[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M729WK1MZAEHK9EN8YFP4YDR":3,"skill-same:01M729WK1MZAEHK9EN8YFP4YDR":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},"01M729WK1MZAEHK9EN8YFP4YDR","code-review-and-quality",null,"Conduct multi-axis code review covering correctness, readability, architecture, security, and performance. Use before merging any change, after implementing a feature, when reviewing code written by yourself, another agent, or a human, or when assessing overall code quality. Trigger on any request involving code review, PR review, quality check, or \"review this change\".","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\u002Fcode-review-and-quality\u002FSKILL.md",[],[],"2026-11-10 16:53:17",false,"---\nname: code-review-and-quality\ndescription: >\n  Conduct multi-axis code review covering correctness, readability, architecture, security, and performance.\n  Use before merging any change, after implementing a feature, when reviewing code written by yourself, another agent, or a human, or when assessing overall code quality.\n  Trigger on any request involving code review, PR review, quality check, or \"review this change\".\n---\n\n# Code Review and Quality\n\n## Overview\n\nMulti-dimensional code review with clear quality gates. Every meaningful change should be reviewed before it enters the main branch.\n\n**Approval standard**: Approve a change when it clearly improves overall code health, even if it is not perfect. Perfect code does not exist. The goal is continuous improvement.\n\n## When to Use\n\n- Before merging any PR or significant change\n- After completing a feature or bug fix\n- When another agent or model produced code that needs evaluation\n- During refactoring\n- When asked to \"review this\", \"check quality\", or \"is this good to merge?\"\n\n## The Five-Axis Review\n\nEvaluate every change across these dimensions:\n\n### 1. Correctness\nDoes the code do what it claims?\n- Matches the spec or stated requirements?\n- Edge cases handled (null, empty, boundaries)?\n- Error paths handled (not only happy path)?\n- Tests actually verify the right behavior?\n- Off-by-one, race conditions, or state bugs?\n\n### 2. Readability & Simplicity\nCan another engineer (or agent) understand this without the author explaining it?\n- Clear, consistent naming?\n- Straightforward control flow?\n- Appropriate level of abstraction (not over-engineered)?\n- Could this be done in significantly fewer lines?\n- Dead code, leftover comments, or temporary hacks?\n\n### 3. Architecture\nDoes the change fit the system?\n- Follows existing patterns or introduces a justified new one?\n- Clean module boundaries?\n- Dependencies flowing in the right direction?\n- Feature-specific logic kept out of shared modules?\n- Complexity reduced rather than merely relocated?\n\n### 4. Security\n- Input validated and sanitized at boundaries?\n- Secrets kept out of code, logs, and commits?\n- AuthZ checks present where needed?\n- No injection risks (SQL, command, XSS, etc.)?\n- External data treated as untrusted?\n\n### 5. Performance\n- N+1 queries or unbounded operations?\n- Unnecessary work in hot paths?\n- Missing pagination on lists?\n- Synchronous work that should be async?\n\n## Review Process\n\n1. **Understand context** – What is this change trying to achieve?\n2. **Review tests first** – Do they exist and test behavior (not just implementation)?\n3. **Walk the implementation** using the five axes.\n4. **Categorize findings**:\n   - **Critical** – Blocks merge (security, data loss, broken functionality)\n   - Required (no prefix) – Must fix before merge\n   - **Nit** \u002F Optional – Style or preference\n   - **FYI** – Informational only\n5. **Verify the verification** – What tests were run? Build status? Manual checks?\n\n## Change Sizing Guidance\n\n- ~100 lines changed → Ideal\n- ~300 lines → Acceptable if single logical change\n- ~1000+ lines → Usually too large; ask to split\n\nPrefer small, focused changes. Separate refactoring from feature work.\n\n## Common Rationalizations to Reject\n\n| Rationalization | Reality |\n|-----------------|--------|\n| \"It works, that's enough\" | Working but unreadable\u002Finsecure\u002Farchitecturally wrong creates debt. |\n| \"We'll clean it up later\" | Later rarely comes. Require cleanup now. |\n| \"AI-generated code is probably fine\" | AI code needs *more* scrutiny, not less. |\n| \"The tests pass\" | Necessary but not sufficient. |\n\n## Verification Checklist\n\n- [ ] All Critical and Required issues resolved\n- [ ] Tests pass and cover the change\n- [ ] Build succeeds\n- [ ] No secrets or sensitive data introduced\n- [ ] Complexity has not increased without justification\n\n## See Also\n\n- `security-and-hardening` for deeper security review\n- `code-simplification` for focused complexity reduction\n- `test-driven-development` for test quality\n","# Code Review and Quality\n\n## Overview\n\nMulti-dimensional code review with clear quality gates. Every meaningful change should be reviewed before it enters the main branch.\n\n**Approval standard**: Approve a change when it clearly improves overall code health, even if it is not perfect. Perfect code does not exist. The goal is continuous improvement.\n\n## When to Use\n\n- Before merging any PR or significant change\n- After completing a feature or bug fix\n- When another agent or model produced code that needs evaluation\n- During refactoring\n- When asked to \"review this\", \"check quality\", or \"is this good to merge?\"\n\n## The Five-Axis Review\n\nEvaluate every change across these dimensions:\n\n### 1. Correctness\nDoes the code do what it claims?\n- Matches the spec or stated requirements?\n- Edge cases handled (null, empty, boundaries)?\n- Error paths handled (not only happy path)?\n- Tests actually verify the right behavior?\n- Off-by-one, race conditions, or state bugs?\n\n### 2. Readability & Simplicity\nCan another engineer (or agent) understand this without the author explaining it?\n- Clear, consistent naming?\n- Straightforward control flow?\n- Appropriate level of abstraction (not over-engineered)?\n- Could this be done in significantly fewer lines?\n- Dead code, leftover comments, or temporary hacks?\n\n### 3. Architecture\nDoes the change fit the system?\n- Follows existing patterns or introduces a justified new one?\n- Clean module boundaries?\n- Dependencies flowing in the right direction?\n- Feature-specific logic kept out of shared modules?\n- Complexity reduced rather than merely relocated?\n\n### 4. Security\n- Input validated and sanitized at boundaries?\n- Secrets kept out of code, logs, and commits?\n- AuthZ checks present where needed?\n- No injection risks (SQL, command, XSS, etc.)?\n- External data treated as untrusted?\n\n### 5. Performance\n- N+1 queries or unbounded operations?\n- Unnecessary work in hot paths?\n- Missing pagination on lists?\n- Synchronous work that should be async?\n\n## Review Process\n\n1. **Understand context** – What is this change trying to achieve?\n2. **Review tests first** – Do they exist and test behavior (not just implementation)?\n3. **Walk the implementation** using the five axes.\n4. **Categorize findings**:\n   - **Critical** – Blocks merge (security, data loss, broken functionality)\n   - Required (no prefix) – Must fix before merge\n   - **Nit** \u002F Optional – Style or preference\n   - **FYI** – Informational only\n5. **Verify the verification** – What tests were run? Build status? Manual checks?\n\n## Change Sizing Guidance\n\n- ~100 lines changed → Ideal\n- ~300 lines → Acceptable if single logical change\n- ~1000+ lines → Usually too large; ask to split\n\nPrefer small, focused changes. Separate refactoring from feature work.\n\n## Common Rationalizations to Reject\n\n| Rationalization | Reality |\n|-----------------|--------|\n| \"It works, that's enough\" | Working but unreadable\u002Finsecure\u002Farchitecturally wrong creates debt. |\n| \"We'll clean it up later\" | Later rarely comes. Require cleanup now. |\n| \"AI-generated code is probably fine\" | AI code needs *more* scrutiny, not less. |\n| \"The tests pass\" | Necessary but not sufficient. |\n\n## Verification Checklist\n\n- [ ] All Critical and Required issues resolved\n- [ ] Tests pass and cover the change\n- [ ] Build succeeds\n- [ ] No secrets or sensitive data introduced\n- [ ] Complexity has not increased without justification\n\n## See Also\n\n- `security-and-hardening` for deeper security review\n- `code-simplification` for focused complexity reduction\n- `test-driven-development` for test quality\n",{"name":5,"description":7},"main","8d2fd11b59496bfb3792e747e5519817e554e6cf","skills\u002Fengineering\u002Fcode-review-and-quality",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 code-review-and-quality",{"tool":39,"command":40},"bunx","bunx skills add itsual\u002Fagent-skills-collection --skill code-review-and-quality",{"tool":42,"command":43},"pnpm","pnpm dlx skills add itsual\u002Fagent-skills-collection --skill code-review-and-quality","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fitsual\u002Fagent-skills-collection\u002Fblob\u002Fmain\u002Fskills\u002Fengineering\u002Fcode-review-and-quality\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",[],[]]