[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KDXA372V0W7F06YVRRZXSQ":3,"skill-same:01M6KDXA372V0W7F06YVRRZXSQ":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":22,"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},"01M6KDXA372V0W7F06YVRRZXSQ","arxiv-paper-lookup",null,"Look up arxiv paper metadata (title, abstract, subject) from arxiv IDs to classify papers by topic without reading full PDFs.","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\u002Fb1-one-shot-claude-sonnet-4-6\u002Forganize-messy-files\u002Farxiv-paper-lookup\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35],"lookup","paper lookup","arxiv","paper","arxiv paper lookup","metadata","ids","up","look","full","classify","reading","papers","2026-11-10 10:12:30",false,"---\nname: arxiv-paper-lookup\ndescription: Look up arxiv paper metadata (title, abstract, subject) from arxiv IDs to classify papers by topic without reading full PDFs.\n---\n\n# Arxiv Paper Lookup Skill\n\n## Overview\nWhen files are named with arxiv IDs (e.g., `2305.12773v1.pdf`), you can extract metadata via the arxiv API to classify papers by subject without downloading\u002Freading the full PDF.\n\n## Extracting Arxiv ID from Filename\n```python\nimport re\n\ndef extract_arxiv_id(filename):\n    # Strip path and extension\n    base = os.path.basename(filename).replace('.pdf', '')\n    # Remove version suffix like v1, v2, v3\n    arxiv_id = re.sub(r'v\\d+$', '', base)\n    return arxiv_id  # e.g., \"2305.12773\"\n```\n\n## Fetching Metadata via Arxiv API\n```python\nimport urllib.request\nimport xml.etree.ElementTree as ET\n\ndef fetch_arxiv_metadata(arxiv_id):\n    url = f\"http:\u002F\u002Fexport.arxiv.org\u002Fapi\u002Fquery?id_list={arxiv_id}\"\n    with urllib.request.urlopen(url) as response:\n        data = response.read().decode('utf-8')\n\n    root = ET.fromstring(data)\n    ns = {'atom': 'http:\u002F\u002Fwww.w3.org\u002F2005\u002FAtom'}\n    entry = root.find('atom:entry', ns)\n    if entry is None:\n        return None\n\n    title = entry.find('atom:title', ns).text.strip()\n    summary = entry.find('atom:summary', ns).text.strip()\n    categories = [c.get('term') for c in entry.findall('{http:\u002F\u002Farxiv.org\u002Fschemas\u002Fatom}primary_category', ns)]\n\n    return {'title': title, 'summary': summary, 'categories': categories}\n```\n\n## Batch Fetching (up to 100 IDs at once)\n```python\ndef fetch_batch(arxiv_ids):\n    id_list = ','.join(arxiv_ids)\n    url = f\"http:\u002F\u002Fexport.arxiv.org\u002Fapi\u002Fquery?id_list={id_list}&max_results={len(arxiv_ids)}\"\n    # ... parse response\n```\n\n## Classification Strategy\n- Use title + abstract keywords to classify\n- Common arxiv categories: cs.CL, cs.AI (LLM), quant-ph (quantum), gr-qc (black hole), q-bio (DNA\u002Fbiology)\n- Titles are usually sufficient for classification\n\n## Rate Limiting\n- Arxiv API allows ~3 requests\u002Fsecond\n- Add `time.sleep(0.5)` between batch requests\n- Batch up to 100 IDs per request\n","# Arxiv Paper Lookup Skill\n\n## Overview\nWhen files are named with arxiv IDs (e.g., `2305.12773v1.pdf`), you can extract metadata via the arxiv API to classify papers by subject without downloading\u002Freading the full PDF.\n\n## Extracting Arxiv ID from Filename\n```python\nimport re\n\ndef extract_arxiv_id(filename):\n    # Strip path and extension\n    base = os.path.basename(filename).replace('.pdf', '')\n    # Remove version suffix like v1, v2, v3\n    arxiv_id = re.sub(r'v\\d+$', '', base)\n    return arxiv_id  # e.g., \"2305.12773\"\n```\n\n## Fetching Metadata via Arxiv API\n```python\nimport urllib.request\nimport xml.etree.ElementTree as ET\n\ndef fetch_arxiv_metadata(arxiv_id):\n    url = f\"http:\u002F\u002Fexport.arxiv.org\u002Fapi\u002Fquery?id_list={arxiv_id}\"\n    with urllib.request.urlopen(url) as response:\n        data = response.read().decode('utf-8')\n\n    root = ET.fromstring(data)\n    ns = {'atom': 'http:\u002F\u002Fwww.w3.org\u002F2005\u002FAtom'}\n    entry = root.find('atom:entry', ns)\n    if entry is None:\n        return None\n\n    title = entry.find('atom:title', ns).text.strip()\n    summary = entry.find('atom:summary', ns).text.strip()\n    categories = [c.get('term') for c in entry.findall('{http:\u002F\u002Farxiv.org\u002Fschemas\u002Fatom}primary_category', ns)]\n\n    return {'title': title, 'summary': summary, 'categories': categories}\n```\n\n## Batch Fetching (up to 100 IDs at once)\n```python\ndef fetch_batch(arxiv_ids):\n    id_list = ','.join(arxiv_ids)\n    url = f\"http:\u002F\u002Fexport.arxiv.org\u002Fapi\u002Fquery?id_list={id_list}&max_results={len(arxiv_ids)}\"\n    # ... parse response\n```\n\n## Classification Strategy\n- Use title + abstract keywords to classify\n- Common arxiv categories: cs.CL, cs.AI (LLM), quant-ph (quantum), gr-qc (black hole), q-bio (DNA\u002Fbiology)\n- Titles are usually sufficient for classification\n\n## Rate Limiting\n- Arxiv API allows ~3 requests\u002Fsecond\n- Add `time.sleep(0.5)` between batch requests\n- Batch up to 100 IDs per request\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-claude-sonnet-4-6\u002Forganize-messy-files\u002Farxiv-paper-lookup",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":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill arxiv-paper-lookup",{"tool":55,"command":56},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill arxiv-paper-lookup",{"tool":58,"command":59},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill arxiv-paper-lookup","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-claude-sonnet-4-6\u002Forganize-messy-files\u002Farxiv-paper-lookup\u002FSKILL.md",true,[],[64,80,105,121,141,153],{"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":73,"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":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[74,75,76,77,78,79],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":89,"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",[86],{"categoryNo":87,"categoryName":88,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[90,91,92,93,94,95,96,97,98,99,100,101,102,103,104],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":106,"name":107,"nameZh":6,"description":108,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":109,"categories":110,"keywords":111,"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",[],[112,113,114,115,116,117,30,93,31,118,94,119,99,120,91],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","flights","you","database",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":125,"categories":126,"keywords":127,"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",[],[128,129,130,99,131,132,133,134,135,136,137,138,119,139,140],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"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":87,"categoryName":88,"taxonomyType":20,"taxonomyTypeDesc":21},[149,140,130,129,135,134,131,150,151,99,152],"glm calibration","setup","companion","metric",{"skillNo":154,"name":155,"nameZh":6,"description":156,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":157,"categories":158,"keywords":159,"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",[],[160,130,134,161,149,140,99,129,162,163,164,165,166,167,131],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]