[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KDNTT229VQJFJMMYAQ49FR":3,"skill-same:01M6KDNTT229VQJFJMMYAQ49FR":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},"01M6KDNTT229VQJFJMMYAQ49FR","file-classification",null,"Classify academic papers and documents into subject categories using keyword-based text analysis.","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\u002Fb1-one-shot-claude-opus-4-6\u002Forganize-messy-files\u002Ffile-classification\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27],"file","classification","file classification","documents","analysis","text","Academic","classify","categories","papers","2026-11-10 10:12:30",false,"---\nname: file-classification\ndescription: Classify academic papers and documents into subject categories using keyword-based text analysis.\n---\n\n# File Classification by Subject\n\n## Approach: Keyword Scoring\nFor classifying documents into known categories, a keyword scoring approach is effective:\n\n1. Define keyword sets for each category\n2. Extract text from each document\n3. Score text against each keyword set (count occurrences)\n4. Assign document to highest-scoring category\n\n## Keyword Sets for This Task\n\n- **LLM**: language model, transformer, attention mechanism, GPT, BERT, token, prompt, fine-tuning, NLP, neural network, deep learning, text generation, embedding, LLM, large language, reinforcement learning from human feedback, RLHF, instruction tuning, pretraining, machine learning\n- **Trapped ion \u002F Quantum computing**: trapped ion, quantum computing, qubit, quantum gate, entanglement, quantum error, ion trap, quantum circuit, quantum algorithm, quantum processor, quantum information, Coulomb, motional mode, laser cooling, quantum simulation\n- **Black hole**: black hole, event horizon, Hawking radiation, singularity, gravitational, spacetime, general relativity, accretion, Schwarzschild, Kerr, entropy, holographic, AdS\u002FCFT, cosmological, dark energy, dark matter\n- **DNA**: DNA, genome, gene expression, nucleotide, protein, sequencing, CRISPR, mutation, chromosome, transcription, RNA, epigenetic, genetic, molecular biology, bioinformatics, cell, amino acid\n- **Music history**: music, composer, symphony, opera, baroque, classical period, jazz, rhythm, harmony, melody, instrument, musicology, sonata, concert, orchestra, musical\n\n## Implementation Pattern\n\n```python\ndef classify(text, keyword_sets):\n    text_lower = text.lower()\n    scores = {}\n    for category, keywords in keyword_sets.items():\n        scores[category] = sum(text_lower.count(kw.lower()) for kw in keywords)\n    return max(scores, key=scores.get)\n```\n","# File Classification by Subject\n\n## Approach: Keyword Scoring\nFor classifying documents into known categories, a keyword scoring approach is effective:\n\n1. Define keyword sets for each category\n2. Extract text from each document\n3. Score text against each keyword set (count occurrences)\n4. Assign document to highest-scoring category\n\n## Keyword Sets for This Task\n\n- **LLM**: language model, transformer, attention mechanism, GPT, BERT, token, prompt, fine-tuning, NLP, neural network, deep learning, text generation, embedding, LLM, large language, reinforcement learning from human feedback, RLHF, instruction tuning, pretraining, machine learning\n- **Trapped ion \u002F Quantum computing**: trapped ion, quantum computing, qubit, quantum gate, entanglement, quantum error, ion trap, quantum circuit, quantum algorithm, quantum processor, quantum information, Coulomb, motional mode, laser cooling, quantum simulation\n- **Black hole**: black hole, event horizon, Hawking radiation, singularity, gravitational, spacetime, general relativity, accretion, Schwarzschild, Kerr, entropy, holographic, AdS\u002FCFT, cosmological, dark energy, dark matter\n- **DNA**: DNA, genome, gene expression, nucleotide, protein, sequencing, CRISPR, mutation, chromosome, transcription, RNA, epigenetic, genetic, molecular biology, bioinformatics, cell, amino acid\n- **Music history**: music, composer, symphony, opera, baroque, classical period, jazz, rhythm, harmony, melody, instrument, musicology, sonata, concert, orchestra, musical\n\n## Implementation Pattern\n\n```python\ndef classify(text, keyword_sets):\n    text_lower = text.lower()\n    scores = {}\n    for category, keywords in keyword_sets.items():\n        scores[category] = sum(text_lower.count(kw.lower()) for kw in keywords)\n    return max(scores, key=scores.get)\n```\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-claude-opus-4-6\u002Forganize-messy-files\u002Ffile-classification",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 file-classification",{"tool":47,"command":48},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill file-classification",{"tool":50,"command":51},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill file-classification","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-claude-opus-4-6\u002Forganize-messy-files\u002Ffile-classification\u002FSKILL.md",true,[],[56,74,99,117,137,149],{"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",[],[124,125,126,93,127,128,129,130,131,132,133,134,115,135,136],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":144,"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",[143],{"categoryNo":81,"categoryName":82,"taxonomyType":65,"taxonomyTypeDesc":66},[145,136,126,125,131,130,127,146,147,93,148],"glm calibration","setup","companion","metric",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":155,"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",[],[156,126,130,157,145,136,93,125,158,159,160,161,162,163,127],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]