[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KFWHFHTB6ABKNQKTRRHAMV":3,"skill-same:01M6KFWHFHTB6ABKNQKTRRHAMV":63},{"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":36,"linkOnly":37,"markdownContent":38,"markdownBody":39,"frontMatter":40,"refName":41,"commitSha":42,"skillDirectory":43,"forksCount":44,"repositoryHtmlUrl":45,"repositoryHomepageUrl":46,"ownerHtmlUrl":47,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":48,"install":49,"resources":62},"01M6KFWHFHTB6ABKNQKTRRHAMV","run2_glm-calibration",null,"How to run and calibrate GLM3 for lake temperature simulation - includes correct parameter effects, grid search strategy, and verified working parameter ranges.","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-claude-sonnet-4-6\u002Ftemperature-simulation\u002Frun2_glm-calibration\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35],"run2 glm calibration","glm calibration","calibration","glm","search strategy","effects","for","verified","grid","temperature","run","how to","strategy","correct","how","simulation","calibrate","search","2026-11-10 10:12:30",false,"---\nname: run2_glm-calibration\ndescription: How to run and calibrate GLM3 for lake temperature simulation - includes correct parameter effects, grid search strategy, and verified working parameter ranges.\n---\n\n# GLM3 Calibration Skill (Improved)\n\n## Setup\n```bash\ncd \u002Froot   # Must run from directory containing glm3.nml\nglm        # Reads glm3.nml, writes to out_dir\u002Fout_fn.nc\n```\n\n## Key Calibration Parameters\n\n| Parameter | Section | Calibration Range | Primary Effect |\n|-----------|---------|-------------------|----------------|\n| `Kw` | `&light` | [0.1, 0.5] | Light attenuation; higher = more surface heating, deeper stratification |\n| `coef_mix_hyp` | `&mixing` | [0.3, 0.7] | Hypolimnion mixing; **lower = cooler deep temps** |\n| `wind_factor` | `&meteorology` | [0.7, 1.3] | Wind scaling; **lower = more stratification, cooler deep** |\n| `lw_factor` | `&meteorology` | [0.7, 1.3] | Longwave radiation; higher = more surface heat |\n| `ch` | `&meteorology` | [0.0005, 0.002] | Sensible heat coef; minor effect |\n\n## Do NOT Change\n- `sw_factor`, `cd`, `ce`\n- `the_depths`, `the_temps`, `the_sals`\n\n## Bias Diagnosis\n\nIf `sim_temp > obs_temp` (positive bias) in deep\u002Fsummer:\n- Reduce `coef_mix_hyp` (less hypolimnion mixing)\n- Reduce `wind_factor` (less surface mixing → stronger stratification)\n- Increase `Kw` (stronger light attenuation → sharper thermocline)\n\n## Verified Working Parameters (Lake Mendota 2009-2015)\n```\nKw = 0.3\ncoef_mix_hyp = 0.35\nwind_factor = 0.9\nlw_factor = 1.0\nch = 0.0013\n```\nResults: overall_rmse=1.37, annual_deep_rmse=1.35, summer_deep_rmse=1.47\n\n## NML Update Pattern\n```python\nimport re\n\ndef update_nml(filepath, params):\n    with open(filepath, 'r') as f:\n        content = f.read()\n    for param, value in params.items():\n        pattern = rf'(\\b{param}\\s*=\\s*)[^\\n]+'\n        replacement = rf'\\g\u003C1>{value}'\n        content = re.sub(pattern, replacement, content)\n    with open(filepath, 'w') as f:\n        f.write(content)\n```\n\n## Grid Search Strategy\n1. Focus on `coef_mix_hyp` and `wind_factor` first (biggest impact on deep temps)\n2. Try `coef_mix_hyp`: [0.30, 0.35, 0.40], `wind_factor`: [0.80, 0.90, 1.00]\n3. Then tune `Kw`: [0.25, 0.30, 0.35]\n4. Score = overall_rmse + annual_deep_rmse + summer_deep_rmse + penalties for violations\n\n## Key Insight\nReducing `wind_factor` from 1.0 to 0.9 had the largest single improvement for deep temperature accuracy on Lake Mendota. With Kw=0.3, mix_hyp=0.35, wind=0.9, all three RMSE targets were met comfortably.\n","# GLM3 Calibration Skill (Improved)\n\n## Setup\n```bash\ncd \u002Froot   # Must run from directory containing glm3.nml\nglm        # Reads glm3.nml, writes to out_dir\u002Fout_fn.nc\n```\n\n## Key Calibration Parameters\n\n| Parameter | Section | Calibration Range | Primary Effect |\n|-----------|---------|-------------------|----------------|\n| `Kw` | `&light` | [0.1, 0.5] | Light attenuation; higher = more surface heating, deeper stratification |\n| `coef_mix_hyp` | `&mixing` | [0.3, 0.7] | Hypolimnion mixing; **lower = cooler deep temps** |\n| `wind_factor` | `&meteorology` | [0.7, 1.3] | Wind scaling; **lower = more stratification, cooler deep** |\n| `lw_factor` | `&meteorology` | [0.7, 1.3] | Longwave radiation; higher = more surface heat |\n| `ch` | `&meteorology` | [0.0005, 0.002] | Sensible heat coef; minor effect |\n\n## Do NOT Change\n- `sw_factor`, `cd`, `ce`\n- `the_depths`, `the_temps`, `the_sals`\n\n## Bias Diagnosis\n\nIf `sim_temp > obs_temp` (positive bias) in deep\u002Fsummer:\n- Reduce `coef_mix_hyp` (less hypolimnion mixing)\n- Reduce `wind_factor` (less surface mixing → stronger stratification)\n- Increase `Kw` (stronger light attenuation → sharper thermocline)\n\n## Verified Working Parameters (Lake Mendota 2009-2015)\n```\nKw = 0.3\ncoef_mix_hyp = 0.35\nwind_factor = 0.9\nlw_factor = 1.0\nch = 0.0013\n```\nResults: overall_rmse=1.37, annual_deep_rmse=1.35, summer_deep_rmse=1.47\n\n## NML Update Pattern\n```python\nimport re\n\ndef update_nml(filepath, params):\n    with open(filepath, 'r') as f:\n        content = f.read()\n    for param, value in params.items():\n        pattern = rf'(\\b{param}\\s*=\\s*)[^\\n]+'\n        replacement = rf'\\g\u003C1>{value}'\n        content = re.sub(pattern, replacement, content)\n    with open(filepath, 'w') as f:\n        f.write(content)\n```\n\n## Grid Search Strategy\n1. Focus on `coef_mix_hyp` and `wind_factor` first (biggest impact on deep temps)\n2. Try `coef_mix_hyp`: [0.30, 0.35, 0.40], `wind_factor`: [0.80, 0.90, 1.00]\n3. Then tune `Kw`: [0.25, 0.30, 0.35]\n4. Score = overall_rmse + annual_deep_rmse + summer_deep_rmse + penalties for violations\n\n## Key Insight\nReducing `wind_factor` from 1.0 to 0.9 had the largest single improvement for deep temperature accuracy on Lake Mendota. With Kw=0.3, mix_hyp=0.35, wind=0.9, all three RMSE targets were met comfortably.\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-claude-sonnet-4-6\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":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_glm-calibration",{"tool":55,"command":56},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_glm-calibration",{"tool":58,"command":59},"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-claude-sonnet-4-6\u002Ftemperature-simulation\u002Frun2_glm-calibration\u002FSKILL.md",true,[],[64,82,106,123,141,152],{"skillNo":65,"name":66,"nameZh":6,"description":67,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":68,"categories":69,"keywords":75,"publishedAt":36},"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",[70],{"categoryNo":71,"categoryName":72,"taxonomyType":73,"taxonomyTypeDesc":74},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[76,77,78,79,80,81],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":83,"name":84,"nameZh":6,"description":85,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":91,"publishedAt":36},"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",[88],{"categoryNo":89,"categoryName":90,"taxonomyType":73,"taxonomyTypeDesc":74},"01JBSEEDCAT000000000000023","Skill与智能体开发",[92,93,94,95,96,97,98,99,100,24,101,102,103,104,105],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","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":36},"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",[],[35,113,114,115,116,117,118,95,119,120,96,121,24,122,93],"tool","dataset","bundled dataset search tool","restaurants","Tools","up","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":36},"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",[],[130,131,21,24,132,133,134,135,136,137,138,139,121,140,20],"glm output","output","tasks","metrics","json","basics","guidance","matching","verifier","processing","root",{"skillNo":142,"name":143,"nameZh":6,"description":144,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":145,"categories":146,"keywords":148,"publishedAt":36},"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",[147],{"categoryNo":89,"categoryName":90,"taxonomyType":73,"taxonomyTypeDesc":74},[19,20,21,131,136,135,132,149,150,24,151],"setup","companion","metric",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":158,"publishedAt":36},"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",[],[159,21,135,160,19,20,24,131,161,162,163,164,32,165,132],"glm basics","inspect","relevant","files","good","use","Evaluation"]