[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JPXCBADS4D162F9ESSF6P0":3,"skill-same:01M6JPXCBADS4D162F9ESSF6P0":91},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":51,"ownerHtmlUrl":52,"ownerBlogUrl":53,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M6JPXCBADS4D162F9ESSF6P0","orchestrate_batch_refactor",null,"Plan and execute large refactors with dependency-aware work packets and parallel analysis.","LongLeo287\u002FOmniClaw","LongLeo287","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F141803367?v=4","01M6JK75C62VCNZKR5BRZ386YV","LongLeo","01M6JK75GQ78TSP1ZPXNKK3MCH",9,"MIT","https:\u002F\u002Fgithub.com\u002FLongLeo287\u002FOmniClaw\u002Fblob\u002Fmain\u002Fecosystem\u002Fskills\u002Forchestrate_batch_refactor\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000007","代码审查","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38],"batch","orchestrate","refactor","orchestrate batch refactor","safe","analysis","plan","parallel","work","execute","dependency","with","2026-11-10 10:12:30",false,"---\nname: \"orchestrate_batch_refactor\"\n\ndescription: \"Plan and execute large refactors with dependency-aware work packets and parallel analysis.\"\nrisk: safe\nsource: \"Dimillian\u002FSkills (MIT)\"\ndate_added: \"2026-03-25\"\n---\n\n# Orchestrate Batch Refactor\n\n## Overview\n\nUse this skill to run high-throughput refactors safely.\nAnalyze scope in parallel, synthesize a single plan, then execute independent work packets with sub-agents.\n\n## When to Use\n\n- When a refactor spans many files or subsystems and needs clear work partitioning.\n- When you need dependency-aware planning before parallel implementation.\n\n## Inputs\n\n- Repo path and target scope (paths, modules, or feature area)\n- Goal type: refactor, rewrite, or hybrid\n- Constraints: behavior parity, API stability, deadlines, test requirements\n\n## When to Use Parallelization\n\n- Use this skill for medium\u002Flarge scope touching many files or subsystems.\n- Skip multi-agent execution for tiny edits or highly coupled single-file work.\n\n## Core Workflow\n\n1. Define scope and success criteria.\n   - List target paths\u002Fmodules and non-goals.\n   - State behavior constraints (for example: preserve external behavior).\n2. Run parallel analysis first.\n   - Split target scope into analysis lanes.\n   - Spawn `explorer` sub-agents in parallel to analyze each lane.\n   - Ask each agent for: intent map, coupling risks, candidate work packets, required validations.\n3. Build one dependency-aware plan.\n   - Merge explorer output into a single work graph.\n   - Create work packets with clear file ownership and validation commands.\n   - Sequence packets by dependency level; run only independent packets in parallel.\n4. Execute with worker agents.\n   - Spawn one `worker` per independent packet.\n   - Assign explicit ownership (files\u002Fresponsibility).\n   - Instruct every worker that they are not alone in the codebase and must ignore unrelated edits.\n5. Integrate and verify.\n   - Review packet outputs, resolve overlaps, and run validation gates.\n   - Run targeted tests per packet, then broader suite for integrated scope.\n6. Report and close.\n   - Summarize packet outcomes, key refactors, conflicts resolved, and residual risks.\n\n## Work Packet Rules\n\n- One owner per file per execution wave.\n- No parallel edits on overlapping file sets.\n- Keep packet goals narrow and measurable.\n- Include explicit done criteria and required checks.\n- Prefer behavior-preserving refactors unless user explicitly requests behavior change.\n\n## Planning Contract\n\nEvery packet must include:\n\n1. Packet ID and objective.\n2. Owned files.\n3. Dependencies (none or packet IDs).\n4. Risks and invariants to preserve.\n5. Required checks.\n6. Integration notes for main thread.\n\nUse [`references\u002Fwork-packet-template.md`](references\u002Fwork-packet-template.md) for the exact shape.\n\n## Agent Prompting Contract\n\n- Use the prompt templates in [`references\u002Fagent-prompt-templates.md`](references\u002Fagent-prompt-templates.md).\n- Explorer prompts focus on analysis and decomposition.\n- Worker prompts focus on implementation and validation with strict ownership boundaries.\n\n## Safety Guardrails\n\n- Do not start worker execution before plan synthesis is complete.\n- Do not parallelize across unresolved dependencies.\n- Do not claim completion if any required packet check fails.\n- Stop and re-plan when packet boundaries cause repeated merge conflicts.\n\n## Validation Strategy\n\nRun in this order:\n\n1. Packet-level checks (fast and scoped).\n2. Cross-packet integration checks.\n3. Full project safety checks when scope is broad.\n\nPrefer fast feedback loops, but never skip required behavior checks.\n","# Orchestrate Batch Refactor\n\n## Overview\n\nUse this skill to run high-throughput refactors safely.\nAnalyze scope in parallel, synthesize a single plan, then execute independent work packets with sub-agents.\n\n## When to Use\n\n- When a refactor spans many files or subsystems and needs clear work partitioning.\n- When you need dependency-aware planning before parallel implementation.\n\n## Inputs\n\n- Repo path and target scope (paths, modules, or feature area)\n- Goal type: refactor, rewrite, or hybrid\n- Constraints: behavior parity, API stability, deadlines, test requirements\n\n## When to Use Parallelization\n\n- Use this skill for medium\u002Flarge scope touching many files or subsystems.\n- Skip multi-agent execution for tiny edits or highly coupled single-file work.\n\n## Core Workflow\n\n1. Define scope and success criteria.\n   - List target paths\u002Fmodules and non-goals.\n   - State behavior constraints (for example: preserve external behavior).\n2. Run parallel analysis first.\n   - Split target scope into analysis lanes.\n   - Spawn `explorer` sub-agents in parallel to analyze each lane.\n   - Ask each agent for: intent map, coupling risks, candidate work packets, required validations.\n3. Build one dependency-aware plan.\n   - Merge explorer output into a single work graph.\n   - Create work packets with clear file ownership and validation commands.\n   - Sequence packets by dependency level; run only independent packets in parallel.\n4. Execute with worker agents.\n   - Spawn one `worker` per independent packet.\n   - Assign explicit ownership (files\u002Fresponsibility).\n   - Instruct every worker that they are not alone in the codebase and must ignore unrelated edits.\n5. Integrate and verify.\n   - Review packet outputs, resolve overlaps, and run validation gates.\n   - Run targeted tests per packet, then broader suite for integrated scope.\n6. Report and close.\n   - Summarize packet outcomes, key refactors, conflicts resolved, and residual risks.\n\n## Work Packet Rules\n\n- One owner per file per execution wave.\n- No parallel edits on overlapping file sets.\n- Keep packet goals narrow and measurable.\n- Include explicit done criteria and required checks.\n- Prefer behavior-preserving refactors unless user explicitly requests behavior change.\n\n## Planning Contract\n\nEvery packet must include:\n\n1. Packet ID and objective.\n2. Owned files.\n3. Dependencies (none or packet IDs).\n4. Risks and invariants to preserve.\n5. Required checks.\n6. Integration notes for main thread.\n\nUse [`references\u002Fwork-packet-template.md`](references\u002Fwork-packet-template.md) for the exact shape.\n\n## Agent Prompting Contract\n\n- Use the prompt templates in [`references\u002Fagent-prompt-templates.md`](references\u002Fagent-prompt-templates.md).\n- Explorer prompts focus on analysis and decomposition.\n- Worker prompts focus on implementation and validation with strict ownership boundaries.\n\n## Safety Guardrails\n\n- Do not start worker execution before plan synthesis is complete.\n- Do not parallelize across unresolved dependencies.\n- Do not claim completion if any required packet check fails.\n- Stop and re-plan when packet boundaries cause repeated merge conflicts.\n\n## Validation Strategy\n\nRun in this order:\n\n1. Packet-level checks (fast and scoped).\n2. Cross-packet integration checks.\n3. 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Use when working with store code (src\u002Fstore\u002F**), implementing actions, managing state, or creating slices. Triggers on Zustand store development, state management questions, or action implementation.","https:\u002F\u002Fgithub.com\u002FLongLeo287\u002FOmniClaw\u002Fblob\u002Fmain\u002Fecosystem\u002Fskills\u002Fzustand\u002FSKILL.md",[],[162,185,168,186,169,187,188,171,189,173,190,191,192,163,38,193,194,177,178],"development","code","actions","src","guide","implementation","zustand state","triggers","questions","zustand state management",{"skillNo":196,"name":197,"nameZh":6,"description":198,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":199,"categories":200,"keywords":205,"publishedAt":39},"01M6JQZSWV4KWJP112G6XHEP44","youtube_summarizer","Automatically fetch YouTube video transcripts, generate structured summaries, and send full transcripts to messaging platforms. Detects YouTube URLs and provides metadata, key insights, and downloadable transcripts.","https:\u002F\u002Fgithub.com\u002FLongLeo287\u002FOmniClaw\u002Fblob\u002Fmain\u002Fecosystem\u002Fskills\u002Fyoutube_summarizer\u002FSKILL.md",[201,204],{"categoryNo":202,"categoryName":203,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221],"youtube","youtube summarizer","summarizer","generate","metadata","messaging","Video","fetch","telegram","transcription","summarization","structured","full","insights","transcripts","summaries",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":226,"categories":227,"keywords":228,"publishedAt":39},"01M6JQZN3QJB6B77JGKJD62DQT","youtube_clipper","YouTube 视频智能剪辑工具。下载视频和字幕，AI 分析生成精细章节（几分钟级别）， 用户选择片段后自动剪辑、翻译字幕为中英双语、烧录字幕到视频，并生成总结文案。 使用场景：当用户需要剪辑 YouTube 视频、生成短视频片段、制作双语字幕版本时。 关键词：视频剪辑、YouTube、字幕翻译、双语字幕、视频下载、clip video","https:\u002F\u002Fgithub.com\u002FLongLeo287\u002FOmniClaw\u002Fblob\u002Fmain\u002Fecosystem\u002Fskills\u002Fyoutube_clipper\u002FSKILL.md",[],[206,229,230,212,231,232,233,234,235,236,237,238,239,240,241,242,243],"clipper","youtube clipper","Bash","下载","视频","生成","关键词","翻译","read","write","clip","总结","工具","文案","sonnet"]