[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGG8D7W49JC50SQR8R0D8P":3,"skill-same:01M6KGG8D7W49JC50SQR8R0D8P":59},{"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":32,"linkOnly":33,"markdownContent":34,"markdownBody":35,"frontMatter":36,"refName":37,"commitSha":38,"skillDirectory":39,"forksCount":40,"repositoryHtmlUrl":41,"repositoryHomepageUrl":42,"ownerHtmlUrl":43,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":44,"install":45,"resources":58},"01M6KGG8D7W49JC50SQR8R0D8P","run2_pdf-parser",null,"An improved skill to programmatically parse PDF calendars, mapping drawing coordinates to precise time intervals using PyMuPDF.","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\u002Fschedule-planning\u002Frun2_pdf-parser\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31],"PDF","parser","pdf parser","run2 pdf parser","coordinates","time","drawing","mapping","pymupdf","2026-11-10 10:12:30",false,"---\nname: run2_pdf-parser\ndescription: An improved skill to programmatically parse PDF calendars, mapping drawing coordinates to precise time intervals using PyMuPDF.\n---\n\n# Improved PDF Schedule Parsing\n\nThis advanced skill automates the extraction of schedules from visual PDFs. Instead of manual inference, it mathematically calculates time blocks by using baseline anchor lines.\n\n## Dynamic Time Calculation\nBy identifying the top-most timeline grid line (e.g., the 12:00 AM line), you can calculate any Y-coordinate's corresponding time.\n\n```python\nimport fitz\n\ndef parse_calendar(pdf_path):\n    doc = fitz.open(pdf_path)\n    page = doc.load_page(0)\n    \n    # Extract lines to find the 15-minute grid spacing\n    lines = [d[\"rect\"] for d in page.get_drawings() if d.get(\"fill\") is None and d[\"rect\"].height \u003C 2]\n    lines.sort(key=lambda r: r.y0)\n    \n    base_y = lines[0].y0  # The 12:00 AM line\n    interval_y = lines[1].y0 - lines[0].y0  # Height of 15 minutes\n    hour_height = interval_y * 4\n    \n    # Extract rectangles (appointments)\n    # Exclude the background\u002Fgrid lines and thin borders by checking width\u002Fheight and colors\n    blocks = []\n    for d in page.get_drawings():\n        rect = d[\"rect\"]\n        color = d.get(\"fill\")\n        if color and rect.width > 100:  # Assuming meeting blocks span the column width\n            start_hour = (rect.y0 - base_y) \u002F hour_height\n            end_hour = (rect.y1 - base_y) \u002F hour_height\n            blocks.append({\n                \"start\": start_hour,\n                \"end\": end_hour,\n                \"color\": color\n            })\n    return blocks\n```\n\n## Identifying Flexible Slots\nBy checking the RGB values of the `fill` color, we can identify \"blue-colored\" blocks that denote flexible time and drop them from our strict occupied schedule list.\n\n","# Improved PDF Schedule Parsing\n\nThis advanced skill automates the extraction of schedules from visual PDFs. Instead of manual inference, it mathematically calculates time blocks by using baseline anchor lines.\n\n## Dynamic Time Calculation\nBy identifying the top-most timeline grid line (e.g., the 12:00 AM line), you can calculate any Y-coordinate's corresponding time.\n\n```python\nimport fitz\n\ndef parse_calendar(pdf_path):\n    doc = fitz.open(pdf_path)\n    page = doc.load_page(0)\n    \n    # Extract lines to find the 15-minute grid spacing\n    lines = [d[\"rect\"] for d in page.get_drawings() if d.get(\"fill\") is None and d[\"rect\"].height \u003C 2]\n    lines.sort(key=lambda r: r.y0)\n    \n    base_y = lines[0].y0  # The 12:00 AM line\n    interval_y = lines[1].y0 - lines[0].y0  # Height of 15 minutes\n    hour_height = interval_y * 4\n    \n    # Extract rectangles (appointments)\n    # Exclude the background\u002Fgrid lines and thin borders by checking width\u002Fheight and colors\n    blocks = []\n    for d in page.get_drawings():\n        rect = d[\"rect\"]\n        color = d.get(\"fill\")\n        if color and rect.width > 100:  # Assuming meeting blocks span the column width\n            start_hour = (rect.y0 - base_y) \u002F hour_height\n            end_hour = (rect.y1 - base_y) \u002F hour_height\n            blocks.append({\n                \"start\": start_hour,\n                \"end\": end_hour,\n                \"color\": color\n            })\n    return blocks\n```\n\n## Identifying Flexible Slots\nBy checking the RGB values of the `fill` color, we can identify \"blue-colored\" blocks that denote flexible time and drop them from our strict occupied schedule list.\n\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-gemini-3.1-pro-preview\u002Fschedule-planning\u002Frun2_pdf-parser",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":46,"prompt":56,"zipAvailable":57},[47,50,53],{"tool":48,"command":49},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_pdf-parser",{"tool":51,"command":52},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_pdf-parser",{"tool":54,"command":55},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_pdf-parser","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\u002Fschedule-planning\u002Frun2_pdf-parser\u002FSKILL.md",true,[],[60,76,101,119,139,151],{"skillNo":61,"name":62,"nameZh":6,"description":63,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":64,"categories":65,"keywords":69,"publishedAt":32},"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",[66],{"categoryNo":67,"categoryName":68,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[70,71,72,73,74,75],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":80,"categories":81,"keywords":85,"publishedAt":32},"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",[82],{"categoryNo":83,"categoryName":84,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[86,87,88,89,90,91,92,93,94,95,96,97,98,99,100],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":32},"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",[],[108,109,110,111,112,113,114,89,115,116,90,117,95,118,87],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":125,"publishedAt":32},"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",[],[126,127,128,95,129,130,131,132,133,134,135,136,117,137,138],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":140,"name":141,"nameZh":6,"description":142,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":143,"categories":144,"keywords":146,"publishedAt":32},"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",[145],{"categoryNo":83,"categoryName":84,"taxonomyType":20,"taxonomyTypeDesc":21},[147,138,128,127,133,132,129,148,149,95,150],"glm calibration","setup","companion","metric",{"skillNo":152,"name":153,"nameZh":6,"description":154,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":155,"categories":156,"keywords":157,"publishedAt":32},"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",[],[158,128,132,159,147,138,95,127,160,161,162,163,164,165,129],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]