[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGGRDBVJWHCT4896411JS5":3,"skill-same:01M6KGGRDBVJWHCT4896411JS5":56},{"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":29,"linkOnly":30,"markdownContent":31,"markdownBody":32,"frontMatter":33,"refName":34,"commitSha":35,"skillDirectory":36,"forksCount":37,"repositoryHtmlUrl":38,"repositoryHomepageUrl":39,"ownerHtmlUrl":40,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":41,"install":42,"resources":55},"01M6KGGRDBVJWHCT4896411JS5","run2_netcdf_rmse_evaluation",null,"Instructions for safely parsing GLM NetCDF output with netCDF4 and correctly executing an exact datetime + rounded-depth merge.","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\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Ftemperature-simulation\u002Frun2_netcdf_rmse_evaluation\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28],"Evaluation","netcdf","run2 netcdf rmse evaluation","merge","parsing","instructions","glm","output","with","datetime","for","2026-11-10 10:12:30",false,"---\nname: run2_netcdf_rmse_evaluation\ndescription: Instructions for safely parsing GLM NetCDF output with netCDF4 and correctly executing an exact datetime + rounded-depth merge.\n---\n\n# GLM Evaluation (Improved)\n\nThis skill describes how to correctly open and extract depth and temperature from GLM `output.nc` without coordinate conflict errors, and how to compute exact match RMSE against field data.\n\n## Loading and Interpolating\nUsing `xarray` to open `output.nc` will likely result in a `MissingDimensionsError` because GLM defines a `z` variable with a dimension that shares its name. \nInstead, rely on the `netCDF4` library natively.\n\n```python\nimport netCDF4 as nc\nimport pandas as pd\nimport numpy as np\n\n# Load simulation\nds = nc.Dataset('output.nc')\ntimes = nc.num2date(ds.variables['time'][:], ds.variables['time'].units)\ndatetimes = pd.to_datetime([t.strftime('%Y-%m-%d %H:%M:%S') for t in times])\n\nz = ds.variables['z'][:, :, 0, 0]\ntemp = ds.variables['temp'][:, :, 0, 0]\nns = ds.variables['NS'][:] # Number of simulated layers at each time step\n```\n\n## Depth Extraction\nGLM saves `z` as distance from the lake bottom. The distance from the surface down (which field data typically uses) is dynamically derived using `surface_z - z`:\n```python\ndepths = np.array([z[i, ns[i]-1] - z[i, :ns[i]] if ns[i] > 0 else [] for i in range(len(ns))])\n```\n\n## Creating Paired Dataset\nTo perform the *exact `datetime` and rounded-depth merge*:\n1. Construct a flat `sim_df` of valid datetime, rounded depth, and simulated temperature pairs.\n2. Format `obs` datetime to match, and generate its `depth_rounded`.\n3. Perform an explicit merge `pd.merge(obs, sim_df, on=['datetime', 'depth_rounded'])`.\nAvoid \"alternative depth binning\" (like `groupby().mean()`) unless explicitly instructed; GLM layers matching the same rounded integer will create multiple paired rows.\n","# GLM Evaluation (Improved)\n\nThis skill describes how to correctly open and extract depth and temperature from GLM `output.nc` without coordinate conflict errors, and how to compute exact match RMSE against field data.\n\n## Loading and Interpolating\nUsing `xarray` to open `output.nc` will likely result in a `MissingDimensionsError` because GLM defines a `z` variable with a dimension that shares its name. \nInstead, rely on the `netCDF4` library natively.\n\n```python\nimport netCDF4 as nc\nimport pandas as pd\nimport numpy as np\n\n# Load simulation\nds = nc.Dataset('output.nc')\ntimes = nc.num2date(ds.variables['time'][:], ds.variables['time'].units)\ndatetimes = pd.to_datetime([t.strftime('%Y-%m-%d %H:%M:%S') for t in times])\n\nz = ds.variables['z'][:, :, 0, 0]\ntemp = ds.variables['temp'][:, :, 0, 0]\nns = ds.variables['NS'][:] # Number of simulated layers at each time step\n```\n\n## Depth Extraction\nGLM saves `z` as distance from the lake bottom. The distance from the surface down (which field data typically uses) is dynamically derived using `surface_z - z`:\n```python\ndepths = np.array([z[i, ns[i]-1] - z[i, :ns[i]] if ns[i] > 0 else [] for i in range(len(ns))])\n```\n\n## Creating Paired Dataset\nTo perform the *exact `datetime` and rounded-depth merge*:\n1. Construct a flat `sim_df` of valid datetime, rounded depth, and simulated temperature pairs.\n2. Format `obs` datetime to match, and generate its `depth_rounded`.\n3. Perform an explicit merge `pd.merge(obs, sim_df, on=['datetime', 'depth_rounded'])`.\nAvoid \"alternative depth binning\" (like `groupby().mean()`) unless explicitly instructed; GLM layers matching the same rounded integer will create multiple paired rows.\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Ftemperature-simulation\u002Frun2_netcdf_rmse_evaluation",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":43,"prompt":53,"zipAvailable":54},[44,47,50],{"tool":45,"command":46},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_netcdf_rmse_evaluation",{"tool":48,"command":49},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_netcdf_rmse_evaluation",{"tool":51,"command":52},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_netcdf_rmse_evaluation","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_netcdf_rmse_evaluation\u002FSKILL.md",true,[],[57,75,99,117,135,147],{"skillNo":58,"name":59,"nameZh":6,"description":60,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":61,"categories":62,"keywords":68,"publishedAt":29},"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",[63],{"categoryNo":64,"categoryName":65,"taxonomyType":66,"taxonomyTypeDesc":67},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[69,70,71,72,73,74],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":84,"publishedAt":29},"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",[81],{"categoryNo":82,"categoryName":83,"taxonomyType":66,"taxonomyTypeDesc":67},"01JBSEEDCAT000000000000023","Skill与智能体开发",[85,86,87,88,89,90,91,92,93,28,94,95,96,97,98],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","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":29},"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",[],[106,107,108,109,110,111,112,88,113,114,89,115,28,116,86],"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":29},"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",[],[124,25,24,28,125,126,127,128,129,130,131,132,115,133,134],"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":29},"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":82,"categoryName":83,"taxonomyType":66,"taxonomyTypeDesc":67},[143,134,24,25,129,128,125,144,145,28,146],"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":29},"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",[],[154,24,128,155,143,134,28,25,156,157,158,159,160,18,125],"glm basics","inspect","relevant","files","good","use","how"]