[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KJ3SVDA9FHTPC2TPHT6DZ4":3,"skill-same:01M6KJ3SVDA9FHTPC2TPHT6DZ4":55},{"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":28,"linkOnly":29,"markdownContent":30,"markdownBody":31,"frontMatter":32,"refName":33,"commitSha":34,"skillDirectory":35,"forksCount":36,"repositoryHtmlUrl":37,"repositoryHomepageUrl":38,"ownerHtmlUrl":39,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":40,"install":41,"resources":54},"01M6KJ3SVDA9FHTPC2TPHT6DZ4","run_glm_simulation",null,"Executes the GLM simulation and retrieves lake-specific depth data to ensure accurate mapping between model output and field observations.","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\u002Fb3-teacher-feedback-gemini-3.1-flash-lite-preview\u002Ftemperature-simulation\u002Frun3_run_glm_simulation\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27],"run","simulation","glm","glm simulation","run glm simulation","data","model","mapping","output","observations","2026-11-10 10:12:30",false,"---\nname: run_glm_simulation\ndescription: Executes the GLM simulation and retrieves lake-specific depth data to ensure accurate mapping between model output and field observations.\n---\n\nBefore processing, extract the `lake_depth` from the `&init_profiles` section of the namelist to correctly derive the depth of water layers (`depth = lake_depth - z`). Ensure the output at `\u002Froot\u002Foutput\u002Foutput.nc` is generated successfully.\n\n```python\nimport subprocess\nimport f90nml\nimport xarray as xr\n\ndef run_simulation():\n    # Extract lake depth from nml for correct depth calculation\n    nml = f90nml.read('\u002Froot\u002Fglm3.nml')\n    lake_depth = nml['init_profiles']['lake_depth']\n    \n    # Run the model\n    subprocess.run(['glm', 'x86_64', '--nml', '\u002Froot\u002Fglm3.nml'], check=True)\n    return lake_depth\n```","Before processing, extract the `lake_depth` from the `&init_profiles` section of the namelist to correctly derive the depth of water layers (`depth = lake_depth - z`). Ensure the output at `\u002Froot\u002Foutput\u002Foutput.nc` is generated successfully.\n\n```python\nimport subprocess\nimport f90nml\nimport xarray as xr\n\ndef run_simulation():\n    # Extract lake depth from nml for correct depth calculation\n    nml = f90nml.read('\u002Froot\u002Fglm3.nml')\n    lake_depth = nml['init_profiles']['lake_depth']\n    \n    # Run the model\n    subprocess.run(['glm', 'x86_64', '--nml', '\u002Froot\u002Fglm3.nml'], check=True)\n    return lake_depth\n```",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb3-teacher-feedback-gemini-3.1-flash-lite-preview\u002Ftemperature-simulation\u002Frun3_run_glm_simulation",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":42,"prompt":52,"zipAvailable":53},[43,46,49],{"tool":44,"command":45},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run_glm_simulation",{"tool":47,"command":48},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run_glm_simulation",{"tool":50,"command":51},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run_glm_simulation","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb3-teacher-feedback-gemini-3.1-flash-lite-preview\u002Ftemperature-simulation\u002Frun3_run_glm_simulation\u002FSKILL.md",true,[],[56,74,99,117,135,147],{"skillNo":57,"name":58,"nameZh":6,"description":59,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":60,"categories":61,"keywords":67,"publishedAt":28},"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",[62],{"categoryNo":63,"categoryName":64,"taxonomyType":65,"taxonomyTypeDesc":66},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[68,69,70,71,72,73],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":83,"publishedAt":28},"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",[80],{"categoryNo":81,"categoryName":82,"taxonomyType":65,"taxonomyTypeDesc":66},"01JBSEEDCAT000000000000023","Skill与智能体开发",[84,85,86,87,88,89,90,91,92,93,94,95,96,97,98],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":100,"name":101,"nameZh":6,"description":102,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":103,"categories":104,"keywords":105,"publishedAt":28},"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",[],[106,107,108,109,110,111,112,87,113,114,88,115,93,116,85],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":123,"publishedAt":28},"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",[],[124,26,20,93,125,126,127,128,129,130,131,132,115,133,134],"glm output","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":136,"name":137,"nameZh":6,"description":138,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":139,"categories":140,"keywords":142,"publishedAt":28},"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",[141],{"categoryNo":81,"categoryName":82,"taxonomyType":65,"taxonomyTypeDesc":66},[143,134,20,26,129,128,125,144,145,93,146],"glm calibration","setup","companion","metric",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":151,"categories":152,"keywords":153,"publishedAt":28},"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",[],[154,20,128,155,143,134,93,26,156,157,158,159,160,161,125],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]