[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6MF29CMSMACBR720PD9K1R0":3,"skill-same:01M6MF29CMSMACBR720PD9K1R0":75},{"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":25,"publishedAt":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":13,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6MF29CMSMACBR720PD9K1R0","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.","JiayiV\u002Fquestion_mixing","JiayiV","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F54466889?v=4","01M6JFJYYMRZ3QRGP9339JNQ2K","01M6JFJZ35KB0QVT50NAHPJA7G",0,"MIT","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fpublic-02-claude-skills-main-gemini-skills-ab-test-setup-skill-647213f5\u002FSKILL.md",[17,22],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000008","测试与质量","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000015","视觉设计",[26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"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: Alireza Rezvani\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### Calculate It (bundled tool)\n\nUse this skill's own calculator — don't eyeball it:\n\n```bash\npython3 scripts\u002Fsample_size_calculator.py --baseline 0.05 --mde 0.20            # human-readable\npython3 scripts\u002Fsample_size_calculator.py --baseline 0.05 --mde 0.20 --json    # for pipelines\npython3 scripts\u002Fsample_size_calculator.py --baseline 0.05 --mde 0.20 --daily-traffic 2000  # adds test-duration estimate\n```\n\nPaste `sample_size_per_variation` and the duration estimate directly into the test plan's \"Sample size + duration\" row before any test is approved to run.\n\n### Quick Reference\n\nGenerated by `sample_size_calculator.py` (two-proportion z-test, α=0.05 two-tailed, 80% power; relative MDE):\n\n| Baseline | 10% Lift | 20% Lift | 50% Lift |\n|----------|----------|----------|----------|\n| 1% | 163k\u002Fvariant | 43k\u002Fvariant | 7.7k\u002Fvariant |\n| 3% | 53k\u002Fvariant | 14k\u002Fvariant | 2.5k\u002Fvariant |\n| 5% | 31k\u002Fvariant | 8.2k\u002Fvariant | 1.5k\u002Fvariant |\n| 10% | 15k\u002Fvariant | 3.8k\u002Fvariant | 683\u002Fvariant |\n\n**Cross-check calculators** (should agree with the script within rounding):\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### Calculate It (bundled tool)\n\nUse this skill's own calculator — don't eyeball it:\n\n```bash\npython3 scripts\u002Fsample_size_calculator.py --baseline 0.05 --mde 0.20            # human-readable\npython3 scripts\u002Fsample_size_calculator.py --baseline 0.05 --mde 0.20 --json    # for pipelines\npython3 scripts\u002Fsample_size_calculator.py --baseline 0.05 --mde 0.20 --daily-traffic 2000  # adds test-duration estimate\n```\n\nPaste `sample_size_per_variation` and the duration estimate directly into the test plan's \"Sample size + duration\" row before any test is approved to run.\n\n### Quick Reference\n\nGenerated by `sample_size_calculator.py` (two-proportion z-test, α=0.05 two-tailed, 80% power; relative MDE):\n\n| Baseline | 10% Lift | 20% Lift | 50% Lift |\n|----------|----------|----------|----------|\n| 1% | 163k\u002Fvariant | 43k\u002Fvariant | 7.7k\u002Fvariant |\n| 3% | 53k\u002Fvariant | 14k\u002Fvariant | 2.5k\u002Fvariant |\n| 5% | 31k\u002Fvariant | 8.2k\u002Fvariant | 1.5k\u002Fvariant |\n| 10% | 15k\u002Fvariant | 3.8k\u002Fvariant | 683\u002Fvariant |\n\n**Cross-check calculators** (should agree with the script within rounding):\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":14,"metadata":51,"description":7},{"author":52,"updated":53,"version":54,"category":41},"Alireza Rezvani",1772755200000,"1.0.0","main","068c2486ccc0f8d200a3db4d21ec137064eda3b4","skills\u002Fpublic-02-claude-skills-main-gemini-skills-ab-test-setup-skill-647213f5","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing","https:\u002F\u002Fgithub.com\u002FJiayiV","2026-08-25 16:30:52",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add JiayiV\u002Fquestion_mixing --skill ab-test-setup",{"tool":67,"command":68},"bunx","bunx skills add JiayiV\u002Fquestion_mixing --skill ab-test-setup",{"tool":70,"command":71},"pnpm","pnpm dlx skills add JiayiV\u002Fquestion_mixing --skill ab-test-setup","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fpublic-02-claude-skills-main-gemini-skills-ab-test-setup-skill-647213f5\u002FSKILL.md",true,[],[76,100,128,159,187,222],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":82,"publishedAt":46},"01M6NN9PJS9NHWPDG4QP93BP8F","webmcpify","Make a web app agent-ready — propose a WebMCP tool manifest, integrate, verify in a real browser, heal; unrelated code stays untouched. Use for \"webmcpify\", \"add WebMCP\", or \"expose app actions to AI agents\".","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fwebmcpify\u002FSKILL.md",[],[78,83,84,85,86,87,88,89,90,91,92,93,94,40,95,96,97,98,35,99],"github","ai agents","web","notes","code","browser","https","actions","manifest","inventory","verify","tool","ready","unrelated","app","scope","webmcp",{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":104,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":46},"01M6NN6KZE5RBN6NGHN2WXV1RE","vardoger-analyze","Use when the user asks to personalize the GitHub Copilot CLI assistant, adapt Copilot to their style, use vardoger, or analyze their Copilot CLI conversation history. Reads the local session directory at `~\u002F.copilot\u002Fsession-state\u002F`, extracts recurring preferences and conventions, and writes a fenced personalization block into `~\u002F.copilot\u002Fcopilot-instructions.md`. Runs entirely on the user's machine via the local `vardoger` CLI (`pipx install vardoger`); no network calls and no uploads. Triggers: 'personalize my copilot', 'analyze my copilot history', 'tailor copilot to me', 'run vardoger', 'update my copilot instructions from history', 'make copilot learn my style'.","Apache-2.0","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fvardoger-analyze\u002FSKILL.md",[],[108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127],"vardoger analyze","analyze","directory","cli","github copilot","copilot","run","update","learn","network","my style","install","conversation","me","local","github copilot cli","pipx","my","at","copilot instructions",{"skillNo":129,"name":130,"nameZh":6,"description":131,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":132,"categories":133,"keywords":140,"publishedAt":46},"01M6NN3CZGQP3XAJ04BM1NNYGT","transloadit-media-processing","Process media files (video, audio, images, documents) using Transloadit. Use when asked to encode video to HLS\u002FMP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, or run any media processing pipeline. Covers 86+ processing robots for file transformation at scale.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Ftransloadit-media-processing\u002FSKILL.md",[134,137],{"categoryNo":135,"categoryName":136,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",{"categoryNo":138,"categoryName":139,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[141,142,143,144,145,146,147,148,149,150,151,152,153,40,154,155,156,157,35,158],"transloadit media processing","Media","media processing","processing","MCP","mcp server","documents","subtitles","pipeline","ocr","Video","Audio","mp4","images","files","transformation","free","hls",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":168,"publishedAt":46},"01M6NN28E92F9GH4WFYQ2H1Y15","terraform-azurerm-set-diff-analyzer","Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes. Use when reviewing terraform plan output for Azure resources like Application Gateway, Load Balancer, Firewall, Front Door, NSG, and other resources with Set-type attributes that cause spurious diffs due to internal ordering changes.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fterraform-azurerm-set-diff-analyzer\u002FSKILL.md",[165],{"categoryNo":166,"categoryName":167,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000011","运维与部署",[169,170,171,172,173,174,175,176,177,40,178,179,180,181,182,183,184,185,186,35],"terraform","SET","terraform azurerm set diff analyzer","diff","analyzer","diffs","azure","gateway","firewall","only","resource changes","provider","resources","load balancer","attributes","front","load","balancer",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":204,"publishedAt":46},"01M6NN1QMH2TDYWTH3XY3N5XPV","technical-job-search","Use this skill when a software engineer asks for help with job search tasks: parsing or analyzing a job description, tailoring a CV\u002Fresume, writing a cover letter, evaluating a job offer, or drafting a post-interview follow-up email. Do not activate for general career advice unrelated to an active job search action.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Ftechnical-job-search\u002FSKILL.md",[193,196,201],{"categoryNo":194,"categoryName":195,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":197,"categoryName":198,"taxonomyType":199,"taxonomyTypeDesc":200},"01JBSEEDCAT000000000000036","内容创作者","PROFESSION","适用职业",{"categoryNo":202,"categoryName":203,"taxonomyType":199,"taxonomyTypeDesc":200},"01JBSEEDCAT000000000000038","人力资源",[205,206,207,208,209,210,211,212,109,213,40,214,215,216,217,218,219,220,35,221],"search","Job Search","technical job search","technical","writing","tasks","email","parsing","interview","up","engineer","general","activate","do","task","cv","letter",{"skillNo":223,"name":224,"nameZh":6,"description":225,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":226,"categories":227,"keywords":228,"publishedAt":46},"01M6NMZFA67XYS1GS09WT42235","steno-mode","Shorthand-first response compression that cuts ~40% of response tokens while preserving technical precision and exact literals. Use when the user says \"steno mode\", \"shorthand mode\", \"compressed responses\", \"token reduction\", \"brief structured output\", or invokes \u002Fsteno. Supports four compression levels: lite, brief, court, machine. Do not trigger for requests needing polished prose such as onboarding\u002Ftutorial content, stakeholder or customer-facing copy, or teaching-focused explanations.","https:\u002F\u002Fgithub.com\u002FJiayiV\u002Fquestion_mixing\u002Fblob\u002Fmain\u002Fskills\u002Fsteno-mode\u002FSKILL.md",[],[229,230,231,232,233,208,234,40,218,235,236,237,238,239,240,241,242,243,35,244],"steno mode","mode","customer","brief","first","tutorial","teaching","tokens","as","trigger","structured output","machine","output","court","requests","response compression"]