[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6QBZMPS99S2STJXCMJSBQVG":3,"skill-same:01M6QBZMPS99S2STJXCMJSBQVG":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":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":55,"commitSha":56,"skillDirectory":57,"forksCount":14,"repositoryHtmlUrl":58,"repositoryHomepageUrl":59,"ownerHtmlUrl":60,"ownerBlogUrl":61,"ownerDescription":62,"updatedAt":63,"install":64,"resources":77},"01M6QBZMPS99S2STJXCMJSBQVG","data-throughput-accelerator",null,"Diagnose and accelerate large data movement — ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, and table synchronization — by isolating the true bottleneck, benchmarking variants, and codifying the fastest path with a hard accounting block proving rows and timestamps cohere. Use when a pipeline or backfill is too slow and must get faster without losing data correctness.","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\u002Fdata-throughput-accelerator\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000040","财务",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"data","data throughput accelerator","throughput","ingestion","variants","catch","timestamps","loading","Accelerate","get","up","benchmarking","bottleneck","edit","write","read","synchronization","Bash","pipeline","etl","2026-11-10 10:12:30",false,"---\nname: data-throughput-accelerator\ndescription: Diagnose and accelerate large data movement — ingestion, backfill, export, ETL, warehouse loading, manifest catch-up, and table synchronization — by isolating the true bottleneck, benchmarking variants, and codifying the fastest path with a hard accounting block proving rows and timestamps cohere. Use when a pipeline or backfill is too slow and must get faster without losing data correctness.\nlicense: MIT\nmetadata:\n  origin: ECC\ntools: Read, Write, Edit, Bash, Grep, Glob\n---\n\n# Data Throughput Accelerator\n\nUse this skill when the bottleneck is moving, transforming, or saving lots of\ndata. The goal is not just speed. The goal is faster correct data landing in the\nright place with proof.\n\n## First Distinction\n\nSeparate these before optimizing:\n\n- source extraction speed;\n- network transfer speed;\n- warehouse\u002Fload speed;\n- transform speed;\n- serving-table freshness;\n- live tail growth while the job runs.\n\nA pipeline can be \"fast\" and still appear behind if new data arrives faster\nthan the final catch-up window.\n\n## Fast Path Heuristics\n\n- Move compute to where the data already is.\n- Prefer warehouse-native scans, joins, and appends for large landed files.\n- Use manifests or checkpoints so completed files\u002Fpartitions are skipped.\n- Use partitioning and clustering that match the read and append pattern.\n- Batch small files, requests, and writes.\n- Make writes idempotent through unique keys, manifests, or replaceable staging.\n- Keep raw, derived, and serving tables separately accountable.\n\n## Workflow\n\n1. Read the current source, target, and manifest contracts.\n2. Measure backlog: external files, manifest rows, raw rows, derived rows,\n   min\u002Fmax timestamps, and unprocessed counts.\n3. Run a safe catch-up or sample benchmark.\n4. Compare variants: batch size, worker count, warehouse SQL, file grouping,\n   staging shape, and manifest update method.\n5. Promote only the fastest path that keeps counts and timestamps coherent.\n6. Codify the path as a CLI, scheduled job, workflow, or runbook.\n7. Rerun final accounting after the codified path executes.\n\n## Accounting Output\n\nUse a hard accounting block:\n\n```text\nData throughput result:\n- Source files discovered: 294\n- Files processed this run: 294\n- Raw rows added: 9,683,598\n- Derived rows added: 8,917,585\n- Remaining tail: 24 files at readback time\n- Runtime: 38.7s\n- Correctness gate: manifest counts and table max timestamps match\n```\n\n## Guardrails\n\n- Do not delete raw data to make a metric look better.\n- Do not skip failed files silently.\n- Do not mix historical backfill status with live-tail freshness.\n- Do not call a pipeline complete until the target tables and manifest agree.\n- For finance, healthcare, regulated, or customer-impacting data, preserve\n  replay evidence and approval gates.\n","# Data Throughput Accelerator\n\nUse this skill when the bottleneck is moving, transforming, or saving lots of\ndata. The goal is not just speed. The goal is faster correct data landing in the\nright place with proof.\n\n## First Distinction\n\nSeparate these before optimizing:\n\n- source extraction speed;\n- network transfer speed;\n- warehouse\u002Fload speed;\n- transform speed;\n- serving-table freshness;\n- live tail growth while the job runs.\n\nA pipeline can be \"fast\" and still appear behind if new data arrives faster\nthan the final catch-up window.\n\n## Fast Path Heuristics\n\n- Move compute to where the data already is.\n- Prefer warehouse-native scans, joins, and appends for large landed files.\n- Use manifests or checkpoints so completed files\u002Fpartitions are skipped.\n- Use partitioning and clustering that match the read and append pattern.\n- Batch small files, requests, and writes.\n- Make writes idempotent through unique keys, manifests, or replaceable staging.\n- Keep raw, derived, and serving tables separately accountable.\n\n## Workflow\n\n1. Read the current source, target, and manifest contracts.\n2. Measure backlog: external files, manifest rows, raw rows, derived rows,\n   min\u002Fmax timestamps, and unprocessed counts.\n3. Run a safe catch-up or sample benchmark.\n4. Compare variants: batch size, worker count, warehouse SQL, file grouping,\n   staging shape, and manifest update method.\n5. Promote only the fastest path that keeps counts and timestamps coherent.\n6. Codify the path as a CLI, scheduled job, workflow, or runbook.\n7. Rerun final accounting after the codified path executes.\n\n## Accounting Output\n\nUse a hard accounting block:\n\n```text\nData throughput result:\n- Source files discovered: 294\n- Files processed this run: 294\n- Raw rows added: 9,683,598\n- Derived rows added: 8,917,585\n- Remaining tail: 24 files at readback time\n- Runtime: 38.7s\n- Correctness gate: manifest counts and table max timestamps match\n```\n\n## Guardrails\n\n- Do not delete raw data to make a metric look better.\n- Do not skip failed files silently.\n- Do not mix historical backfill status with live-tail freshness.\n- Do not call a pipeline complete until the target tables and manifest agree.\n- For finance, healthcare, regulated, or customer-impacting data, preserve\n  replay evidence and approval gates.\n",{"name":5,"tools":52,"license":15,"metadata":53,"description":7},"Read, Write, Edit, Bash, Grep, Glob",{"origin":54},"ECC","main","0da4485785ee599388900be50db1635489d53056",".agents\u002Fskills\u002Fdata-throughput-accelerator","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":65,"prompt":75,"zipAvailable":76},[66,69,72],{"tool":67,"command":68},"npx","npx skills add Tashima-Tarsh\u002FDisha6.6 --skill data-throughput-accelerator",{"tool":70,"command":71},"bunx","bunx skills add Tashima-Tarsh\u002FDisha6.6 --skill data-throughput-accelerator",{"tool":73,"command":74},"pnpm","pnpm dlx skills add Tashima-Tarsh\u002FDisha6.6 --skill data-throughput-accelerator","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FTashima-Tarsh\u002FDisha6.6\u002Fblob\u002Fmain\u002F.agents\u002Fskills\u002Fdata-throughput-accelerator\u002FSKILL.md",true,[],[79,109,133,160,184,207],{"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":6,"sourceHtmlUrl":83,"categories":84,"keywords":90,"publishedAt":47},"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",[85],{"categoryNo":86,"categoryName":87,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[91,92,93,94,95,96,27,97,98,99,100,101,36,102,103,104,105,106,107,108],"youtube full","youtube","full","transcript","search","monitoring","Video","Audio","list","files","do","age","trigger","URL","for","browsing","request","youtube transcripts",{"skillNo":110,"name":111,"nameZh":6,"description":112,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":113,"categories":114,"keywords":115,"publishedAt":47},"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",[],[116,117,118,119,95,120,121,122,123,124,125,126,41,127,128,129,130,131,132,105],"twitter","x twitter growth","growth","research","content","engineering","plan","web search","competitors","analyze","engine","strategy","use","platform","grow","posting","competitive",{"skillNo":134,"name":135,"nameZh":6,"description":136,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":137,"categories":138,"keywords":142,"publishedAt":47},"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",[139],{"categoryNo":140,"categoryName":141,"taxonomyType":88,"taxonomyTypeDesc":89},"01JBSEEDCAT000000000000023","Skill与智能体开发",[143,41,144,145,146,147,148,149,128,150,151,55,152,153,154,155,156,157,158,159],"write a skill","github","create","https","productivity","matt pocock","direct","example","build","new skill","structure","new agent","resources","with","tree","progressive disclosure","author",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":47},"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",[],[167,37,168,169,170,171,172,173,174,128,175,101,176,177,178,179,180,181,182,183],"wrap up","wrap","gemini","deployment","run","file","hardening","closeout","stop","upgrade","founder","close","companion","you","tables","directions","phase",{"skillNo":185,"name":186,"nameZh":6,"description":187,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":188,"categories":189,"keywords":191,"publishedAt":47},"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",[190],{"categoryNo":140,"categoryName":141,"taxonomyType":88,"taxonomyTypeDesc":89},[192,193,194,195,196,145,171,197,198,45,199,200,41,201,100,202,203,204,205,206],"workflow","workflow builder","builder","design","workflows","code","orchestrate","loop","multi-agent","context","across","fresh","scripts","judge","claude code workflow",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":213,"publishedAt":47},"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",[],[214,215,216,217,218,219,220,221,222,199,223,224,225,226,227,228,36,229,230,183],"review","weekly review","weekly","deterministic scripts","reflect","feeds","inventory","open","team","projects","project-management","scanner","conversation","back","project","sprint","cadence"]