[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KDWQKC74KXR919YWHTNTQC":3,"skill-same:01M6KDWQKC74KXR919YWHTNTQC":62},{"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":17,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":45,"ownerHtmlUrl":46,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":47,"install":48,"resources":61},"01M6KDWQKC74KXR919YWHTNTQC","trl-setup",null,"Setup and installation of TRL (Transformer Reinforcement Learning) library with compatible torch\u002Ftransformers versions. Use when setting up preference optimization training environments.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",93,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-claude-sonnet-4-6\u002Fnlp-paper-reproduction\u002Ftrl-setup\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],"trl setup","TRL","setup","up","environments","with","Preference Optimization","Transformer","reinforcement","installation","use","learning","Transformers","Optimization","library","reinforcement learning","training","2026-11-10 10:12:30",false,"---\nname: trl-setup\ndescription: Setup and installation of TRL (Transformer Reinforcement Learning) library with compatible torch\u002Ftransformers versions. Use when setting up preference optimization training environments.\n---\n\n# TRL Setup\n\n## Installation\n```bash\npip install torch transformers trl accelerate peft datasets --break-system-packages\n```\n\n## Compatibility Notes\n- TRL imports from `trl.trainer.utils`: `DPODataCollatorWithPadding`, `pad_to_length`, etc.\n- Some imports may change across TRL versions (e.g., `trl_sanitze_kwargs_for_tagging` vs `trl_sanitize_kwargs_for_tagging`)\n- If running in externally-managed Python (Debian), use `--break-system-packages` flag\n\n## Common Import Errors\n- `trl_sanitze_kwargs_for_tagging` not found → check trl version, may be renamed or removed\n- Missing `CPOTrainer` → upgrade trl: `pip install --upgrade trl`\n\n## Running Tests from Project Root\n```bash\ncd \u002Froot\u002FSimPO && python -m pytest unit_test\u002Funit_test_1.py -v\n# or\ncd \u002Froot\u002FSimPO && python unit_test\u002Funit_test_1.py\n```\n\n## Key TRL Trainer Utilities\n- `DPODataCollatorWithPadding`: handles padding for preference pairs\n- `pad_to_length`: pads tensor to specified length\n- `disable_dropout_in_model`: disables dropout during eval\n","# TRL Setup\n\n## Installation\n```bash\npip install torch transformers trl accelerate peft datasets --break-system-packages\n```\n\n## Compatibility Notes\n- TRL imports from `trl.trainer.utils`: `DPODataCollatorWithPadding`, `pad_to_length`, etc.\n- Some imports may change across TRL versions (e.g., `trl_sanitze_kwargs_for_tagging` vs `trl_sanitize_kwargs_for_tagging`)\n- If running in externally-managed Python (Debian), use `--break-system-packages` flag\n\n## Common Import Errors\n- `trl_sanitze_kwargs_for_tagging` not found → check trl version, may be renamed or removed\n- Missing `CPOTrainer` → upgrade trl: `pip install --upgrade trl`\n\n## Running Tests from Project Root\n```bash\ncd \u002Froot\u002FSimPO && python -m pytest unit_test\u002Funit_test_1.py -v\n# or\ncd \u002Froot\u002FSimPO && python unit_test\u002Funit_test_1.py\n```\n\n## Key TRL Trainer Utilities\n- `DPODataCollatorWithPadding`: handles padding for preference pairs\n- `pad_to_length`: pads tensor to specified length\n- `disable_dropout_in_model`: disables dropout during eval\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-claude-sonnet-4-6\u002Fnlp-paper-reproduction\u002Ftrl-setup",8,"https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench","https:\u002F\u002Fcxcscmu.github.io\u002FSkillLearnBench","https:\u002F\u002Fgithub.com\u002Fcxcscmu","2026-10-02 22:55:36",{"source":8,"skillName":5,"commands":49,"prompt":59,"zipAvailable":60},[50,53,56],{"tool":51,"command":52},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill trl-setup",{"tool":54,"command":55},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill trl-setup",{"tool":57,"command":58},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill trl-setup","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-claude-sonnet-4-6\u002Fnlp-paper-reproduction\u002Ftrl-setup\u002FSKILL.md",true,[],[63,81,106,123,143,154],{"skillNo":64,"name":65,"nameZh":6,"description":66,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":67,"categories":68,"keywords":74,"publishedAt":35},"01M6KM5KH175P5ABP5JMX2SBDQ","object_counter","Count occurrences of an object in the image using computer vision algorithm.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Fvideo-object-counting\u002Fobject_counter\u002FSKILL.md",[69],{"categoryNo":70,"categoryName":71,"taxonomyType":72,"taxonomyTypeDesc":73},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[75,76,77,78,79,80],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":82,"name":83,"nameZh":6,"description":84,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":85,"categories":86,"keywords":90,"publishedAt":35},"01M6KM51TMVXG79MMPCS6Y8G86","travel-planning-workflow","Invoke this skill when performing travel planning. It defines a structured workflow for generating itineraries. Following this workflow is essential to ensure the final plan satisfies user requirements.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Ftravel-planning-workflow\u002FSKILL.md",[87],{"categoryNo":88,"categoryName":89,"taxonomyType":72,"taxonomyTypeDesc":73},"01JBSEEDCAT000000000000023","Skill与智能体开发",[91,92,93,94,95,96,97,98,99,100,101,102,103,104,105],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":107,"name":108,"nameZh":6,"description":109,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":110,"categories":111,"keywords":112,"publishedAt":35},"01M6KM4DY820XHS9YTSK5QP5HD","bundled-dataset-search-tool","invoke this skill when you need to perform database search for travel planning. This skill provides some useful pre-packaged tools to look up accommodations, attractions, cities, driving distance, flights, and restaurants from the bundled dataset.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftravel-planning\u002Fbundled-dataset-search-tool\u002FSKILL.md",[],[113,114,115,116,117,118,21,94,119,120,95,121,100,122,92],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","look","flights","you","database",{"skillNo":124,"name":125,"nameZh":6,"description":126,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":127,"categories":128,"keywords":129,"publishedAt":35},"01M6KM465BB9PQ0B548HQSVAWZ","glm-output","Output-processing guidance for GLM tasks. Especially useful after glm-basics and glm-calibration when you need verifier-matching metrics from output.nc and a final \u002Froot\u002Fmetrics.json.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-output\u002FSKILL.md",[],[130,131,132,100,133,134,135,136,137,138,139,140,121,141,142],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":144,"name":145,"nameZh":6,"description":146,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":147,"categories":148,"keywords":150,"publishedAt":35},"01M6KM40KY3QWDW5W97S39R2BC","glm-calibration","Calibration guidance for GLM tasks. Often most effective after glm-basics has clarified the setup; glm-output is the companion skill for exact final metric computation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-calibration\u002FSKILL.md",[149],{"categoryNo":88,"categoryName":89,"taxonomyType":72,"taxonomyTypeDesc":73},[151,142,132,131,137,136,133,20,152,100,153],"glm calibration","companion","metric",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":160,"publishedAt":35},"01M6KM3VHHVSGMGHJQB3PPT6YG","glm-basics","A good starting point for GLM calibration tasks. Use to inspect glm3.nml, confirm how GLM runs, and identify the relevant files before moving on to calibration and output evaluation.","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fhuman_authored\u002Ftemperature-simulation\u002Fglm-basics\u002FSKILL.md",[],[161,132,136,162,151,142,100,131,163,164,165,28,166,167,133],"glm basics","inspect","relevant","files","good","how","Evaluation"]