[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QJ1SQFT70S2ZF5X9QWM0Z2":3,"skill-same:01M6QJ1SQFT70S2ZF5X9QWM0Z2":81},{"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":28,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":58,"commitSha":59,"skillDirectory":60,"forksCount":14,"repositoryHtmlUrl":61,"repositoryHomepageUrl":62,"ownerHtmlUrl":63,"ownerBlogUrl":64,"ownerDescription":65,"updatedAt":66,"install":67,"resources":80},"01M6QJ1SQFT70S2ZF5X9QWM0Z2","linkedin-analytics",null,"Use when someone wants to understand their own LinkedIn numbers — which posts worked, why reach dropped, whether a pattern is real, or how to test a hypothesis. Triggers on \"why did my reach drop\", \"what's working on my LinkedIn\", \"analyze my posts\", \"do carousels do better for me\", \"should I test this\", \"LinkedIn analytics\". Reads your own exported post data, reports medians and outlier bands, tests candidate patterns against a permutation null, and sizes a real experiment — refusing to conclude anything below 10 posts.","Tashima-Tarsh\u002FDisha6.6","Tashima-Tarsh","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F269906498?v=4","01M6PBH98X03XERH0HFT4PFB1P","NITISH KUMAR","01M6PBH9DA0M6KM3Z12P848F0K",0,"MIT","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Flinkedin-analytics\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000013","分析与可视化","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"linkedin","linkedin analytics","analytics","patterns","exported","candidate","my","which","posts","how to","pattern","drop","me","reports","experiment","analyze","marketing","why","how","data","2026-11-10 10:12:30",false,"---\nname: linkedin-analytics\ndescription: Use when someone wants to understand their own LinkedIn numbers — which posts worked, why reach dropped, whether a pattern is real, or how to test a hypothesis. Triggers on \"why did my reach drop\", \"what's working on my LinkedIn\", \"analyze my posts\", \"do carousels do better for me\", \"should I test this\", \"LinkedIn analytics\". Reads your own exported post data, reports medians and outlier bands, tests candidate patterns against a permutation null, and sizes a real experiment — refusing to conclude anything below 10 posts.\nlicense: MIT\nmetadata:\n  version: 1.0.0\n  author: Alireza Rezvani\n  category: marketing\n  updated: 2026-08-25\n---\n\n# LinkedIn Analytics — describe honestly, then refuse to over-conclude\n\nThe characteristic sentence of LinkedIn analytics is \"carousels do 3x better for me\", built\non four posts. With engagement as heavy-tailed as it is, four posts will show a 3x difference\nbetween almost any two groups you care to define. These three scripts stop that sentence\nbecoming a strategy.\n\n**Your own data only.** Nothing is fetched; scraping post or profile data is prohibited by\nUser Agreement §8.2 and none of this analysis needs it.\n\n## Workflow\n\n**1. Get the export.** LinkedIn Analytics → Post impressions → Export, or Settings → Data\nprivacy → Get a copy of your data. CSV and JSON both work.\n\n**2. Describe it.** Exit 0 analysed \u002F 2 below the 10-post floor, descriptive only \u002F 3\nunusable. Reports median and MAD rather than mean and standard deviation — one breakout post\nmakes a mean describe a distribution none of your posts belong to — plus Tukey percentile\nbands and a 1.5×IQR breakout threshold, so \"this did well\" has a number behind it.\n\n```bash\npython3 scripts\u002Fpost_performance_analyzer.py --input posts.csv --csv --output human\n```\n\n**3. Test the pattern they think they see.**\n\n```bash\npython3 scripts\u002Fpattern_miner.py --input posts.json --output human\n```\n\nExit 0 something survived \u002F 2 nothing survived \u002F 3 under 10 posts. Four gates: 5 posts in and\n5 out; a 15% relative difference in medians; beating 90% of 2,000 seeded label shuffles; and\na multiple-comparisons accounting of how many candidates would pass on noise alone.\n\n**\"Nothing survived\" is the most common honest answer and it is a real finding.** Report it\nas one. Do not soften it into a hedge that reads like a conclusion.\n\n**4. Turn a survivor into a test.**\n\n```bash\npython3 scripts\u002Fexperiment_planner.py --hypothesis \"...\" --variable \"...\" \\\n  --cv 0.45 --effect 0.30 --posts-per-week 2 --max-weeks 12 --output human\n```\n\nCV comes from step 2: `1.4826 * MAD \u002F median`. Exit 0 feasible \u002F 2 too long, with the minimum\ndetectable effect in their window \u002F 3 refused. It will frequently say the test needs more\nposts than a quarter allows — **that is the honest answer**, and more useful than a confident\nconclusion from retrospective data.\n\n## Rules\n\n- **Under 10 posts, describe; do not conclude.** Say so plainly.\n- **A pattern in past posts is a hypothesis.** Retrospective data is confounded — you made\n  carousels when you had structured material, on topics you knew best, in weeks you had time.\n  No statistics on the same data removes that.\n- **Never benchmark against someone else's numbers.** Different denominator, different\n  audience, usually a vendor's sample.\n- **Follower count is not a success metric.** Track inbound conversations, specific\n  references, invitations — the Tier 1 metrics you count by hand.\n- **Report the confidence level.** LinkedIn-official ?, third-party study ?, folklore ?.\n- **One good post is not evidence.** It is the most common cause of a strategy change and the\n  least informative event available.\n\n## Scripts\n\n| Script | Role |\n|---|---|\n| [`scripts\u002Fpost_performance_analyzer.py`](scripts\u002Fpost_performance_analyzer.py) | Median\u002FMAD, percentile bands, IQR outlier fence, per-post BREAKOUT→DUD classification; refuses conclusions below 10 posts. |\n| [`scripts\u002Fpattern_miner.py`](scripts\u002Fpattern_miner.py) | Four-gate permutation test with multiple-comparisons accounting; reports why every rejected candidate failed. |\n| [`scripts\u002Fexperiment_planner.py`](scripts\u002Fexperiment_planner.py) | Sizes a two-arm posting experiment, names the confounds to hold constant, and writes the falsification condition before the first post. |\n\n## References and assets\n\n- [`references\u002Flinkedin_metrics_canon.md`](references\u002Flinkedin_metrics_canon.md) — what each number is, what it is not, and which three tiers to track (7 sources)\n- [`references\u002Fevidence_thresholds.md`](references\u002Fevidence_thresholds.md) — the four gates, forking paths, and the uncomfortable arithmetic of LinkedIn A\u002FB tests (7 sources)\n\n- [`assets\u002Fexample_post_export.csv`](assets\u002Fexample_post_export.csv) — a 12-post export in the expected shape\n- [`assets\u002Fmeasurement_log_template.md`](assets\u002Fmeasurement_log_template.md) — the Tier 1 outcome log you keep by hand\n\n## Distinct from\n\n- **`marketing-skill\u002Fsocial-media-analyzer`** — cross-platform brand campaign reporting. This\n  is one person's own LinkedIn export, with refusals attached.\n- **`linkedin-strategy`** — decides what to do next. This says what happened.\n- **`product-team\u002Fexperiment-designer`** — product A\u002FB tests with real traffic; here n is\n  posts, and usually too small.\n\n---\n**Version:** 1.0.0\n","# LinkedIn Analytics — describe honestly, then refuse to over-conclude\n\nThe characteristic sentence of LinkedIn analytics is \"carousels do 3x better for me\", built\non four posts. With engagement as heavy-tailed as it is, four posts will show a 3x difference\nbetween almost any two groups you care to define. These three scripts stop that sentence\nbecoming a strategy.\n\n**Your own data only.** Nothing is fetched; scraping post or profile data is prohibited by\nUser Agreement §8.2 and none of this analysis needs it.\n\n## Workflow\n\n**1. Get the export.** LinkedIn Analytics → Post impressions → Export, or Settings → Data\nprivacy → Get a copy of your data. CSV and JSON both work.\n\n**2. Describe it.** Exit 0 analysed \u002F 2 below the 10-post floor, descriptive only \u002F 3\nunusable. Reports median and MAD rather than mean and standard deviation — one breakout post\nmakes a mean describe a distribution none of your posts belong to — plus Tukey percentile\nbands and a 1.5×IQR breakout threshold, so \"this did well\" has a number behind it.\n\n```bash\npython3 scripts\u002Fpost_performance_analyzer.py --input posts.csv --csv --output human\n```\n\n**3. Test the pattern they think they see.**\n\n```bash\npython3 scripts\u002Fpattern_miner.py --input posts.json --output human\n```\n\nExit 0 something survived \u002F 2 nothing survived \u002F 3 under 10 posts. Four gates: 5 posts in and\n5 out; a 15% relative difference in medians; beating 90% of 2,000 seeded label shuffles; and\na multiple-comparisons accounting of how many candidates would pass on noise alone.\n\n**\"Nothing survived\" is the most common honest answer and it is a real finding.** Report it\nas one. Do not soften it into a hedge that reads like a conclusion.\n\n**4. Turn a survivor into a test.**\n\n```bash\npython3 scripts\u002Fexperiment_planner.py --hypothesis \"...\" --variable \"...\" \\\n  --cv 0.45 --effect 0.30 --posts-per-week 2 --max-weeks 12 --output human\n```\n\nCV comes from step 2: `1.4826 * MAD \u002F median`. Exit 0 feasible \u002F 2 too long, with the minimum\ndetectable effect in their window \u002F 3 refused. It will frequently say the test needs more\nposts than a quarter allows — **that is the honest answer**, and more useful than a confident\nconclusion from retrospective data.\n\n## Rules\n\n- **Under 10 posts, describe; do not conclude.** Say so plainly.\n- **A pattern in past posts is a hypothesis.** Retrospective data is confounded — you made\n  carousels when you had structured material, on topics you knew best, in weeks you had time.\n  No statistics on the same data removes that.\n- **Never benchmark against someone else's numbers.** Different denominator, different\n  audience, usually a vendor's sample.\n- **Follower count is not a success metric.** Track inbound conversations, specific\n  references, invitations — the Tier 1 metrics you count by hand.\n- **Report the confidence level.** LinkedIn-official ?, third-party study ?, folklore ?.\n- **One good post is not evidence.** It is the most common cause of a strategy change and the\n  least informative event available.\n\n## Scripts\n\n| Script | Role |\n|---|---|\n| [`scripts\u002Fpost_performance_analyzer.py`](scripts\u002Fpost_performance_analyzer.py) | Median\u002FMAD, percentile bands, IQR outlier fence, per-post BREAKOUT→DUD classification; refuses conclusions below 10 posts. |\n| [`scripts\u002Fpattern_miner.py`](scripts\u002Fpattern_miner.py) | Four-gate permutation test with multiple-comparisons accounting; reports why every rejected candidate failed. |\n| [`scripts\u002Fexperiment_planner.py`](scripts\u002Fexperiment_planner.py) | Sizes a two-arm posting experiment, names the confounds to hold constant, and writes the falsification condition before the first post. |\n\n## References and assets\n\n- [`references\u002Flinkedin_metrics_canon.md`](references\u002Flinkedin_metrics_canon.md) — what each number is, what it is not, and which three tiers to track (7 sources)\n- [`references\u002Fevidence_thresholds.md`](references\u002Fevidence_thresholds.md) — the four gates, forking paths, and the uncomfortable arithmetic of LinkedIn A\u002FB tests (7 sources)\n\n- [`assets\u002Fexample_post_export.csv`](assets\u002Fexample_post_export.csv) — a 12-post export in the expected shape\n- [`assets\u002Fmeasurement_log_template.md`](assets\u002Fmeasurement_log_template.md) — the Tier 1 outcome log you keep by hand\n\n## Distinct from\n\n- **`marketing-skill\u002Fsocial-media-analyzer`** — cross-platform brand campaign reporting. This\n  is one person's own LinkedIn export, with refusals attached.\n- **`linkedin-strategy`** — decides what to do next. This says what happened.\n- **`product-team\u002Fexperiment-designer`** — product A\u002FB tests with real traffic; here n is\n  posts, and usually too small.\n\n---\n**Version:** 1.0.0\n",{"name":5,"license":15,"metadata":54,"description":7},{"author":55,"updated":56,"version":57,"category":45},"Alireza Rezvani",1787616000000,"1.0.0","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Flinkedin-analytics","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6","https:\u002F\u002Fdisha6.6.thenitishkr.in\u002F","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh","www.thenitishkr","thenitishkr | AI • Cyber-security • Research\r\n","2026-11-08 03:33:25",{"source":8,"skillName":5,"commands":68,"prompt":78,"zipAvailable":79},[69,72,75],{"tool":70,"command":71},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill linkedin-analytics",{"tool":73,"command":74},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill linkedin-analytics",{"tool":76,"command":77},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill linkedin-analytics","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Flinkedin-analytics\u002FSKILL.md",true,[],[82,111,135,162,187,211],{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":91,"publishedAt":49},"01M6QRD07D4TGNDK6VJ4EHF8BV","youtube-full","Use when the user needs YouTube transcripts, video search, channel browsing, playlist extraction, or content monitoring. Trigger phrases: 'get the transcript for', 'search YouTube for', 'what are the latest videos on', 'list this playlist', 'monitor this channel', or any request involving a YouTube URL, video ID, or @handle. Do NOT use for downloading video or audio files, YouTube engagement data (likes, comments), or private\u002Fage-restricted videos.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fyoutube-full\u002FSKILL.md",[88],{"categoryNo":89,"categoryName":90,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000016","图像与多媒体",[92,93,94,95,96,97,48,98,99,100,101,102,103,104,105,106,107,108,109,110],"youtube full","youtube","full","transcript","search","monitoring","Video","Audio","list","files","do","get","age","trigger","URL","for","browsing","request","youtube transcripts",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":49},"01M6QRCJ3112VYPA97YG7KGMC2","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fx-twitter-growth\u002FSKILL.md",[],[118,119,120,121,96,122,123,124,125,126,44,127,128,129,130,131,132,133,134,107],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","engine","write","strategy","use","platform","grow","posting","competitive",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":144,"publishedAt":49},"01M6QRBN3EHQNJY42RERP7A149","write-a-skill","Create new agent skills with proper structure, progressive disclosure, and bundled resources. Use when user wants to create, write, build, or author a new skill.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrite-a-skill\u002FSKILL.md",[141],{"categoryNo":142,"categoryName":143,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[145,128,146,147,148,149,150,151,130,152,153,58,154,155,156,157,158,159,160,161],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":166,"categories":167,"keywords":168,"publishedAt":49},"01M6QRB6G8P33PNGAF9Y69EJ9T","wrap-up","Close out a launched Claude Managed Agent — recap every primitive the founder now owns, regenerate the single-file overview page, and suggest the next 1-2 upgrades. Use when the user says \"wrap up\", \"close this out\", \"what do I own now\", \"give me the summary\", \"recap the agent\", or when the orchestrator routes phase=wrap-up. primitives_inventory.py tables everything owned (agent, environment, session, memory, outcome, deployment); overview_page.py regenerates a self-contained .\u002Fmy-agent\u002Fagent-overview.html; upgrade_suggester.py ranks the next moves from recorded deferrals plus standing hardening steps. Companion to run-without-you; the last stop before phase=done.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fwrap-up\u002FSKILL.md",[],[169,170,171,172,173,174,175,176,177,130,178,102,179,180,181,182,183,184,185,186],"wrap up","up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":188,"name":189,"nameZh":6,"description":190,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":191,"categories":192,"keywords":194,"publishedAt":49},"01M6QRA5AYC1RBFZ39GTWGS3N8","workflow-builder","Design and write deterministic multi-agent workflow scripts (.js files in .claude\u002Fworkflows\u002F) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fworkflow-builder\u002FSKILL.md",[193],{"categoryNo":142,"categoryName":143,"taxonomyType":21,"taxonomyTypeDesc":22},[195,196,197,198,199,147,174,200,201,202,203,204,128,205,101,206,207,208,209,210],"workflow","workflow builder","builder","design","workflows","code","orchestrate","pipeline","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":212,"name":213,"nameZh":6,"description":214,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":215,"categories":216,"keywords":217,"publishedAt":49},"01M6QR7QMCZESKQA0ERW1E657M","weekly-review","Use when someone wants to run a weekly review, close open loops, audit stalled projects and commitments, get their system back to trusted, restart a lapsed review habit, or says \"\u002Fcs:weekly-review\". Walks David Allen's three-phase loop — GET CLEAR, GET CURRENT, GET CREATIVE — with deterministic scripts that inventory open loops, gate the checklist with named gaps, and score commitment health 0-100.","https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fweekly-review\u002FSKILL.md",[],[218,219,220,221,222,223,224,225,226,203,227,228,229,230,231,232,103,233,234,186],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]