[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGGMGBT04AH4DFATK01K1A":3,"skill-same:01M6KGGMGBT04AH4DFATK01K1A":53},{"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":26,"linkOnly":27,"markdownContent":28,"markdownBody":29,"frontMatter":30,"refName":31,"commitSha":32,"skillDirectory":33,"forksCount":34,"repositoryHtmlUrl":35,"repositoryHomepageUrl":36,"ownerHtmlUrl":37,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":38,"install":39,"resources":52},"01M6KGGMGBT04AH4DFATK01K1A","run2_glm_calibration",null,"Instructions for modifying GLM parameters using regex and systematically optimizing them within physical limits.","cxcscmu\u002FSkillLearnBench","cxcscmu","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F142838272?v=4","01M6KCQDFQWPDSN0MKZY96MSQP","01M6KCQDM7G70TKJ28NTSQC0RZ",92,"MIT","https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Ftemperature-simulation\u002Frun2_glm_calibration\u002FSKILL.md",[],[18,19,20,21,22,23,24,25],"glm","calibration","glm calibration","run2 glm calibration","instructions","physical","regex","for","2026-11-10 10:12:30",false,"---\nname: run2_glm_calibration\ndescription: Instructions for modifying GLM parameters using regex and systematically optimizing them within physical limits.\n---\n\n# GLM Calibration (Improved)\n\nThis skill covers programmatic configuration of GLM and automated parameter tuning.\n\n## Modifying Parameters\nGLM uses a `.nml` configuration file. A robust way to edit it without dedicated Fortran namelist parsers is using Python's `re` module.\nThis ensures parameters with scientific notation or decimals are reliably replaced.\n\n```python\nimport re\ndef update_nml(file_path, params):\n    with open(file_path, 'r') as f:\n        content = f.read()\n    for k, v in params.items():\n        # \\b ensures exact keyword match, avoiding partial matches like 'catchrain'\n        content = re.sub(rf\"(\\b{k}\\s*=\\s*)[0-9\\.eE+-]+\", rf\"\\g\u003C1>{v}\", content)\n    with open(file_path, 'w') as f:\n        f.write(content)\n```\n\n## Running the Model\nCall the `glm` executable in the directory containing `glm3.nml`:\n```bash\nsubprocess.run(['glm'], cwd='\u002Fpath\u002Fto\u002Fproject', stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n```\n\n## Automated Tuning\nTo quickly find parameters satisfying specific RMSE constraints without manual trial-and-error, you can employ `scipy.optimize.minimize` (like Nelder-Mead) over a custom objective function. \nProvide boundary lists to keep values within physically reasonable calibration ranges.\n","# GLM Calibration (Improved)\n\nThis skill covers programmatic configuration of GLM and automated parameter tuning.\n\n## Modifying Parameters\nGLM uses a `.nml` configuration file. A robust way to edit it without dedicated Fortran namelist parsers is using Python's `re` module.\nThis ensures parameters with scientific notation or decimals are reliably replaced.\n\n```python\nimport re\ndef update_nml(file_path, params):\n    with open(file_path, 'r') as f:\n        content = f.read()\n    for k, v in params.items():\n        # \\b ensures exact keyword match, avoiding partial matches like 'catchrain'\n        content = re.sub(rf\"(\\b{k}\\s*=\\s*)[0-9\\.eE+-]+\", rf\"\\g\u003C1>{v}\", content)\n    with open(file_path, 'w') as f:\n        f.write(content)\n```\n\n## Running the Model\nCall the `glm` executable in the directory containing `glm3.nml`:\n```bash\nsubprocess.run(['glm'], cwd='\u002Fpath\u002Fto\u002Fproject', stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)\n```\n\n## Automated Tuning\nTo quickly find parameters satisfying specific RMSE constraints without manual trial-and-error, you can employ `scipy.optimize.minimize` (like Nelder-Mead) over a custom objective function. \nProvide boundary lists to keep values within physically reasonable calibration ranges.\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Ftemperature-simulation\u002Frun2_glm_calibration",7,"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":40,"prompt":50,"zipAvailable":51},[41,44,47],{"tool":42,"command":43},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_glm_calibration",{"tool":45,"command":46},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_glm_calibration",{"tool":48,"command":49},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_glm_calibration","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Ftemperature-simulation\u002Frun2_glm_calibration\u002FSKILL.md",true,[],[54,72,96,114,132,143],{"skillNo":55,"name":56,"nameZh":6,"description":57,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":58,"categories":59,"keywords":65,"publishedAt":26},"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",[60],{"categoryNo":61,"categoryName":62,"taxonomyType":63,"taxonomyTypeDesc":64},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[66,67,68,69,70,71],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":73,"name":74,"nameZh":6,"description":75,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":76,"categories":77,"keywords":81,"publishedAt":26},"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",[78],{"categoryNo":79,"categoryName":80,"taxonomyType":63,"taxonomyTypeDesc":64},"01JBSEEDCAT000000000000023","Skill与智能体开发",[82,83,84,85,86,87,88,89,90,25,91,92,93,94,95],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":26},"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",[],[103,104,105,106,107,108,109,85,110,111,86,112,25,113,83],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":120,"publishedAt":26},"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",[],[121,122,18,25,123,124,125,126,127,128,129,130,112,131,19],"glm output","output","tasks","metrics","json","basics","guidance","matching","verifier","processing","root",{"skillNo":133,"name":134,"nameZh":6,"description":135,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":136,"categories":137,"keywords":139,"publishedAt":26},"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",[138],{"categoryNo":79,"categoryName":80,"taxonomyType":63,"taxonomyTypeDesc":64},[20,19,18,122,127,126,123,140,141,25,142],"setup","companion","metric",{"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":149,"publishedAt":26},"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",[],[150,18,126,151,20,19,25,122,152,153,154,155,156,157,123],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]