them into focused components. USE FOR: building a complex Blazor page with multiple sections, planning component decomposition, designing a multi-section dashboard or layout, breaking down a large UI feature into composable components, pages with sidebars and content panels, any page with 3+ distinct visual sections or multiple interacting sub-features, identifying parent-child relationships and data flow. DO NOT USE FOR: creating new Blazor projects or apps from scratch (use create-blazor-project), implementing a single individual component (use author-component), writing component code with parameters and EventCallback (use author-component), or simple single-component pages.
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