[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M73ABCCMAQ7PVQ6HD8902CSZ":3,"skill-same:01M73ABCCMAQ7PVQ6HD8902CSZ":65},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":18,"linkOnly":19,"markdownContent":20,"markdownBody":21,"frontMatter":22,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":36,"repositoryHtmlUrl":37,"repositoryHomepageUrl":6,"ownerHtmlUrl":38,"ownerBlogUrl":39,"ownerDescription":6,"updatedAt":40,"install":41,"resources":54},"01M73ABCCMAQ7PVQ6HD8902CSZ","measure-experiment-results",null,"Documents the results of a completed experiment or A\u002FB test with statistical analysis, learnings, and recommendations. Use after experiments conclude to communicate findings, inform decisions, and build organizational knowledge.","roohe\u002Fagentic-super-skills","roohe","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F99054084?v=4","01M734WCDFP6RZVT5CNM00QR26","01M734WCE4BD0T4NZS5PCV9V5F",10,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills\u002Fblob\u002Fmaster\u002Fskills_library\u002Fmeasure-experiment-results\u002FSKILL.md",[],[],"2026-11-11 15:11:14",false,"---\nname: measure-experiment-results\ndescription: Documents the results of a completed experiment or A\u002FB test with statistical analysis, learnings, and recommendations. Use after experiments conclude to communicate findings, inform decisions, and build organizational knowledge.\nphase: measure\nversion: \"2.0.0\"\nupdated: 2026-01-26\nlicense: Apache-2.0\nmetadata:\n  category: reflection\n  frameworks: [triple-diamond, lean-startup, design-thinking]\n  author: product-on-purpose\n---\n\u003C!-- PM-Skills | https:\u002F\u002Fgithub.com\u002Fproduct-on-purpose\u002Fpm-skills | Apache 2.0 -->\n# Experiment Results\n\nAn experiment results document captures what happened when you tested a hypothesis, including statistical outcomes, segment analysis, learnings, and clear recommendations. Good results documentation turns individual experiments into organizational knowledge that improves future decision-making.\n\n## When to Use\n\n- After an A\u002FB test or experiment reaches statistical significance\n- When an experiment is ended early (for any reason)\n- To communicate findings to stakeholders who weren't involved\n- During decision-making about whether to ship, iterate, or kill a feature\n- To build a repository of learnings that inform future experiments\n\n## Instructions\n\nWhen asked to document experiment results, follow these steps:\n\n1. **Summarize the Experiment**\n   Provide context: what was tested, when it ran, how much traffic it received. Link to the original experiment design document if one exists.\n\n2. **Restate the Hypothesis**\n   Remind readers what you believed would happen and why. This frames the results interpretation.\n\n3. **Present Primary Results**\n   Show the primary metric outcome clearly: what were the values for control and treatment? Include statistical significance (p-value), confidence intervals, and sample sizes. Be honest about whether results are conclusive.\n\n4. **Analyze Secondary Metrics**\n   Present guardrail metrics that ensure you didn't cause unintended harm. Note any secondary metrics that moved unexpectedly.both positive and negative.\n\n5. **Segment the Data**\n   Look for differential effects across user segments (platform, tenure, plan type, etc.). Sometimes overall results mask important segment-level insights.\n\n6. **Extract Learnings**\n   What did you learn beyond the numbers? Include surprising findings, questions raised, and implications for the product hypothesis. Negative results are valuable learnings.\n\n7. **Make a Recommendation**\n   Be clear: should we ship, iterate, or kill? Support the recommendation with the evidence. If the decision is nuanced, explain the trade-offs.\n\n8. **Define Next Steps**\n   Specify what happens now.engineering work to ship, follow-up experiments, metrics to continue monitoring, or documentation to update.\n\n## Output Format\n\nUse the template in `references\u002FTEMPLATE.md` to structure the output.\n\n## Quality Checklist\n\nBefore finalizing, verify:\n\n- [ ] Statistical methods and significance are clearly stated\n- [ ] Confidence intervals are included (not just p-values)\n- [ ] Segment analysis checked for differential effects\n- [ ] Secondary\u002Fguardrail metrics are reported\n- [ ] Learnings go beyond just the numbers\n- [ ] Recommendation is clear and actionable\n- [ ] Negative or inconclusive results are reported honestly\n\n## Examples\n\nSee `references\u002FEXAMPLE.md` for a completed example.\n","\u003C!-- PM-Skills | https:\u002F\u002Fgithub.com\u002Fproduct-on-purpose\u002Fpm-skills | Apache 2.0 -->\n# Experiment Results\n\nAn experiment results document captures what happened when you tested a hypothesis, including statistical outcomes, segment analysis, learnings, and clear recommendations. Good results documentation turns individual experiments into organizational knowledge that improves future decision-making.\n\n## When to Use\n\n- After an A\u002FB test or experiment reaches statistical significance\n- When an experiment is ended early (for any reason)\n- To communicate findings to stakeholders who weren't involved\n- During decision-making about whether to ship, iterate, or kill a feature\n- To build a repository of learnings that inform future experiments\n\n## Instructions\n\nWhen asked to document experiment results, follow these steps:\n\n1. **Summarize the Experiment**\n   Provide context: what was tested, when it ran, how much traffic it received. Link to the original experiment design document if one exists.\n\n2. **Restate the Hypothesis**\n   Remind readers what you believed would happen and why. This frames the results interpretation.\n\n3. **Present Primary Results**\n   Show the primary metric outcome clearly: what were the values for control and treatment? Include statistical significance (p-value), confidence intervals, and sample sizes. Be honest about whether results are conclusive.\n\n4. **Analyze Secondary Metrics**\n   Present guardrail metrics that ensure you didn't cause unintended harm. Note any secondary metrics that moved unexpectedly.both positive and negative.\n\n5. **Segment the Data**\n   Look for differential effects across user segments (platform, tenure, plan type, etc.). Sometimes overall results mask important segment-level insights.\n\n6. **Extract Learnings**\n   What did you learn beyond the numbers? Include surprising findings, questions raised, and implications for the product hypothesis. Negative results are valuable learnings.\n\n7. **Make a Recommendation**\n   Be clear: should we ship, iterate, or kill? Support the recommendation with the evidence. If the decision is nuanced, explain the trade-offs.\n\n8. **Define Next Steps**\n   Specify what happens now.engineering work to ship, follow-up experiments, metrics to continue monitoring, or documentation to update.\n\n## Output Format\n\nUse the template in `references\u002FTEMPLATE.md` to structure the output.\n\n## Quality Checklist\n\nBefore finalizing, verify:\n\n- [ ] Statistical methods and significance are clearly stated\n- [ ] Confidence intervals are included (not just p-values)\n- [ ] Segment analysis checked for differential effects\n- [ ] Secondary\u002Fguardrail metrics are reported\n- [ ] Learnings go beyond just the numbers\n- [ ] Recommendation is clear and actionable\n- [ ] Negative or inconclusive results are reported honestly\n\n## Examples\n\nSee `references\u002FEXAMPLE.md` for a completed example.\n",{"name":5,"phase":23,"license":14,"updated":24,"version":25,"metadata":26,"description":7},"measure",1769385600000,"2.0.0",{"author":27,"category":28,"frameworks":29},"product-on-purpose","reflection",[30,31,32],"triple-diamond","lean-startup","design-thinking","master","f483e54f3ad5f202a77d45f2e57f8d970b71195f","skills_library\u002Fmeasure-experiment-results",2,"https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills","https:\u002F\u002Fgithub.com\u002Froohe","yoou.link","2026-05-19 15:56:07",{"source":8,"skillName":5,"commands":42,"prompt":52,"zipAvailable":53},[43,46,49],{"tool":44,"command":45},"npx","npx skills add roohe\u002Fagentic-super-skills --skill measure-experiment-results",{"tool":47,"command":48},"bunx","bunx skills add roohe\u002Fagentic-super-skills --skill measure-experiment-results",{"tool":50,"command":51},"pnpm","pnpm dlx skills add roohe\u002Fagentic-super-skills --skill measure-experiment-results","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills\u002Fblob\u002Fmaster\u002Fskills_library\u002Fmeasure-experiment-results\u002FSKILL.md",true,[55,61],{"path":56,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":59,"sourceHtmlUrl":60},"references\u002FEXAMPLE.md","REFERENCE","参考文档",9136,"https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills\u002Fblob\u002Fmaster\u002Fskills_library\u002Fmeasure-experiment-results\u002Freferences\u002FEXAMPLE.md",{"path":62,"resourceType":57,"resourceTypeDesc":58,"sizeBytes":63,"sourceHtmlUrl":64},"references\u002FTEMPLATE.md",4301,"https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills\u002Fblob\u002Fmaster\u002Fskills_library\u002Fmeasure-experiment-results\u002Freferences\u002FTEMPLATE.md",[66,74,81,88,95,102],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":70,"licenseFileUrl":6,"sourceHtmlUrl":71,"categories":72,"keywords":73,"publishedAt":18},"01M73D865CZME2FCMKA4PQM348","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","MIT","https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills\u002Fblob\u002Fmaster\u002Fskills_library\u002Fzarr-python\u002FSKILL.md",[],[],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":70,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":18},"01M73D7Z64J4ABWZJD7P44XBBF","youtube-downloader","Download and process YouTube content for research. Use when: downloading competitor videos for analysis; extracting audio for podcasts; getting transcripts for content repurposing; archiving webinars; research content curation","https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills\u002Fblob\u002Fmaster\u002Fskills_library\u002Fyoutube-downloader\u002FSKILL.md",[],[],{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":70,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":87,"publishedAt":18},"01M73D7K8778TGZV3GJFR3703D","yc-pitch-deck","Create a compelling startup pitch deck that follows the structure proven to raise billions from top investors. Master the YC and Sequoia formats that get founders funded. Use when: **Fundraising** to create investor pitch decks; **YC application** to structure your narrative; **Demo Day prep** to craft your 2-minute pitch; **Angel investors** to communicate your opportunity; **Partnership pitches** to structure compelling asks","https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills\u002Fblob\u002Fmaster\u002Fskills_library\u002Fyc-pitch-deck\u002FSKILL.md",[],[],{"skillNo":89,"name":90,"nameZh":6,"description":91,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":70,"licenseFileUrl":6,"sourceHtmlUrl":92,"categories":93,"keywords":94,"publishedAt":18},"01M73D70KJSE0E8Z28YMNNRT04","x-twitter-scraper","Use when the user needs X (Twitter) data or confirmation-gated X actions through Xquik: tweet search, user lookup, follower extraction, media download, monitoring, webhooks, MCP, SDKs, posting, likes, DMs, and profile updates. Requires a Xquik API key. Never ask for X login material.","https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills\u002Fblob\u002Fmaster\u002Fskills_library\u002Fx-twitter-scraper\u002FSKILL.md",[],[],{"skillNo":96,"name":97,"nameZh":6,"description":98,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":70,"licenseFileUrl":6,"sourceHtmlUrl":99,"categories":100,"keywords":101,"publishedAt":18},"01M73D6ED01GV077R7K4NZNFZJ","write-vignette","Create R package vignettes using R Markdown or Quarto. Covers vignette setup, YAML configuration, code chunk options, building and testing, and CRAN requirements for vignettes. Use when adding a Getting Started tutorial, documenting complex workflows spanning multiple functions, creating domain-specific guides, or when CRAN submission requires user-facing documentation beyond function help pages.","https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills\u002Fblob\u002Fmaster\u002Fskills_library\u002Fwrite-vignette\u002FSKILL.md",[],[],{"skillNo":103,"name":104,"nameZh":6,"description":105,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":70,"licenseFileUrl":6,"sourceHtmlUrl":106,"categories":107,"keywords":108,"publishedAt":18},"01M73D69YJR0BK3NFHE0CYG3EF","write-validation-documentation","Write IQ\u002FOQ\u002FPQ validation documentation for computerized systems in regulated environments. Covers protocols, reports, test scripts, deviation handling, and approval workflows. Use when validating R or other software for regulated use, preparing for a regulatory audit, documenting qualification of computing environments, or creating and updating validation protocols and reports for new or re-qualified systems.","https:\u002F\u002Fgithub.com\u002Froohe\u002Fagentic-super-skills\u002Fblob\u002Fmaster\u002Fskills_library\u002Fwrite-validation-documentation\u002FSKILL.md",[],[]]