[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6GZVWCFD8DGDVGSP1AYATR7":3,"skill-same:01M6GZVWCFD8DGDVGSP1AYATR7":78},{"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":16,"sourceHtmlUrl":17,"categories":18,"keywords":24,"publishedAt":36,"linkOnly":37,"markdownContent":38,"markdownBody":39,"frontMatter":40,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":44,"repositoryHtmlUrl":45,"repositoryHomepageUrl":6,"ownerHtmlUrl":46,"ownerBlogUrl":47,"ownerDescription":48,"updatedAt":49,"install":50,"resources":63},"01M6GZVWCFD8DGDVGSP1AYATR7","report-uncertainty-ranges",null,"Use when stating any estimate or measurement. Attaches an interval and the source of the uncertainty instead of a single false-precision number.","jiayaoqijia\u002Fcryptoskill","jiayaoqijia","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1453879?v=4","01M6GMYTX7Q7S504VC6E756ENR","YQ","01M6GMYV1QKJ1CC75MTTGT9PR0",80,"MIT","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fpayments\u002F0xklen-report-uncertainty-ranges\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fpayments\u002F0xklen-report-uncertainty-ranges\u002FSKILL.md",[19],{"categoryNo":20,"categoryName":21,"taxonomyType":22,"taxonomyTypeDesc":23},"01JBSEEDCAT000000000000034","行政与办公人员","PROFESSION","适用职业",[25,26,27,28,29,30,31,32,33,34,35],"report","uncertainty","report uncertainty ranges","use","false","measurement","number","source","estimate","single","when","2026-11-10 10:12:30",false,"---\nname: report-uncertainty-ranges\ndescription: Use when stating any estimate or measurement. Attaches an interval and the source of the uncertainty instead of a single false-precision number.\n---\n\n# Report Uncertainty Ranges\n\nA point estimate without an interval tells the reader the number is exact when it is not. Quantify the range or say plainly that you cannot.\n\n## Procedure\n\n1. Name the uncertainty type: sampling (finite n), measurement (instrument\u002Fannotation error), model (assumption), or scenario (future path). They compose differently.\n2. For sampling uncertainty, give 95% intervals. Bootstrap is the fallback for any statistic without a closed form:\n   ```python\n   import numpy as np\n   rng = np.random.default_rng(0)\n   boots = [np.median(rng.choice(x, x.size, replace=True)) for _ in range(10000)]\n   lo, hi = np.percentile(boots, [2.5, 97.5])\n   ```\n3. For a proportion, use a Wilson interval, not the textbook normal one, when p is near 0 or 1 or n is small.\n4. Propagate measurement error rather than ignoring it: for a product `f = a·b`, `σ_f\u002F|f| = sqrt((σ_a\u002Fa)² + (σ_b\u002Fb)²)`.\n5. Report intervals that describe the quantity you mean. A 95% CI is not \"95% likely to contain the true value\" under every interpretation — say which.\n6. Do not shrink an interval to look decisive; do not widen it to cover a claim it cannot support. If the interval spans the decision threshold, say the data is inconclusive.\n7. Round the interval to the precision of the estimate: `12.4% (95% CI 9.1–15.7)`, not `12.4312 ± 0.4937`.\n\n```bash\npython3 -c \"from statsmodels.stats.proportion import proportion_confint; \\\nprint(proportion_confint(37, 210, method='wilson'))\"\n```\n\n## Pitfalls\n\n- A CI on a biased estimator is precisely wrong; uncertainty about bias is separate and usually larger.\n- The interval says nothing about systematic error you did not model — name it.\n- Error bars that are ±1 SEM look 3.9× narrower than a 95% interval and mislead by convention.\n- Combining intervals by adding half-widths is wrong for independent errors; combine variances.\n- Too many significant figures implies precision the interval contradicts.\n\n## Verification\n\n    grep -cE '95%|CI|interval|±|\\[[0-9.]+, ?[0-9.]+\\]' estimates.md\n\nEvery headline number carries an interval or an explicit \"no interval available: reason\". Report: \"Estimate 12.4% (95% CI 9.1–15.7, Wilson, n=210); model bias not quantified.\"\n","# Report Uncertainty Ranges\n\nA point estimate without an interval tells the reader the number is exact when it is not. Quantify the range or say plainly that you cannot.\n\n## Procedure\n\n1. Name the uncertainty type: sampling (finite n), measurement (instrument\u002Fannotation error), model (assumption), or scenario (future path). They compose differently.\n2. For sampling uncertainty, give 95% intervals. Bootstrap is the fallback for any statistic without a closed form:\n   ```python\n   import numpy as np\n   rng = np.random.default_rng(0)\n   boots = [np.median(rng.choice(x, x.size, replace=True)) for _ in range(10000)]\n   lo, hi = np.percentile(boots, [2.5, 97.5])\n   ```\n3. For a proportion, use a Wilson interval, not the textbook normal one, when p is near 0 or 1 or n is small.\n4. Propagate measurement error rather than ignoring it: for a product `f = a·b`, `σ_f\u002F|f| = sqrt((σ_a\u002Fa)² + (σ_b\u002Fb)²)`.\n5. Report intervals that describe the quantity you mean. A 95% CI is not \"95% likely to contain the true value\" under every interpretation — say which.\n6. Do not shrink an interval to look decisive; do not widen it to cover a claim it cannot support. If the interval spans the decision threshold, say the data is inconclusive.\n7. Round the interval to the precision of the estimate: `12.4% (95% CI 9.1–15.7)`, not `12.4312 ± 0.4937`.\n\n```bash\npython3 -c \"from statsmodels.stats.proportion import proportion_confint; \\\nprint(proportion_confint(37, 210, method='wilson'))\"\n```\n\n## Pitfalls\n\n- A CI on a biased estimator is precisely wrong; uncertainty about bias is separate and usually larger.\n- The interval says nothing about systematic error you did not model — name it.\n- Error bars that are ±1 SEM look 3.9× narrower than a 95% interval and mislead by convention.\n- Combining intervals by adding half-widths is wrong for independent errors; combine variances.\n- Too many significant figures implies precision the interval contradicts.\n\n## Verification\n\n    grep -cE '95%|CI|interval|±|\\[[0-9.]+, ?[0-9.]+\\]' estimates.md\n\nEvery headline number carries an interval or an explicit \"no interval available: reason\". Report: \"Estimate 12.4% (95% CI 9.1–15.7, Wilson, n=210); model bias not quantified.\"\n",{"name":5,"description":7},"main","3df1d0d67b30ff15236561f8c0f9f06d33f42dfd","skills\u002Fpayments\u002F0xklen-report-uncertainty-ranges",17,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia","https:\u002F\u002Feth2030.com","Hardcore infra. Building @alt-research and applied cryptography.","2026-11-11 08:47:40",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add jiayaoqijia\u002Fcryptoskill --skill report-uncertainty-ranges",{"tool":56,"command":57},"bunx","bunx skills add jiayaoqijia\u002Fcryptoskill --skill report-uncertainty-ranges",{"tool":59,"command":60},"pnpm","pnpm dlx skills add jiayaoqijia\u002Fcryptoskill --skill report-uncertainty-ranges","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fpayments\u002F0xklen-report-uncertainty-ranges\u002FSKILL.md",true,[64,68,74],{"path":65,"resourceType":65,"resourceTypeDesc":66,"sizeBytes":67,"sourceHtmlUrl":16},"LICENSE","许可证",1063,{"path":69,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":72,"sourceHtmlUrl":73},"SOURCE.md","REFERENCE","参考文档",351,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fpayments\u002F0xklen-report-uncertainty-ranges\u002FSOURCE.md",{"path":75,"resourceType":70,"resourceTypeDesc":71,"sizeBytes":76,"sourceHtmlUrl":77},"TRUST.auto.yaml",2197,"https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fpayments\u002F0xklen-report-uncertainty-ranges\u002FTRUST.auto.yaml",[79,107,130,148,161,171],{"skillNo":80,"name":81,"nameZh":6,"description":82,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":83,"sourceHtmlUrl":84,"categories":85,"keywords":86,"publishedAt":36},"01M712C436EQYE43342Q4E7GG1","recipe-performance","Diagnose a specific MetaMask user flow's real-app performance and Sentry critical-user-flow telemetry with deterministic mm-harness recipes. Use for CUF boundaries, cold\u002Fwarm\u002Fbackground-resume behavior, latency regressions, lifecycle tags, duplicate or blocking network requests, long tasks, layout\u002Fpaint cost, screen redraws, React rerenders, or browser trace analysis where tests alone cannot prove the real app.","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fwallets\u002Fmetamask-official-recipe-performance\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fwallets\u002Fmetamask-official-recipe-performance\u002FSKILL.md",[],[87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106],"performance","recipe 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keys.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fexchanges\u002Fmncrftfrcnm-mexc-spot-websocket\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fexchanges\u002Fmncrftfrcnm-mexc-spot-websocket\u002FSKILL.md",[],[116,117,118,119,120,121,122,123,124,125,126,127,128,129],"mexc spot websocket","spot","websocket","api","workflows","public","listen","streams","depth","keys","for","api keys","maintenance","protobuf",{"skillNo":131,"name":132,"nameZh":6,"description":133,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":134,"sourceHtmlUrl":135,"categories":136,"keywords":137,"publishedAt":36},"01M70XT0MBXN3WZY1BKN0CPNKR","mexc-spot-rest","MEXC Spot REST workflows for public data, signed account requests, orders, wallet\u002Fadmin actions, and secure signing without exposing API keys.","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fexchanges\u002Fmncrftfrcnm-mexc-spot-rest\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fexchanges\u002Fmncrftfrcnm-mexc-spot-rest\u002FSKILL.md",[],[138,117,139,140,141,142,143,127,126,125,144,145,146,121,147,120,119],"mexc spot rest","REST","admin","signing","Wallet","requests","actions","secure","data","orders",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":111,"licenseFileUrl":152,"sourceHtmlUrl":153,"categories":154,"keywords":155,"publishedAt":36},"01M70XSYSVZ2N0N91XCAR2CAK0","mexc-futures-websocket","MEXC Futures WebSocket workflows for public streams, private account\u002Forder\u002Fposition streams, ping\u002Fpong, subscriptions, and depth maintenance.","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fexchanges\u002Fmncrftfrcnm-mexc-futures-websocket\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fexchanges\u002Fmncrftfrcnm-mexc-futures-websocket\u002FSKILL.md",[],[156,157,118,158,159,128,126,160,124,123,121,120],"mexc futures websocket","futures","position","ping","subscriptions",{"skillNo":162,"name":163,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":164,"sourceHtmlUrl":165,"categories":166,"keywords":167,"publishedAt":36},"01M6H0ZWHVE5W04WG2DKQE2SQC","0xklen-version-and-refresh-sanctions-lists","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fpayments\u002F0xklen-version-and-refresh-sanctions-lists\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fpayments\u002F0xklen-version-and-refresh-sanctions-lists\u002FSKILL.md",[],[168,169,170],"refresh","0xklen version and refresh sanctions lists","version",{"skillNo":172,"name":173,"nameZh":6,"description":174,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":175,"sourceHtmlUrl":176,"categories":177,"keywords":178,"publishedAt":36},"01M6H0Z28Q3R8J0S799GJQWXM3","verify-tests-cover-changed-lines","Use when a PR adds code that the existing suite may not exercise. Measures patch coverage — the share of newly changed lines hit by tests — before approving.","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fpayments\u002F0xklen-verify-tests-cover-changed-lines\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fjiayaoqijia\u002Fcryptoskill\u002Fblob\u002Fmain\u002Fskills\u002Fpayments\u002F0xklen-verify-tests-cover-changed-lines\u002FSKILL.md",[],[179,180,181,182,183,184,185,28,186,187,188],"tests","cover","verify tests cover changed lines","verify","coverage","pr","exercise","patch","code","share"]