[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M72A103SHFSGB6JVQ47QGFCB":3,"skill-same:01M72A103SHFSGB6JVQ47QGFCB":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},"01M72A103SHFSGB6JVQ47QGFCB","spec-driven-development",null,"Create clear specifications and acceptance criteria before writing code. Use for any new feature, significant change, or when requirements are ambiguous. Trigger when the user says \"spec\", \"requirements\", \"what should we build\", or before starting non-trivial implementation.","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\u002Fspec-driven-development\u002FSKILL.md",[],[],"2026-11-10 16:53:06",false,"---\nname: spec-driven-development\ndescription: >\n  Create clear specifications and acceptance criteria before writing code.\n  Use for any new feature, significant change, or when requirements are ambiguous.\n  Trigger when the user says \"spec\", \"requirements\", \"what should we build\", or before starting non-trivial implementation.\n---\n\n# Spec-Driven Development\n\n## Overview\n\nWrite a lightweight but precise specification before implementation. This prevents the most common agent failure mode: building the wrong thing confidently.\n\n## When to Use\n\n- New features or user stories\n- Significant refactors or architectural changes\n- Ambiguous requests\n- Before any non-trivial coding work\n\n## Core Process\n\n### 1. Capture Intent\nWhat problem are we solving? For whom? Why now?\n\n### 2. Define Success\nWhat does \"done\" look like from the user’s perspective?\n\n### 3. Write the Spec\nKeep it short. Focus on:\n\n- **Problem statement**\n- **Goals** (and non-goals)\n- **User-facing behavior**\n- **Acceptance criteria** (testable)\n- **Edge cases & error handling**\n- **Constraints** (performance, compatibility, security, etc.)\n- **Open questions**\n\n### 4. Review & Agree\nSurface the spec to the user for confirmation before coding.\n\n## Recommended Spec Template\n\n```markdown\n# Spec: [Feature Name]\n\n## Problem\n...\n\n## Goals\n- ...\n\n## Non-Goals\n- ...\n\n## User Stories \u002F Behavior\n- As a ... I want ... so that ...\n\n## Acceptance Criteria\n- [ ] ...\n- [ ] ...\n\n## Edge Cases\n- ...\n\n## Technical Notes \u002F Constraints\n- ...\n\n## Open Questions\n- ...\n```\n\n## Principles\n\n- Prefer concrete, testable statements over vague ones\n- Explicitly call out non-goals to prevent scope creep\n- Keep the spec living — update it if requirements change\n- A good spec makes implementation and review much easier\n\n## Verification\n\nBefore writing code, confirm:\n- The user has seen and agreed with the spec (or key parts)\n- Acceptance criteria are specific enough to test against\n- Major assumptions are documented\n","# Spec-Driven Development\n\n## Overview\n\nWrite a lightweight but precise specification before implementation. This prevents the most common agent failure mode: building the wrong thing confidently.\n\n## When to Use\n\n- New features or user stories\n- Significant refactors or architectural changes\n- Ambiguous requests\n- Before any non-trivial coding work\n\n## Core Process\n\n### 1. Capture Intent\nWhat problem are we solving? For whom? Why now?\n\n### 2. Define Success\nWhat does \"done\" look like from the user’s perspective?\n\n### 3. Write the Spec\nKeep it short. Focus on:\n\n- **Problem statement**\n- **Goals** (and non-goals)\n- **User-facing behavior**\n- **Acceptance criteria** (testable)\n- **Edge cases & error handling**\n- **Constraints** (performance, compatibility, security, etc.)\n- **Open questions**\n\n### 4. Review & Agree\nSurface the spec to the user for confirmation before coding.\n\n## Recommended Spec Template\n\n```markdown\n# Spec: [Feature Name]\n\n## Problem\n...\n\n## Goals\n- ...\n\n## Non-Goals\n- ...\n\n## User Stories \u002F Behavior\n- As a ... I want ... so that ...\n\n## Acceptance Criteria\n- [ ] ...\n- [ ] ...\n\n## Edge Cases\n- ...\n\n## Technical Notes \u002F Constraints\n- ...\n\n## Open Questions\n- ...\n```\n\n## Principles\n\n- Prefer concrete, testable statements over vague ones\n- Explicitly call out non-goals to prevent scope creep\n- Keep the spec living — update it if requirements change\n- A good spec makes implementation and review much easier\n\n## Verification\n\nBefore writing code, confirm:\n- The user has seen and agreed with the spec (or key parts)\n- Acceptance criteria are specific enough to test against\n- Major assumptions are documented\n",{"name":5,"description":7},"main","8d2fd11b59496bfb3792e747e5519817e554e6cf","skills\u002Fengineering\u002Fspec-driven-development",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 spec-driven-development",{"tool":39,"command":40},"bunx","bunx skills add itsual\u002Fagent-skills-collection --skill spec-driven-development",{"tool":42,"command":43},"pnpm","pnpm dlx skills add itsual\u002Fagent-skills-collection --skill spec-driven-development","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fengineering\u002Fspec-driven-development\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",[],[]]