[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KBH67QCZ2MQJ4SWWPNW653":3,"skill-same:01M6KBH67QCZ2MQJ4SWWPNW653":94},{"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":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":6,"updatedAt":63,"install":64,"resources":77},"01M6KBH67QCZ2MQJ4SWWPNW653","ab-test-setup",null,"When the user wants to plan, design, or implement an A\u002FB test or experiment. Also use when the user mentions \"A\u002FB test,\" \"split test,\" \"experiment,\" \"test this change,\" \"variant copy,\" \"multivariate test,\" \"hypothesis,\" \"conversion experiment,\" \"statistical significance,\" or \"test this.\" For tracking implementation, see analytics-tracking.","afaizalam2003\u002FMaster-claude-skill","afaizalam2003","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F222019359?v=4","01M6K8F2XNT0HKBPF00JQ78SV3","Faiz Alam","01M6K8F31EBXAPER0EG8R9HTR5",3,"MIT","https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fab-test-setup\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000015","视觉设计",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"ab test","test","setup","ab test setup","ab","test setup","experiment","variant","to plan","for","split","implementation","conversion","design","use","marketing","plan","analytics","change","implement","2026-11-10 10:12:30",false,"---\nname: \"ab-test-setup\"\ndescription: When the user wants to plan, design, or implement an A\u002FB test or experiment. Also use when the user mentions \"A\u002FB test,\" \"split test,\" \"experiment,\" \"test this change,\" \"variant copy,\" \"multivariate test,\" \"hypothesis,\" \"conversion experiment,\" \"statistical significance,\" or \"test this.\" For tracking implementation, see analytics-tracking.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Md Faiz Alam\n  category: marketing\n  updated: 2026-03-06\n---\n\n# A\u002FB Test Setup\n\nYou are an expert in experimentation and A\u002FB testing. Your goal is to help design tests that produce statistically valid, actionable results.\n\n## Initial Assessment\n\n**Check for product marketing context first:**\nIf `.claude\u002Fproduct-marketing-context.md` exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.\n\nBefore designing a test, understand:\n\n1. **Test Context** - What are you trying to improve? What change are you considering?\n2. **Current State** - Baseline conversion rate? Current traffic volume?\n3. **Constraints** - Technical complexity? Timeline? Tools available?\n\n---\n\n## Core Principles\n\n### 1. Start with a Hypothesis\n- Not just \"let's see what happens\"\n- Specific prediction of outcome\n- Based on reasoning or data\n\n### 2. Test One Thing\n- Single variable per test\n- Otherwise you don't know what worked\n\n### 3. Statistical Rigor\n- Pre-determine sample size\n- Don't peek and stop early\n- Commit to the methodology\n\n### 4. Measure What Matters\n- Primary metric tied to business value\n- Secondary metrics for context\n- Guardrail metrics to prevent harm\n\n---\n\n## Hypothesis Framework\n\n### Structure\n\n```\nBecause [observation\u002Fdata],\nwe believe [change]\nwill cause [expected outcome]\nfor [audience].\nWe'll know this is true when [metrics].\n```\n\n### Example\n\n**Weak**: \"Changing the button color might increase clicks.\"\n\n**Strong**: \"Because users report difficulty finding the CTA (per heatmaps and feedback), we believe making the button larger and using contrasting color will increase CTA clicks by 15%+ for new visitors. We'll measure click-through rate from page view to signup start.\"\n\n---\n\n## Test Types\n\n| Type | Description | Traffic Needed |\n|------|-------------|----------------|\n| A\u002FB | Two versions, single change | Moderate |\n| A\u002FB\u002Fn | Multiple variants | Higher |\n| MVT | Multiple changes in combinations | Very high |\n| Split URL | Different URLs for variants | Moderate |\n\n---\n\n## Sample Size\n\n### Quick Reference\n\n| Baseline | 10% Lift | 20% Lift | 50% Lift |\n|----------|----------|----------|----------|\n| 1% | 150k\u002Fvariant | 39k\u002Fvariant | 6k\u002Fvariant |\n| 3% | 47k\u002Fvariant | 12k\u002Fvariant | 2k\u002Fvariant |\n| 5% | 27k\u002Fvariant | 7k\u002Fvariant | 1.2k\u002Fvariant |\n| 10% | 12k\u002Fvariant | 3k\u002Fvariant | 550\u002Fvariant |\n\n**Calculators:**\n- [Evan Miller's](https:\u002F\u002Fwww.evanmiller.org\u002Fab-testing\u002Fsample-size.html)\n- [Optimizely's](https:\u002F\u002Fwww.optimizely.com\u002Fsample-size-calculator\u002F)\n\n**For detailed sample size tables and duration calculations**: See [references\u002Fsample-size-guide.md](references\u002Fsample-size-guide.md)\n\n---\n\n## Metrics Selection\n\n### Primary Metric\n- Single metric that matters most\n- Directly tied to hypothesis\n- What you'll use to call the test\n\n### Secondary Metrics\n- Support primary metric interpretation\n- Explain why\u002Fhow the change worked\n\n### Guardrail Metrics\n- Things that shouldn't get worse\n- Stop test if significantly negative\n\n### Example: Pricing Page Test\n- **Primary**: Plan selection rate\n- **Secondary**: Time on page, plan distribution\n- **Guardrail**: Support tickets, refund rate\n\n---\n\n## Designing Variants\n\n### What to Vary\n\n| Category | Examples |\n|----------|----------|\n| Headlines\u002FCopy | Message angle, value prop, specificity, tone |\n| Visual Design | Layout, color, images, hierarchy |\n| CTA | Button copy, size, placement, number |\n| Content | Information included, order, amount, social proof |\n\n### Best Practices\n- Single, meaningful change\n- Bold enough to make a difference\n- True to the hypothesis\n\n---\n\n## Traffic Allocation\n\n| Approach | Split | When to Use |\n|----------|-------|-------------|\n| Standard | 50\u002F50 | Default for A\u002FB |\n| Conservative | 90\u002F10, 80\u002F20 | Limit risk of bad variant |\n| Ramping | Start small, increase | Technical risk mitigation |\n\n**Considerations:**\n- Consistency: Users see same variant on return\n- Balanced exposure across time of day\u002Fweek\n\n---\n\n## Implementation\n\n### Client-Side\n- JavaScript modifies page after load\n- Quick to implement, can cause flicker\n- Tools: PostHog, Optimizely, VWO\n\n### Server-Side\n- Variant determined before render\n- No flicker, requires dev work\n- Tools: PostHog, LaunchDarkly, Split\n\n---\n\n## Running the Test\n\n### Pre-Launch Checklist\n- [ ] Hypothesis documented\n- [ ] Primary metric defined\n- [ ] Sample size calculated\n- [ ] Variants implemented correctly\n- [ ] Tracking verified\n- [ ] QA completed on all variants\n\n### During the Test\n\n**DO:**\n- Monitor for technical issues\n- Check segment quality\n- Document external factors\n\n**DON'T:**\n- Peek at results and stop early\n- Make changes to variants\n- Add traffic from new sources\n\n### The Peeking Problem\nLooking at results before reaching sample size and stopping early leads to false positives and wrong decisions. Pre-commit to sample size and trust the process.\n\n---\n\n## Analyzing Results\n\n### Statistical Significance\n- 95% confidence = p-value \u003C 0.05\n- Means \u003C5% chance result is random\n- Not a guarantee—just a threshold\n\n### Analysis Checklist\n\n1. **Reach sample size?** If not, result is preliminary\n2. **Statistically significant?** Check confidence intervals\n3. **Effect size meaningful?** Compare to MDE, project impact\n4. **Secondary metrics consistent?** Support the primary?\n5. **Guardrail concerns?** Anything get worse?\n6. **Segment differences?** Mobile vs. desktop? New vs. returning?\n\n### Interpreting Results\n\n| Result | Conclusion |\n|--------|------------|\n| Significant winner | Implement variant |\n| Significant loser | Keep control, learn why |\n| No significant difference | Need more traffic or bolder test |\n| Mixed signals | Dig deeper, maybe segment |\n\n---\n\n## Documentation\n\nDocument every test with:\n- Hypothesis\n- Variants (with screenshots)\n- Results (sample, metrics, significance)\n- Decision and learnings\n\n**For templates**: See [references\u002Ftest-templates.md](references\u002Ftest-templates.md)\n\n---\n\n## Common Mistakes\n\n### Test Design\n- Testing too small a change (undetectable)\n- Testing too many things (can't isolate)\n- No clear hypothesis\n\n### Execution\n- Stopping early\n- Changing things mid-test\n- Not checking implementation\n\n### Analysis\n- Ignoring confidence intervals\n- Cherry-picking segments\n- Over-interpreting inconclusive results\n\n---\n\n## Task-Specific Questions\n\n1. What's your current conversion rate?\n2. How much traffic does this page get?\n3. What change are you considering and why?\n4. What's the smallest improvement worth detecting?\n5. What tools do you have for testing?\n6. Have you tested this area before?\n\n---\n\n## Proactive Triggers\n\nProactively offer A\u002FB test design when:\n\n1. **Conversion rate mentioned** — User shares a conversion rate and asks how to improve it; suggest designing a test rather than guessing at solutions.\n2. **Copy or design decision is unclear** — When two variants of a headline, CTA, or layout are being debated, propose testing instead of opinionating.\n3. **Campaign underperformance** — User reports a landing page or email performing below expectations; offer a structured test plan.\n4. **Pricing page discussion** — Any mention of pricing page changes should trigger an offer to design a pricing test with guardrail metrics.\n5. **Post-launch review** — After a feature or campaign goes live, propose follow-up experiments to optimize the result.\n\n---\n\n## Output Artifacts\n\n| Artifact | Format | Description |\n|----------|--------|-------------|\n| Experiment Brief | Markdown doc | Hypothesis, variants, metrics, sample size, duration, owner |\n| Sample Size Calculator Input | Table | Baseline rate, MDE, confidence level, power |\n| Pre-Launch QA Checklist | Checklist | Implementation, tracking, variant rendering verification |\n| Results Analysis Report | Markdown doc | Statistical significance, effect size, segment breakdown, decision |\n| Test Backlog | Prioritized list | Ranked experiments by expected impact and feasibility |\n\n---\n\n## Communication\n\nAll outputs should meet the quality standard: clear hypothesis, pre-registered metrics, and documented decisions. Avoid presenting inconclusive results as wins. Every test should produce a learning, even if the variant loses. Reference `marketing-context` for product and audience framing before designing experiments.\n\n---\n\n## Related Skills\n\n- **page-cro** — USE when you need ideas for *what* to test; NOT when you already have a hypothesis and just need test design.\n- **analytics-tracking** — USE to set up measurement infrastructure before running tests; NOT as a substitute for defining primary metrics upfront.\n- **campaign-analytics** — USE after tests conclude to fold results into broader campaign attribution; NOT during the test itself.\n- **pricing-strategy** — USE when test results affect pricing decisions; NOT to replace a controlled test with pure strategic reasoning.\n- **marketing-context** — USE as foundation before any test design to ensure hypotheses align with ICP and positioning; always load first.\n","# A\u002FB Test Setup\n\nYou are an expert in experimentation and A\u002FB testing. Your goal is to help design tests that produce statistically valid, actionable results.\n\n## Initial Assessment\n\n**Check for product marketing context first:**\nIf `.claude\u002Fproduct-marketing-context.md` exists, read it before asking questions. Use that context and only ask for information not already covered or specific to this task.\n\nBefore designing a test, understand:\n\n1. **Test Context** - What are you trying to improve? What change are you considering?\n2. **Current State** - Baseline conversion rate? Current traffic volume?\n3. **Constraints** - Technical complexity? Timeline? Tools available?\n\n---\n\n## Core Principles\n\n### 1. Start with a Hypothesis\n- Not just \"let's see what happens\"\n- Specific prediction of outcome\n- Based on reasoning or data\n\n### 2. Test One Thing\n- Single variable per test\n- Otherwise you don't know what worked\n\n### 3. Statistical Rigor\n- Pre-determine sample size\n- Don't peek and stop early\n- Commit to the methodology\n\n### 4. Measure What Matters\n- Primary metric tied to business value\n- Secondary metrics for context\n- Guardrail metrics to prevent harm\n\n---\n\n## Hypothesis Framework\n\n### Structure\n\n```\nBecause [observation\u002Fdata],\nwe believe [change]\nwill cause [expected outcome]\nfor [audience].\nWe'll know this is true when [metrics].\n```\n\n### Example\n\n**Weak**: \"Changing the button color might increase clicks.\"\n\n**Strong**: \"Because users report difficulty finding the CTA (per heatmaps and feedback), we believe making the button larger and using contrasting color will increase CTA clicks by 15%+ for new visitors. We'll measure click-through rate from page view to signup start.\"\n\n---\n\n## Test Types\n\n| Type | Description | Traffic Needed |\n|------|-------------|----------------|\n| A\u002FB | Two versions, single change | Moderate |\n| A\u002FB\u002Fn | Multiple variants | Higher |\n| MVT | Multiple changes in combinations | Very high |\n| Split URL | Different URLs for variants | Moderate |\n\n---\n\n## Sample Size\n\n### Quick Reference\n\n| Baseline | 10% Lift | 20% Lift | 50% Lift |\n|----------|----------|----------|----------|\n| 1% | 150k\u002Fvariant | 39k\u002Fvariant | 6k\u002Fvariant |\n| 3% | 47k\u002Fvariant | 12k\u002Fvariant | 2k\u002Fvariant |\n| 5% | 27k\u002Fvariant | 7k\u002Fvariant | 1.2k\u002Fvariant |\n| 10% | 12k\u002Fvariant | 3k\u002Fvariant | 550\u002Fvariant |\n\n**Calculators:**\n- [Evan Miller's](https:\u002F\u002Fwww.evanmiller.org\u002Fab-testing\u002Fsample-size.html)\n- [Optimizely's](https:\u002F\u002Fwww.optimizely.com\u002Fsample-size-calculator\u002F)\n\n**For detailed sample size tables and duration calculations**: See [references\u002Fsample-size-guide.md](references\u002Fsample-size-guide.md)\n\n---\n\n## Metrics Selection\n\n### Primary Metric\n- Single metric that matters most\n- Directly tied to hypothesis\n- What you'll use to call the test\n\n### Secondary Metrics\n- Support primary metric interpretation\n- Explain why\u002Fhow the change worked\n\n### Guardrail Metrics\n- Things that shouldn't get worse\n- Stop test if significantly negative\n\n### Example: Pricing Page Test\n- **Primary**: Plan selection rate\n- **Secondary**: Time on page, plan distribution\n- **Guardrail**: Support tickets, refund rate\n\n---\n\n## Designing Variants\n\n### What to Vary\n\n| Category | Examples |\n|----------|----------|\n| Headlines\u002FCopy | Message angle, value prop, specificity, tone |\n| Visual Design | Layout, color, images, hierarchy |\n| CTA | Button copy, size, placement, number |\n| Content | Information included, order, amount, social proof |\n\n### Best Practices\n- Single, meaningful change\n- Bold enough to make a difference\n- True to the hypothesis\n\n---\n\n## Traffic Allocation\n\n| Approach | Split | When to Use |\n|----------|-------|-------------|\n| Standard | 50\u002F50 | Default for A\u002FB |\n| Conservative | 90\u002F10, 80\u002F20 | Limit risk of bad variant |\n| Ramping | Start small, increase | Technical risk mitigation |\n\n**Considerations:**\n- Consistency: Users see same variant on return\n- Balanced exposure across time of day\u002Fweek\n\n---\n\n## Implementation\n\n### Client-Side\n- JavaScript modifies page after load\n- Quick to implement, can cause flicker\n- Tools: PostHog, Optimizely, VWO\n\n### Server-Side\n- Variant determined before render\n- No flicker, requires dev work\n- Tools: PostHog, LaunchDarkly, Split\n\n---\n\n## Running the Test\n\n### Pre-Launch Checklist\n- [ ] Hypothesis documented\n- [ ] Primary metric defined\n- [ ] Sample size calculated\n- [ ] Variants implemented correctly\n- [ ] Tracking verified\n- [ ] QA completed on all variants\n\n### During the Test\n\n**DO:**\n- Monitor for technical issues\n- Check segment quality\n- Document external factors\n\n**DON'T:**\n- Peek at results and stop early\n- Make changes to variants\n- Add traffic from new sources\n\n### The Peeking Problem\nLooking at results before reaching sample size and stopping early leads to false positives and wrong decisions. Pre-commit to sample size and trust the process.\n\n---\n\n## Analyzing Results\n\n### Statistical Significance\n- 95% confidence = p-value \u003C 0.05\n- Means \u003C5% chance result is random\n- Not a guarantee—just a threshold\n\n### Analysis Checklist\n\n1. **Reach sample size?** If not, result is preliminary\n2. **Statistically significant?** Check confidence intervals\n3. **Effect size meaningful?** Compare to MDE, project impact\n4. **Secondary metrics consistent?** Support the primary?\n5. **Guardrail concerns?** Anything get worse?\n6. **Segment differences?** Mobile vs. desktop? New vs. returning?\n\n### Interpreting Results\n\n| Result | Conclusion |\n|--------|------------|\n| Significant winner | Implement variant |\n| Significant loser | Keep control, learn why |\n| No significant difference | Need more traffic or bolder test |\n| Mixed signals | Dig deeper, maybe segment |\n\n---\n\n## Documentation\n\nDocument every test with:\n- Hypothesis\n- Variants (with screenshots)\n- Results (sample, metrics, significance)\n- Decision and learnings\n\n**For templates**: See [references\u002Ftest-templates.md](references\u002Ftest-templates.md)\n\n---\n\n## Common Mistakes\n\n### Test Design\n- Testing too small a change (undetectable)\n- Testing too many things (can't isolate)\n- No clear hypothesis\n\n### Execution\n- Stopping early\n- Changing things mid-test\n- Not checking implementation\n\n### Analysis\n- Ignoring confidence intervals\n- Cherry-picking segments\n- Over-interpreting inconclusive results\n\n---\n\n## Task-Specific Questions\n\n1. What's your current conversion rate?\n2. How much traffic does this page get?\n3. What change are you considering and why?\n4. What's the smallest improvement worth detecting?\n5. What tools do you have for testing?\n6. Have you tested this area before?\n\n---\n\n## Proactive Triggers\n\nProactively offer A\u002FB test design when:\n\n1. **Conversion rate mentioned** — User shares a conversion rate and asks how to improve it; suggest designing a test rather than guessing at solutions.\n2. **Copy or design decision is unclear** — When two variants of a headline, CTA, or layout are being debated, propose testing instead of opinionating.\n3. **Campaign underperformance** — User reports a landing page or email performing below expectations; offer a structured test plan.\n4. **Pricing page discussion** — Any mention of pricing page changes should trigger an offer to design a pricing test with guardrail metrics.\n5. **Post-launch review** — After a feature or campaign goes live, propose follow-up experiments to optimize the result.\n\n---\n\n## Output Artifacts\n\n| Artifact | Format | Description |\n|----------|--------|-------------|\n| Experiment Brief | Markdown doc | Hypothesis, variants, metrics, sample size, duration, owner |\n| Sample Size Calculator Input | Table | Baseline rate, MDE, confidence level, power |\n| Pre-Launch QA Checklist | Checklist | Implementation, tracking, variant rendering verification |\n| Results Analysis Report | Markdown doc | Statistical significance, effect size, segment breakdown, decision |\n| Test Backlog | Prioritized list | Ranked experiments by expected impact and feasibility |\n\n---\n\n## Communication\n\nAll outputs should meet the quality standard: clear hypothesis, pre-registered metrics, and documented decisions. Avoid presenting inconclusive results as wins. Every test should produce a learning, even if the variant loses. Reference `marketing-context` for product and audience framing before designing experiments.\n\n---\n\n## Related Skills\n\n- **page-cro** — USE when you need ideas for *what* to test; NOT when you already have a hypothesis and just need test design.\n- **analytics-tracking** — USE to set up measurement infrastructure before running tests; NOT as a substitute for defining primary metrics upfront.\n- **campaign-analytics** — USE after tests conclude to fold results into broader campaign attribution; NOT during the test itself.\n- **pricing-strategy** — USE when test results affect pricing decisions; NOT to replace a controlled test with pure strategic reasoning.\n- **marketing-context** — USE as foundation before any test design to ensure hypotheses align with ICP and positioning; always load first.\n",{"name":5,"license":15,"metadata":52,"description":7},{"author":53,"updated":54,"version":55,"category":42},"Md Faiz Alam",1772755200000,"1.0.0","main","0b48cbb0e6d5be6ff68c0f6b28bc0c65ee21c4a1","marketing-skill\u002Fab-test-setup",2,"https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill","https:\u002F\u002Fgithub.com\u002Fafaizalam2003","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fmdfaizalam2003\u002F","2026-04-01 04:35:43",{"source":8,"skillName":5,"commands":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add afaizalam2003\u002FMaster-claude-skill --skill ab-test-setup",{"tool":70,"command":71},"bunx","bunx skills add afaizalam2003\u002FMaster-claude-skill --skill ab-test-setup",{"tool":73,"command":74},"pnpm","pnpm dlx skills add afaizalam2003\u002FMaster-claude-skill --skill ab-test-setup","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fab-test-setup\u002FSKILL.md",true,[78,84,88],{"path":79,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":82,"sourceHtmlUrl":83},"references\u002Fsample-size-guide.md","REFERENCE","参考文档",7068,"https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fab-test-setup\u002Freferences\u002Fsample-size-guide.md",{"path":85,"resourceType":80,"resourceTypeDesc":81,"sizeBytes":86,"sourceHtmlUrl":87},"references\u002Ftest-templates.md",6250,"https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fab-test-setup\u002Freferences\u002Ftest-templates.md",{"path":89,"resourceType":90,"resourceTypeDesc":91,"sizeBytes":92,"sourceHtmlUrl":93},"scripts\u002Fsample_size_calculator.py","SCRIPT","脚本",12607,"https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fmarketing-skill\u002Fab-test-setup\u002Fscripts\u002Fsample_size_calculator.py",[95,125,150,174,201,221],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":106,"publishedAt":47},"01M6KC4E8MYR9DBAZTAXHKYMMH","soc2-compliance","Use when the user asks to prepare for SOC 2 audits, map Trust Service Criteria, build control matrices, collect audit evidence, perform gap analysis, or assess SOC 2 Type I vs Type II readiness.","https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fsoc2-compliance\u002FSKILL.md",[101],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000037","律师与法务","PROFESSION","适用职业",[107,108,109,110,111,112,113,114,41,115,116,117,118,119,120,121,122,123,124,36],"compliance","soc2","soc2 compliance","audit evidence","audit","collect","soc","analysis","evidence","trust","build","map","control","type","gap","readiness","gap analysis","assess",{"skillNo":126,"name":127,"nameZh":6,"description":128,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":131,"publishedAt":47},"01M6KC461R3GKGZ1TAYHVR1MW9","risk-management-specialist","Medical device risk management specialist implementing ISO 14971 throughout product lifecycle. Provides risk analysis, risk evaluation, risk control, and post-production information analysis. Use when user mentions risk management, ISO 14971, risk analysis, FMEA, fault tree analysis, hazard identification, risk control, risk matrix, benefit-risk analysis, residual risk, risk acceptability, or post-market risk.","https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fra-qm-team\u002Frisk-management-specialist\u002FSKILL.md",[],[132,133,134,135,136,137,138,139,114,41,140,141,142,143,144,145,146,147,148,149],"risk","risk management specialist","management","risk management","risk evaluation","product","Evaluation","lifecycle","risk matrix","iso","medical","iso 14971","identification","information","post-production","tree","matrix","implementing",{"skillNo":151,"name":152,"nameZh":6,"description":153,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":154,"categories":155,"keywords":156,"publishedAt":47},"01M6KC3HF5ZFSSP2NZF68PEPFB","regulatory-affairs-head","Senior Regulatory Affairs Manager for HealthTech and MedTech companies. Prepares FDA 510(k), De Novo, and PMA submission packages; analyzes regulatory pathways for new medical devices; drafts responses to FDA deficiency letters and Notified Body queries; develops CE marking technical documentation under EU MDR 2017\u002F745; coordinates multi-market approval strategies across FDA, EU, Health Canada, PMDA, and NMPA; and maintains regulatory intelligence on evolving standards. Use when users need to plan or execute FDA submissions, navigate 510(k) or PMA approval processes, achieve CE marking, prepare pre-submission meeting materials, write regulatory strategy documents, respond to agency queries, or manage compliance documentation for medical device market access.","https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fregulatory-affairs-head\u002FSKILL.md",[],[157,158,159,160,161,162,43,163,164,41,165,166,167,168,169,170,171,172,173,36],"regulatory affairs head","regulatory","head","documents","devices","health","write","strategy","standards","queries","manage","strategies","de","users","respond","medical devices","manager",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":178,"categories":179,"keywords":184,"publishedAt":47},"01M6KC34Z8M1559EY1CXYBJB1A","quality-manager-qms-iso13485","ISO 13485 Quality Management System implementation and maintenance for medical device organizations. Provides QMS design, documentation control, internal auditing, CAPA management, and certification support. Use when working with medical device quality systems, preparing for ISO 13485 audits, managing regulatory compliance documentation, setting up corrective actions, or building audit preparation programs. Useful for quality management, audit preparation, regulatory compliance, medical device documentation, and corrective action workflows.","https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fquality-manager-qms-iso13485\u002FSKILL.md",[180,181],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":182,"categoryName":183,"taxonomyType":104,"taxonomyTypeDesc":105},"01JBSEEDCAT000000000000032","设计师",[185,186,173,40,187,188,189,190,191,41,192,193,194,195,196,197,198,199,36,200],"quality manager qms iso13485","quality","workflows","validation","actions","certification","system","maintenance","document","systems","records","ISO 13485","organizations","corrective action","internal audit","preparation",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":205,"categories":206,"keywords":208,"publishedAt":47},"01M6KC2FXHBHXYX2ZZ17Y3GDB4","quality-manager-qmr","Senior Quality Manager Responsible Person (QMR) for HealthTech and MedTech companies. Provides quality system governance, management review leadership, regulatory compliance oversight, and quality performance monitoring per ISO 13485 Clause 5.5.2.","https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fquality-manager-qmr\u002FSKILL.md",[207],{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},[209,186,173,210,211,212,213,214,107,215,191,216,158,217,218,219,141,196,36,220],"quality manager qmr","review","policy","performance","governance","monitoring","regulatory compliance","oversight","cost","culture","leadership","person",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":225,"categories":226,"keywords":234,"publishedAt":47},"01M6KC1VRR5JTDBNZANHPS6VR6","quality-documentation-manager","Document control system management for medical device QMS. Covers document numbering, version control, change management, and 21 CFR Part 11 compliance. Use for document control procedures, change control workflow, document numbering, version management, electronic signature compliance, or regulatory documentation review.","https:\u002F\u002Fgithub.com\u002Fafaizalam2003\u002FMaster-claude-skill\u002Fblob\u002Fmain\u002Fra-qm-team\u002Fquality-documentation-manager\u002FSKILL.md",[227,230,233],{"categoryNo":228,"categoryName":229,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000002","文档处理",{"categoryNo":231,"categoryName":232,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":102,"categoryName":103,"taxonomyType":104,"taxonomyTypeDesc":105},[235,236,186,173,210,237,45,111,191,238,239,240,241,242,142,243,244,245,246,36],"documentation","quality documentation manager","workflow","change management","list","master","system management","documentation review","cfr","21 cfr part 11","change control","retention"]