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Executes DAG waves with controlled parallelism using the Task tool. Manages concurrent agent spawning, resource limits, and execution coordination. Activate on 'execute dag', 'parallel execution', 'concurrent tasks', 'run workflow', 'spawn agents'. NOT for scheduling (use dag-task-scheduler) or building DAGs (use dag-graph-builder).
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Operations, debugging, and optimization for DAG workflows. Performs root cause analysis on failures, profiles execution performance, aggregates results from parallel branches, bridges context between nodes, and learns patterns from execution history. Activate on "DAG failed", "why did it fail", "root cause", "performance profile", "aggregate results", "merge branches", "execution patterns", "optimize DAG". NOT for planning DAGs (use dag-planner), executing DAGs (use dag-runtime), or validating outputs (use dag-quality).
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Debugs recorded DAG lineage through historical inspection, deterministic decision replay when prerequisites hold, and clearly labeled counterfactual or live re-execution. Activate on "debug DAG", "replay execution", "inspect node state", "compare runs", or "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.
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Redux state for agent execution and firing agent shortcuts. Use when editing features/agents/redux/, building agent UI, creating a conversation, touching assembleRequest or NDJSON stream state, wiring agent-state selectors, adding a per-conversation capability, or triggering a shortcut from a button, menu, or code (useShortcutTrigger, launchAgentExecution).
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Agent-to-Agent (A2A) executor implementation patterns for task handling, execution management, and agent coordination. Use when building A2A executors, implementing task handlers, creating agent execution flows, or when user mentions A2A protocol, task execution, agent executors, task handlers, or agent coordination.
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Detect suspicious PowerShell execution patterns including encoded commands, download cradles, AMSI bypass attempts, and constrained language mode evasion.
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Parse Windows Prefetch files using the windowsprefetch Python library to reconstruct application execution history, detect renamed or masquerading binaries, and identify suspicious program execution patterns.
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Parses and analyzes the Windows Amcache.hve registry hive to extract evidence of program execution, application installation, and driver loading for digital forensics investigations. Uses Eric Zimmerman's AmcacheParser and Timeline Explorer for artifact extraction, SHA-1 hash correlation with threat intel, and timeline reconstruction. Activates for requests involving Amcache forensics, program execution evidence, Windows artifact analysis, or application compatibility cache investigation.
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Parse Windows Prefetch files to determine program execution history including run counts, timestamps, and referenced files for forensic investigation.
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Complete OKR & Strategy Execution system — from company vision to weekly execution. Covers goal hierarchy, OKR writing methodology, scoring rubrics, alignment cascading, KPI dashboards, review cadences, team accountability, and quarterly planning rituals. Use when setting goals, running planning cycles, tracking OKRs, building KPI dashboards, running retrospectives, or aligning team work to strategy. Trigger on: "OKR", "objectives", "key results", "goal setting", "quarterly planning", "KPIs", "strategy execution", "annual planning", "team goals", "alignment", "review cadence", "what should we focus on", "prioritize", "goal tracking", "north star metric".
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通过暂停Node.js执行、检查变量和跟踪执行路径，调查异步任务停滞、测试偶发失败等问题，也可分析内存持续增长和CPU负载的原因。
帮助应用迁移到Swift 6.2并设计并发代码，通过MainActor保护共享状态，解决数据竞争编译错误，梳理协议遵循，并将高负载计算显式移至后台执行。
对GitHub议题和拉取请求进行分类，将计划执行的工作关联到Linear，整理公开进展、内部负责人、优先级及下一步行动。
将大型功能的RFC设计提案拆分为带依赖关系和验收测试的工作单元，组织多个智能体完成调查、实现、测试与审查，再依次合并变更并验证整个系统。
从指令、记忆、工具执行和界面显示等十二个层面排查人工智能代理故障，依据代码和日志梳理原因，按严重程度列出问题并提出修复方案。
分析代码并生成可在浏览器中探索的交互式关系图，查看函数、调用链和执行流程，支持切换多个仓库，也可通过Cloudflare远程分享。
帮助将常用任务指令整理为Claude Code斜杠命令，涵盖参数传递、文件引用、执行工具指定，以及包含用户交互的流程编排。
查看 Caveman Cloud 实验状态和评估结果，整理启动、批准、取消或回滚的判断依据。根据证据提出建议，并阻止执行状态变更。
以只读方式检查Caveman Cloud的费用、响应时间和错误，对照汇总数据与单次执行记录，依据证据整理支出构成及可改进之处。
为native Paperclip Runner接入新的执行工具或提供商，从需求整理、通信协议调查到实现、打包，以及在实际环境中验证完整流程。
为Paperclip新增或扩展贯穿真实浏览器、服务器、数据库和runner的端到端评估，整理测试数据、判定条件与证据，并在现有报告中查看操作结果。
在 Paperclip 中梳理首次工作的目标、限制条件和完成标准，提出计划或单项任务方案，等待批准后，再按约定范围开展工作。
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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Use the command line to send tasks to OmniRoute's A2A server, which supports communication between agents. Inspect execution history and logs, monitor or cancel tasks, and interactively test the JSON-RPC 2.0 communication protocol.
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