[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7BX4JA2VX4B1PCJS3EWCAMC":3,"skill-same:01M7BX4JA2VX4B1PCJS3EWCAMC":45},{"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":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":24,"commitSha":25,"skillDirectory":26,"forksCount":27,"repositoryHtmlUrl":28,"repositoryHomepageUrl":6,"ownerHtmlUrl":29,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":30,"install":31,"resources":44},"01M7BX4JA2VX4B1PCJS3EWCAMC","parallel-processing",null,"Parallel processing with joblib for grid search and batch computations. Use when speeding up computationally intensive tasks across multiple CPU cores.","linyouxue\u002Fskill-repair-benchmark","linyouxue","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F216151464?v=4","01M7BJEVCXKD74KH6FG9CBVSQB","林宥学","01M7BJEVE02NP217V93BKZFC0J",0,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fevaluation\u002Fresults\u002FOpus47-OriginalSkill-79Tasks-20260920\u002Fruns\u002Fopus47-original-skill\u002Fmars-clouds-clustering-opus47-original-skill-r001\u002Finputs\u002Fskills\u002Fparallel-processing\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: parallel-processing\ndescription: Parallel processing with joblib for grid search and batch computations. Use when speeding up computationally intensive tasks across multiple CPU cores.\n---\n\n# Parallel Processing with joblib\n\nSpeed up computationally intensive tasks by distributing work across multiple CPU cores.\n\n## Basic Usage\n\n```python\nfrom joblib import Parallel, delayed\n\ndef process_item(x):\n    \"\"\"Process a single item.\"\"\"\n    return x ** 2\n\n# Sequential\nresults = [process_item(x) for x in range(100)]\n\n# Parallel (uses all available cores)\nresults = Parallel(n_jobs=-1)(\n    delayed(process_item)(x) for x in range(100)\n)\n```\n\n## Key Parameters\n\n- **n_jobs**: `-1` for all cores, `1` for sequential, or specific number\n- **verbose**: `0` (silent), `10` (progress), `50` (detailed)\n- **backend**: `'loky'` (CPU-bound, default) or `'threading'` (I\u002FO-bound)\n\n## Grid Search Example\n\n```python\nfrom joblib import Parallel, delayed\nfrom itertools import product\n\ndef evaluate_params(param_a, param_b):\n    \"\"\"Evaluate one parameter combination.\"\"\"\n    score = expensive_computation(param_a, param_b)\n    return {'param_a': param_a, 'param_b': param_b, 'score': score}\n\n# Define parameter grid\nparams = list(product([0.1, 0.5, 1.0], [10, 20, 30]))\n\n# Parallel grid search\nresults = Parallel(n_jobs=-1, verbose=10)(\n    delayed(evaluate_params)(a, b) for a, b in params\n)\n\n# Filter results\nresults = [r for r in results if r is not None]\nbest = max(results, key=lambda x: x['score'])\n```\n\n## Pre-computing Shared Data\n\nWhen all tasks need the same data, pre-compute it once:\n\n```python\n# Pre-compute once\nshared_data = load_data()\n\ndef process_with_shared(params, data):\n    return compute(params, data)\n\n# Pass shared data to each task\nresults = Parallel(n_jobs=-1)(\n    delayed(process_with_shared)(p, shared_data)\n    for p in param_list\n)\n```\n\n## Performance Tips\n\n- Only worth it for tasks taking >0.1s per item (overhead cost)\n- Watch memory usage - each worker gets a copy of data\n- Use `verbose=10` to monitor progress\n","# Parallel Processing with joblib\n\nSpeed up computationally intensive tasks by distributing work across multiple CPU cores.\n\n## Basic Usage\n\n```python\nfrom joblib import Parallel, delayed\n\ndef process_item(x):\n    \"\"\"Process a single item.\"\"\"\n    return x ** 2\n\n# Sequential\nresults = [process_item(x) for x in range(100)]\n\n# Parallel (uses all available cores)\nresults = Parallel(n_jobs=-1)(\n    delayed(process_item)(x) for x in range(100)\n)\n```\n\n## Key Parameters\n\n- **n_jobs**: `-1` for all cores, `1` for sequential, or specific number\n- **verbose**: `0` (silent), `10` (progress), `50` (detailed)\n- **backend**: `'loky'` (CPU-bound, default) or `'threading'` (I\u002FO-bound)\n\n## Grid Search Example\n\n```python\nfrom joblib import Parallel, delayed\nfrom itertools import product\n\ndef evaluate_params(param_a, param_b):\n    \"\"\"Evaluate one parameter combination.\"\"\"\n    score = expensive_computation(param_a, param_b)\n    return {'param_a': param_a, 'param_b': param_b, 'score': score}\n\n# Define parameter grid\nparams = list(product([0.1, 0.5, 1.0], [10, 20, 30]))\n\n# Parallel grid search\nresults = Parallel(n_jobs=-1, verbose=10)(\n    delayed(evaluate_params)(a, b) for a, b in params\n)\n\n# Filter results\nresults = [r for r in results if r is not None]\nbest = max(results, key=lambda x: x['score'])\n```\n\n## Pre-computing Shared Data\n\nWhen all tasks need the same data, pre-compute it once:\n\n```python\n# Pre-compute once\nshared_data = load_data()\n\ndef process_with_shared(params, data):\n    return compute(params, data)\n\n# Pass shared data to each task\nresults = Parallel(n_jobs=-1)(\n    delayed(process_with_shared)(p, shared_data)\n    for p in param_list\n)\n```\n\n## Performance Tips\n\n- Only worth it for tasks taking >0.1s per item (overhead cost)\n- Watch memory usage - each worker gets a copy of data\n- Use `verbose=10` to monitor progress\n",{"name":5,"description":7},"main","cdcb6d230f3b15c3c33f8a2bdd16439f43602cbe","evaluation\u002Fresults\u002FOpus47-OriginalSkill-79Tasks-20260920\u002Fruns\u002Fopus47-original-skill\u002Fmars-clouds-clustering-opus47-original-skill-r001\u002Finputs\u002Fskills\u002Fparallel-processing",1,"https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark","https:\u002F\u002Fgithub.com\u002Flinyouxue","2026-11-14 20:28:35",{"source":8,"skillName":5,"commands":32,"prompt":42,"zipAvailable":43},[33,36,39],{"tool":34,"command":35},"npx","npx skills add linyouxue\u002Fskill-repair-benchmark --skill parallel-processing",{"tool":37,"command":38},"bunx","bunx skills add linyouxue\u002Fskill-repair-benchmark --skill parallel-processing",{"tool":40,"command":41},"pnpm","pnpm dlx skills add linyouxue\u002Fskill-repair-benchmark --skill parallel-processing","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fevaluation\u002Fresults\u002FOpus47-OriginalSkill-79Tasks-20260920\u002Fruns\u002Fopus47-original-skill\u002Fmars-clouds-clustering-opus47-original-skill-r001\u002Finputs\u002Fskills\u002Fparallel-processing\u002FSKILL.md",true,[],[46,53,60,67,72,77],{"skillNo":47,"name":48,"nameZh":6,"description":49,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":50,"categories":51,"keywords":52,"publishedAt":19},"01M7E1HGVRY73NBJ022CJG13X6","video-frame-extraction","Extract frames from video files and save them as images using OpenCV","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_03\u002Finputs\u002Fskills\u002Fvideo-frame-extraction\u002FSKILL.md",[],[],{"skillNo":54,"name":55,"nameZh":6,"description":56,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":57,"categories":58,"keywords":59,"publishedAt":19},"01M7E1HAG5R7CJ9C8NYJS771MB","openai-vision","Analyze images and multi-frame sequences using OpenAI GPT vision models","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_03\u002Finputs\u002Fskills\u002Fopenai-vision\u002FSKILL.md",[],[],{"skillNo":61,"name":62,"nameZh":6,"description":63,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":64,"categories":65,"keywords":66,"publishedAt":19},"01M7E1H7DADKB6QBWKC2KQ63SZ","image-ocr","Extract text content from images using Tesseract OCR via Python, including structured field extraction from receipts and forms","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_03\u002Finputs\u002Fskills\u002Fimage-ocr\u002FSKILL.md",[],[],{"skillNo":68,"name":48,"nameZh":6,"description":49,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":19},"01M7E1H0WBNT43TENGQY7AVZHD","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_02\u002Finputs\u002Fskills\u002Fvideo-frame-extraction\u002FSKILL.md",[],[],{"skillNo":73,"name":55,"nameZh":6,"description":56,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M7E1GTC7D4N4Q2EF03875J9E","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_02\u002Finputs\u002Fskills\u002Fopenai-vision\u002FSKILL.md",[],[],{"skillNo":78,"name":62,"nameZh":6,"description":63,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":81,"publishedAt":19},"01M7E1GQ92CWFMGH53KSSE4WER","https:\u002F\u002Fgithub.com\u002Flinyouxue\u002Fskill-repair-benchmark\u002Fblob\u002Fmain\u002Fjpg-ocr-stat\u002Fresult\u002Fskilltta-claude4.7\u002Fgroup_03\u002Frun_02\u002Finputs\u002Fskills\u002Fimage-ocr\u002FSKILL.md",[],[]]