[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M729ZS4GTE7MSFSM8MH78QDD":3,"skill-same:01M729ZS4GTE7MSFSM8MH78QDD":47},{"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":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":6,"ownerHtmlUrl":29,"ownerBlogUrl":30,"ownerDescription":31,"updatedAt":32,"install":33,"resources":46},"01M729ZS4GTE7MSFSM8MH78QDD","planning-and-task-breakdown",null,"Break complex work into small, verifiable tasks with clear acceptance criteria. Use whenever starting a non-trivial feature, refactor, or project, or when the user asks for a plan, task list, breakdown, or roadmap of implementation steps. Prefer this skill before writing significant code.","itsual\u002Fagent-skills-collection","itsual","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F47685900?v=4","01M7291J4K9RE0EWMQB0JM3WFN","Arockia Liborious","01M7291J58T0363Q45MQP9KESY",7,"MIT","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fengineering\u002Fplanning-and-task-breakdown\u002FSKILL.md",[],[],"2026-11-10 16:53:17",false,"---\nname: planning-and-task-breakdown\ndescription: >\n  Break complex work into small, verifiable tasks with clear acceptance criteria.\n  Use whenever starting a non-trivial feature, refactor, or project, or when the user asks for a plan, task list, breakdown, or roadmap of implementation steps.\n  Prefer this skill before writing significant code.\n---\n\n# Planning and Task Breakdown\n\n## Overview\n\nTurn ambiguous or large goals into a sequence of small, independently verifiable tasks. Good planning dramatically increases the success rate of agent-driven work.\n\n## When to Use\n\n- Starting any non-trivial feature or change\n- User asks for a plan, breakdown, tasks, or implementation steps\n- Before beginning coding on a new area\n- When a previous attempt failed due to scope or unclear requirements\n\n## Core Process\n\n### 1. Clarify the Goal\nRestate the objective in one or two sentences. Surface assumptions explicitly:\n\n```\nASSUMPTIONS:\n1. ...\n2. ...\n→ Correct me if any of these are wrong.\n```\n\n### 2. Identify Scope Boundaries\nWhat is explicitly in scope? What is out of scope for this iteration?\n\n### 3. Decompose into Vertical Slices\nPrefer thin vertical slices (end-to-end value) over horizontal layers when possible.\n\nEach task should be:\n- Small enough to complete and verify in one focused session\n- Independently testable\n- Ordered by dependency\n\n### 4. Write Clear Acceptance Criteria\nFor every task, define how we will know it is done (tests, behavior, observable outcome).\n\n### 5. Sequence and Estimate Roughly\nOrder tasks. Note any that can be parallelized.\n\n## Output Format\n\nProduce a structured plan:\n\n```markdown\n## Goal\n[One sentence]\n\n## Assumptions\n- ...\n\n## Out of Scope\n- ...\n\n## Tasks\n\n### Task 1: [Short title]\n**Description**: ...\n**Acceptance criteria**:\n- [ ] ...\n- [ ] ...\n**Dependencies**: none \u002F Task X\n\n### Task 2: ...\n```\n\n## Quality Checks\n\n- Are tasks small enough?\n- Does each have verifiable acceptance criteria?\n- Is the first task a good starting point (low risk, high learning)?\n- Have major risks or unknowns been called out?\n\n## Common Pitfalls\n\n- Tasks that are still too large (\"Implement the whole auth system\")\n- Missing acceptance criteria\n- Hidden assumptions not surfaced\n- Starting implementation before the plan is agreed\n\n## Verification\n\nA good plan lets another agent (or human) pick up any task and know exactly what \"done\" looks like without further clarification.\n","# Planning and Task Breakdown\n\n## Overview\n\nTurn ambiguous or large goals into a sequence of small, independently verifiable tasks. Good planning dramatically increases the success rate of agent-driven work.\n\n## When to Use\n\n- Starting any non-trivial feature or change\n- User asks for a plan, breakdown, tasks, or implementation steps\n- Before beginning coding on a new area\n- When a previous attempt failed due to scope or unclear requirements\n\n## Core Process\n\n### 1. Clarify the Goal\nRestate the objective in one or two sentences. Surface assumptions explicitly:\n\n```\nASSUMPTIONS:\n1. ...\n2. ...\n→ Correct me if any of these are wrong.\n```\n\n### 2. Identify Scope Boundaries\nWhat is explicitly in scope? What is out of scope for this iteration?\n\n### 3. Decompose into Vertical Slices\nPrefer thin vertical slices (end-to-end value) over horizontal layers when possible.\n\nEach task should be:\n- Small enough to complete and verify in one focused session\n- Independently testable\n- Ordered by dependency\n\n### 4. Write Clear Acceptance Criteria\nFor every task, define how we will know it is done (tests, behavior, observable outcome).\n\n### 5. Sequence and Estimate Roughly\nOrder tasks. Note any that can be parallelized.\n\n## Output Format\n\nProduce a structured plan:\n\n```markdown\n## Goal\n[One sentence]\n\n## Assumptions\n- ...\n\n## Out of Scope\n- ...\n\n## Tasks\n\n### Task 1: [Short title]\n**Description**: ...\n**Acceptance criteria**:\n- [ ] ...\n- [ ] ...\n**Dependencies**: none \u002F Task X\n\n### Task 2: ...\n```\n\n## Quality Checks\n\n- Are tasks small enough?\n- Does each have verifiable acceptance criteria?\n- Is the first task a good starting point (low risk, high learning)?\n- Have major risks or unknowns been called out?\n\n## Common Pitfalls\n\n- Tasks that are still too large (\"Implement the whole auth system\")\n- Missing acceptance criteria\n- Hidden assumptions not surfaced\n- Starting implementation before the plan is agreed\n\n## Verification\n\nA good plan lets another agent (or human) pick up any task and know exactly what \"done\" looks like without further clarification.\n",{"name":5,"description":7},"main","8d2fd11b59496bfb3792e747e5519817e554e6cf","skills\u002Fengineering\u002Fplanning-and-task-breakdown",1,"https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection","https:\u002F\u002Fgithub.com\u002Fitsual","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Farockialiborious\u002F","Data Science & Analytics Leader | Helping organizations rethink their data-driven decision making process | AI Storyteller","2026-07-22 23:04:48",{"source":8,"skillName":5,"commands":34,"prompt":44,"zipAvailable":45},[35,38,41],{"tool":36,"command":37},"npx","npx skills add itsual\u002Fagent-skills-collection --skill planning-and-task-breakdown",{"tool":39,"command":40},"bunx","bunx skills add itsual\u002Fagent-skills-collection --skill planning-and-task-breakdown",{"tool":42,"command":43},"pnpm","pnpm dlx skills add itsual\u002Fagent-skills-collection --skill planning-and-task-breakdown","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fengineering\u002Fplanning-and-task-breakdown\u002FSKILL.md",true,[],[48,56,63,70,77,85],{"skillNo":49,"name":50,"nameZh":6,"description":51,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":52,"categories":53,"keywords":54,"publishedAt":55},"01M7297PK9X4VZRN6HAD9A58M9","compliance-monitoring","Design compliance monitoring and testing programs that verify controls work in practice and surface issues before regulators do. Use when building monitoring plans, compliance testing, or continuous control monitoring.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Fcompliance-monitoring\u002FSKILL.md",[],[],"2026-11-10 16:53:54",{"skillNo":57,"name":58,"nameZh":6,"description":59,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":60,"categories":61,"keywords":62,"publishedAt":55},"01M7297KXVGGV1CH7CEHA62FAT","complaints-handling","Operate complaints-handling processes that meet regulatory standards — logging, timely resolution, root-cause analysis, and fair outcomes. Use when designing or improving regulated complaints frameworks or analyzing complaint themes for conduct risk.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Fcomplaints-handling\u002FSKILL.md",[],[],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":69,"publishedAt":55},"01M7297HXEX2PSHBEDVWRJC924","aml-kyc-basics","Apply AML\u002FKYC fundamentals — customer due diligence, monitoring, suspicious activity escalation, and recordkeeping — for financial crime compliance. Use when designing onboarding controls, transaction monitoring concepts, or AML program components in financial services.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Faml-kyc-basics\u002FSKILL.md",[],[],{"skillNo":71,"name":72,"nameZh":6,"description":73,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":55},"01M7297G229RT4FXA2KW7EG2K6","ai-model-compliance","Apply compliance controls to AI and automated decision systems — transparency, fairness, human oversight, documentation, and regulatory alignment. Use when deploying AI in regulated processes (credit, fraud, customer decisions) or responding to AI governance expectations.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fcompliance\u002Fai-model-compliance\u002FSKILL.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":84},"01M729KEEE7VTT48V1J7YMS8GA","data-quality-management","Manage data quality end-to-end — dimensions, rules, remediation, and prevention — beyond one-off cleaning scripts. Use when building sustained data quality programs or fixing systemic accuracy, completeness, or consistency problems.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fdata-analytics\u002Fdata-quality-management\u002FSKILL.md",[],[],"2026-11-10 16:53:43",{"skillNo":86,"name":87,"nameZh":6,"description":88,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":89,"categories":90,"keywords":91,"publishedAt":84},"01M729K3VDSZC6TF67TYQ4Z8W9","data-lineage","Capture and use data lineage — where data comes from, how it transforms, and what depends on it — for impact analysis, debugging, and trust. Use when tracing metric origins, assessing change impact, or diagnosing broken pipelines.","https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fdata-analytics\u002Fdata-lineage\u002FSKILL.md",[],[]]