[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6CBS511Z34YC9M9WM558CTD":3,"skill-same:01M6CBS511Z34YC9M9WM558CTD":60},{"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":23,"publishedAt":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":14,"repositoryHtmlUrl":41,"repositoryHomepageUrl":6,"ownerHtmlUrl":42,"ownerBlogUrl":43,"ownerDescription":44,"updatedAt":45,"install":46,"resources":59},"01M6CBS511Z34YC9M9WM558CTD","ab-test-setup",null,"Structured guide for setting up A\u002FB tests with mandatory gates for hypothesis, metrics, and execution readiness.","Gabriellmaia01\u002Fneuro-dose-assist","Gabriellmaia01","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F135177180?v=4","01M6CBRZEZC8G5C400AG3SPFNH","Gabriel","01M6CBRZK8MB7DWW0B4C82TV2G",0,"MIT","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fab-test-setup\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32],"test","setup","ab test setup","test setup","metrics","hypothesis","execution","guide","up","2026-11-10 10:12:30",false,"---\nname: ab-test-setup\ndescription: Structured guide for setting up A\u002FB tests with mandatory gates for hypothesis, metrics, and execution readiness.\n---\n\n# A\u002FB Test Setup\n\n## 1️⃣ Purpose & Scope\n\nEnsure every A\u002FB test is **valid, rigorous, and safe** before a single line of code is written.\n\n- Prevents \"peeking\"\n- Enforces statistical power\n- Blocks invalid hypotheses\n\n---\n\n## 2️⃣ Pre-Requisites\n\nYou must have:\n\n- A clear user problem\n- Access to an analytics source\n- Roughly estimated traffic volume\n\n### Hypothesis Quality Checklist\n\nA valid hypothesis includes:\n\n- Observation or evidence\n- Single, specific change\n- Directional expectation\n- Defined audience\n- Measurable success criteria\n\n---\n\n### 3️⃣ Hypothesis Lock (Hard Gate)\n\nBefore designing variants or metrics, you MUST:\n\n- Present the **final hypothesis**\n- Specify:\n  - Target audience\n  - Primary metric\n  - Expected direction of effect\n  - Minimum Detectable Effect (MDE)\n\nAsk explicitly:\n\n> “Is this the final hypothesis we are committing to for this test?”\n\n**Do NOT proceed until confirmed.**\n\n---\n\n### 4️⃣ Assumptions & Validity Check (Mandatory)\n\nExplicitly list assumptions about:\n\n- Traffic stability\n- User independence\n- Metric reliability\n- Randomization quality\n- External factors (seasonality, campaigns, releases)\n\nIf assumptions are weak or violated:\n\n- Warn the user\n- Recommend delaying or redesigning the test\n\n---\n\n### 5️⃣ Test Type Selection\n\nChoose the simplest valid test:\n\n- **A\u002FB Test** – single change, two variants\n- **A\u002FB\u002Fn Test** – multiple variants, higher traffic required\n- **Multivariate Test (MVT)** – interaction effects, very high traffic\n- **Split URL Test** – major structural changes\n\nDefault to **A\u002FB** unless there is a clear reason otherwise.\n\n---\n\n### 6️⃣ Metrics Definition\n\n#### Primary Metric (Mandatory)\n\n- Single metric used to evaluate success\n- Directly tied to the hypothesis\n- Pre-defined and frozen before launch\n\n#### Secondary Metrics\n\n- Provide context\n- Explain _why_ results occurred\n- Must not override the primary metric\n\n#### Guardrail Metrics\n\n- Metrics that must not degrade\n- Used to prevent harmful wins\n- Trigger test stop if significantly negative\n\n---\n\n### 7️⃣ Sample Size & Duration\n\nDefine upfront:\n\n- Baseline rate\n- MDE\n- Significance level (typically 95%)\n- Statistical power (typically 80%)\n\nEstimate:\n\n- Required sample size per variant\n- Expected test duration\n\n**Do NOT proceed without a realistic sample size estimate.**\n\n---\n\n### 8️⃣ Execution Readiness Gate (Hard Stop)\n\nYou may proceed to implementation **only if all are true**:\n\n- Hypothesis is locked\n- Primary metric is frozen\n- Sample size is calculated\n- Test duration is defined\n- Guardrails are set\n- Tracking is verified\n\nIf any item is missing, stop and resolve it.\n\n---\n\n## Running the Test\n\n### During the Test\n\n**DO:**\n\n- Monitor technical health\n- Document external factors\n\n**DO NOT:**\n\n- Stop early due to “good-looking” results\n- Change variants mid-test\n- Add new traffic sources\n- Redefine success criteria\n\n---\n\n## Analyzing Results\n\n### Analysis Discipline\n\nWhen interpreting results:\n\n- Do NOT generalize beyond the tested population\n- Do NOT claim causality beyond the tested change\n- Do NOT override guardrail failures\n- Separate statistical significance from business judgment\n\n### Interpretation Outcomes\n\n| Result               | Action                                 |\n| -------------------- | -------------------------------------- |\n| Significant positive | Consider rollout                       |\n| Significant negative | Reject variant, document learning      |\n| Inconclusive         | Consider more traffic or bolder change |\n| Guardrail failure    | Do not ship, even if primary wins      |\n\n---\n\n## Documentation & Learning\n\n### Test Record (Mandatory)\n\nDocument:\n\n- Hypothesis\n- Variants\n- Metrics\n- Sample size vs achieved\n- Results\n- Decision\n- Learnings\n- Follow-up ideas\n\nStore records in a shared, searchable location to avoid repeated failures.\n\n---\n\n## Refusal Conditions (Safety)\n\nRefuse to proceed if:\n\n- Baseline rate is unknown and cannot be estimated\n- Traffic is insufficient to detect the MDE\n- Primary metric is undefined\n- Multiple variables are changed without proper design\n- Hypothesis cannot be clearly stated\n\nExplain why and recommend next steps.\n\n---\n\n## Key Principles (Non-Negotiable)\n\n- One hypothesis per test\n- One primary metric\n- Commit before launch\n- No peeking\n- Learning over winning\n- Statistical rigor first\n\n---\n\n## Final Reminder\n\nA\u002FB testing is not about proving ideas right.\nIt is about **learning the truth with confidence**.\n\nIf you feel tempted to rush, simplify, or “just try it” —\nthat is the signal to **slow down and re-check the design**.\n","# A\u002FB Test Setup\n\n## 1️⃣ Purpose & Scope\n\nEnsure every A\u002FB test is **valid, rigorous, and safe** before a single line of code is written.\n\n- Prevents \"peeking\"\n- Enforces statistical power\n- Blocks invalid hypotheses\n\n---\n\n## 2️⃣ Pre-Requisites\n\nYou must have:\n\n- A clear user problem\n- Access to an analytics source\n- Roughly estimated traffic volume\n\n### Hypothesis Quality Checklist\n\nA valid hypothesis includes:\n\n- Observation or evidence\n- Single, specific change\n- Directional expectation\n- Defined audience\n- Measurable success criteria\n\n---\n\n### 3️⃣ Hypothesis Lock (Hard Gate)\n\nBefore designing variants or metrics, you MUST:\n\n- Present the **final hypothesis**\n- Specify:\n  - Target audience\n  - Primary metric\n  - Expected direction of effect\n  - Minimum Detectable Effect (MDE)\n\nAsk explicitly:\n\n> “Is this the final hypothesis we are committing to for this test?”\n\n**Do NOT proceed until confirmed.**\n\n---\n\n### 4️⃣ Assumptions & Validity Check (Mandatory)\n\nExplicitly list assumptions about:\n\n- Traffic stability\n- User independence\n- Metric reliability\n- Randomization quality\n- External factors (seasonality, campaigns, releases)\n\nIf assumptions are weak or violated:\n\n- Warn the user\n- Recommend delaying or redesigning the test\n\n---\n\n### 5️⃣ Test Type Selection\n\nChoose the simplest valid test:\n\n- **A\u002FB Test** – single change, two variants\n- **A\u002FB\u002Fn Test** – multiple variants, higher traffic required\n- **Multivariate Test (MVT)** – interaction effects, very high traffic\n- **Split URL Test** – major structural changes\n\nDefault to **A\u002FB** unless there is a clear reason otherwise.\n\n---\n\n### 6️⃣ Metrics Definition\n\n#### Primary Metric (Mandatory)\n\n- Single metric used to evaluate success\n- Directly tied to the hypothesis\n- Pre-defined and frozen before launch\n\n#### Secondary Metrics\n\n- Provide context\n- Explain _why_ results occurred\n- Must not override the primary metric\n\n#### Guardrail Metrics\n\n- Metrics that must not degrade\n- Used to prevent harmful wins\n- Trigger test stop if significantly negative\n\n---\n\n### 7️⃣ Sample Size & Duration\n\nDefine upfront:\n\n- Baseline rate\n- MDE\n- Significance level (typically 95%)\n- Statistical power (typically 80%)\n\nEstimate:\n\n- Required sample size per variant\n- Expected test duration\n\n**Do NOT proceed without a realistic sample size estimate.**\n\n---\n\n### 8️⃣ Execution Readiness Gate (Hard Stop)\n\nYou may proceed to implementation **only if all are true**:\n\n- Hypothesis is locked\n- Primary metric is frozen\n- Sample size is calculated\n- Test duration is defined\n- Guardrails are set\n- Tracking is verified\n\nIf any item is missing, stop and resolve it.\n\n---\n\n## Running the Test\n\n### During the Test\n\n**DO:**\n\n- Monitor technical health\n- Document external factors\n\n**DO NOT:**\n\n- Stop early due to “good-looking” results\n- Change variants mid-test\n- Add new traffic sources\n- Redefine success criteria\n\n---\n\n## Analyzing Results\n\n### Analysis Discipline\n\nWhen interpreting results:\n\n- Do NOT generalize beyond the tested population\n- Do NOT claim causality beyond the tested change\n- Do NOT override guardrail failures\n- Separate statistical significance from business judgment\n\n### Interpretation Outcomes\n\n| Result               | Action                                 |\n| -------------------- | -------------------------------------- |\n| Significant positive | Consider rollout                       |\n| Significant negative | Reject variant, document learning      |\n| Inconclusive         | Consider more traffic or bolder change |\n| Guardrail failure    | Do not ship, even if primary wins      |\n\n---\n\n## Documentation & Learning\n\n### Test Record (Mandatory)\n\nDocument:\n\n- Hypothesis\n- Variants\n- Metrics\n- Sample size vs achieved\n- Results\n- Decision\n- Learnings\n- Follow-up ideas\n\nStore records in a shared, searchable location to avoid repeated failures.\n\n---\n\n## Refusal Conditions (Safety)\n\nRefuse to proceed if:\n\n- Baseline rate is unknown and cannot be estimated\n- Traffic is insufficient to detect the MDE\n- Primary metric is undefined\n- Multiple variables are changed without proper design\n- Hypothesis cannot be clearly stated\n\nExplain why and recommend next steps.\n\n---\n\n## Key Principles (Non-Negotiable)\n\n- One hypothesis per test\n- One primary metric\n- Commit before launch\n- No peeking\n- Learning over winning\n- Statistical rigor first\n\n---\n\n## Final Reminder\n\nA\u002FB testing is not about proving ideas right.\nIt is about **learning the truth with confidence**.\n\nIf you feel tempted to rush, simplify, or “just try it” —\nthat is the signal to **slow down and re-check the design**.\n",{"name":5,"description":7},"main","eac60cc6e0b6baa2a05a765a851fb10638f4e733",".agent\u002Fskills\u002Fskills\u002Fab-test-setup","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist","https:\u002F\u002Fgithub.com\u002FGabriellmaia01","https:\u002F\u002Fportfolio-bice-theta-81.vercel.app\u002F","💻 Desenvolvimento De Software_\r\n\r\n⚙️ Engenharia De Software\r\n","2026-11-10 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Zapier and Make (formerly Integromat) let non-developers automate business processes without writing code. But no-code doesn't mean no-complexity - these platforms have their own patterns, pitfalls, and breaking points.  This skill covers when to use which platform, how to build reliable automations, and when to graduate to code-based solutions. Key insight: Zapier optimizes for simplicity and integrations (7000+ apps), Make optimizes for power","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fzapier-make-patterns\u002FSKILL.md",[67],{"categoryNo":68,"categoryName":69,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[71,72,73,74,75,76,77,78,79,80,81,82,83,84],"zapier make patterns","writing","workflow","automation","code","apache","how","automate","integrations","business","use","simplicity","power","complexity",{"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":95,"publishedAt":33},"01M6CCFSBPM49S618W7G4NHH13","Cross-Site Scripting and HTML Injection Testing","This skill should be used when the user asks to \"test for XSS vulnerabilities\", \"perform cross-site scripting attacks\", \"identify HTML injection flaws\", \"exploit client-side injection vulnerabilities\", \"steal cookies via XSS\", or \"bypass content security policies\". It provides comprehensive techniques for detecting, exploiting, and understanding XSS and HTML injection attack vectors in web applications.","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fxss-html-injection\u002FSKILL.md",[91,92],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":93,"categoryName":94,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[96,97,98,99,100,101,102,103,104,105,106,107,108,24,109,110,111],"injection","scripting","html","site","Cross Site Scripting and HTML Injection Testing","testing","html injection testing","cookies","applications","xss","exploit","content","web","vulnerabilities","user","security",{"skillNo":113,"name":114,"nameZh":6,"description":115,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":116,"categories":117,"keywords":127,"publishedAt":33},"01M6CCFJ9XTPXHTE4JR06WY475","writing-skills","Use when creating new skills, editing existing skills, or verifying skills work before deployment","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fwriting-skills\u002FSKILL.md",[118,119,122],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":120,"categoryName":121,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":123,"categoryName":124,"taxonomyType":125,"taxonomyTypeDesc":126},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",[72,128,129,130,131,81],"writing skills","deployment","work","editing",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":140,"publishedAt":33},"01M6CCFG98EMZZ43D1CPZZZ54E","writing-plans","Use when you have a spec or requirements for a multi-step task, before touching code","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fwriting-plans\u002FSKILL.md",[138,139],{"categoryNo":120,"categoryName":121,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":123,"categoryName":124,"taxonomyType":125,"taxonomyTypeDesc":126},[72,141,142,75,81],"writing plans","spec",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":150,"publishedAt":33},"01M6CCFDPACVC9ZM8TN63MGBP5","workflow-automation","Workflow automation is the infrastructure that makes AI agents reliable. 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Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fworkflow-automation\u002FSKILL.md",[149],{"categoryNo":68,"categoryName":69,"taxonomyType":21,"taxonomyTypeDesc":22},[151,74,73,152,153,154,155,156,76,157,158,30,159,160,161,162],"Workflow Automation","network","ai agents","accessibility","workflows","infrastructure","flow","Production","inngest","parallel","orchestrator","resume",{"skillNo":164,"name":165,"nameZh":6,"description":166,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":167,"categories":168,"keywords":170,"publishedAt":33},"01M6CCFB0KAB0PPNV38XQPX6YX","WordPress Penetration Testing","This skill should be used when the user asks to \"pentest WordPress sites\", \"scan WordPress for vulnerabilities\", \"enumerate WordPress users, themes, or plugins\", \"exploit WordPress vulnerabilities\", or \"use WPScan\". It provides comprehensive WordPress security assessment methodologies.","https:\u002F\u002Fgithub.com\u002FGabriellmaia01\u002Fneuro-dose-assist\u002Fblob\u002Fmain\u002F.agent\u002Fskills\u002Fskills\u002Fwordpress-penetration-testing\u002FSKILL.md",[169],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[101,165,171,172,111,109,173,174,110,81,106,175,176],"penetration testing","wordpress","assessment","plugins","pentest","security assessment"]