[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KF9Q27NFMPWZXCTQ2QR7K3":3,"skill-same:01M6KF9Q27NFMPWZXCTQ2QR7K3":65},{"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":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":48,"ownerHtmlUrl":49,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":50,"install":51,"resources":64},"01M6KF9Q27NFMPWZXCTQ2QR7K3","run2_pdf-extraction",null,"Robust PDF text extraction with fallback strategies and error handling for academic papers and documents","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\u002Fb2-self-feedback-claude-haiku-4-5\u002Forganize-messy-files\u002Frun2_pdf-extraction\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000002","文档处理","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"PDF","extraction","pdf extraction","run2 pdf extraction","documents","error handling","text","fallback","Academic","strategies","error","with","papers","for","pdf text extraction","2026-11-10 10:12:30",false,"---\nname: run2_pdf-extraction\ndescription: Robust PDF text extraction with fallback strategies and error handling for academic papers and documents\n---\n\n# PDF Text Extraction Skill (Improved)\n\n## Overview\nExtract text from PDF files with improved robustness, handling corrupt PDFs gracefully.\nMultiple extraction strategies ensure maximum content recovery.\n\n## Installation\n```bash\npip install pdfplumber pypdf pymupdf -q\n```\n\n## Usage Examples\n\n### Primary Strategy: pdfplumber (Most Reliable)\n```python\nimport pdfplumber\nimport PyPDF2\n\ndef extract_pdf_content_robust(pdf_path, max_pages=5):\n    \"\"\"\n    Extract text from PDF with fallback strategies.\n    Tries pdfplumber first, then falls back to PyPDF2.\n    \"\"\"\n    text = \"\"\n\n    # Strategy 1: pdfplumber (best for modern PDFs)\n    try:\n        with pdfplumber.open(pdf_path) as pdf:\n            for i, page in enumerate(pdf.pages[:max_pages]):\n                extracted = page.extract_text()\n                if extracted:\n                    text += extracted + \" \"\n        if len(text.strip()) > 100:  # If we got substantial text\n            return text\n    except Exception as e:\n        pass  # Try fallback\n\n    # Strategy 2: PyPDF2 (fallback for problematic PDFs)\n    try:\n        with open(pdf_path, 'rb') as f:\n            reader = PyPDF2.PdfReader(f)\n            for page in reader.pages[:max_pages]:\n                text += page.extract_text() + \" \"\n        if len(text.strip()) > 100:\n            return text\n    except Exception as e:\n        pass\n\n    return text or \"\"\n```\n\n### Optimized Extraction for Academic Papers\n```python\ndef extract_paper_metadata(pdf_path, max_pages=3):\n    \"\"\"\n    Extract key content from academic papers:\n    - Title (usually on first page)\n    - Abstract (usually first 3 pages)\n    - Keywords (sometimes in abstract area)\n    \"\"\"\n    try:\n        with pdfplumber.open(pdf_path) as pdf:\n            # Extract first 3 pages which contain title and abstract\n            text = \"\"\n            for page in pdf.pages[:max_pages]:\n                text += page.extract_text() or \"\"\n            return text\n    except:\n        return \"\"\n```\n\n## Key Improvements\n- **Dual extraction strategy**: pdfplumber + PyPDF2 fallback\n- **Robustness**: Handles corrupt\u002Fdamaged PDFs gracefully\n- **Academic focus**: Optimized for extracting paper abstracts and titles\n- **Efficiency**: Returns early if sufficient text extracted\n- **Error isolation**: Failures in one method don't prevent trying alternatives\n\n## Best Practices\n- For academic papers, first 3-5 pages contain most essential information\n- Title and abstract are usually in first 2 pages\n- Some PDFs are scanned images - these need OCR (pymupdf with OCR)\n- Check text length to verify successful extraction\n- Handle encoding issues gracefully\n","# PDF Text Extraction Skill (Improved)\n\n## Overview\nExtract text from PDF files with improved robustness, handling corrupt PDFs gracefully.\nMultiple extraction strategies ensure maximum content recovery.\n\n## Installation\n```bash\npip install pdfplumber pypdf pymupdf -q\n```\n\n## Usage Examples\n\n### Primary Strategy: pdfplumber (Most Reliable)\n```python\nimport pdfplumber\nimport PyPDF2\n\ndef extract_pdf_content_robust(pdf_path, max_pages=5):\n    \"\"\"\n    Extract text from PDF with fallback strategies.\n    Tries pdfplumber first, then falls back to PyPDF2.\n    \"\"\"\n    text = \"\"\n\n    # Strategy 1: pdfplumber (best for modern PDFs)\n    try:\n        with pdfplumber.open(pdf_path) as pdf:\n            for i, page in enumerate(pdf.pages[:max_pages]):\n                extracted = page.extract_text()\n                if extracted:\n                    text += extracted + \" \"\n        if len(text.strip()) > 100:  # If we got substantial text\n            return text\n    except Exception as e:\n        pass  # Try fallback\n\n    # Strategy 2: PyPDF2 (fallback for problematic PDFs)\n    try:\n        with open(pdf_path, 'rb') as f:\n            reader = PyPDF2.PdfReader(f)\n            for page in reader.pages[:max_pages]:\n                text += page.extract_text() + \" \"\n        if len(text.strip()) > 100:\n            return text\n    except Exception as e:\n        pass\n\n    return text or \"\"\n```\n\n### Optimized Extraction for Academic Papers\n```python\ndef extract_paper_metadata(pdf_path, max_pages=3):\n    \"\"\"\n    Extract key content from academic papers:\n    - Title (usually on first page)\n    - Abstract (usually first 3 pages)\n    - Keywords (sometimes in abstract area)\n    \"\"\"\n    try:\n        with pdfplumber.open(pdf_path) as pdf:\n            # Extract first 3 pages which contain title and abstract\n            text = \"\"\n            for page in pdf.pages[:max_pages]:\n                text += page.extract_text() or \"\"\n            return text\n    except:\n        return \"\"\n```\n\n## Key Improvements\n- **Dual extraction strategy**: pdfplumber + PyPDF2 fallback\n- **Robustness**: Handles corrupt\u002Fdamaged PDFs gracefully\n- **Academic focus**: Optimized for extracting paper abstracts and titles\n- **Efficiency**: Returns early if sufficient text extracted\n- **Error isolation**: Failures in one method don't prevent trying alternatives\n\n## Best Practices\n- For academic papers, first 3-5 pages contain most essential information\n- Title and abstract are usually in first 2 pages\n- Some PDFs are scanned images - these need OCR (pymupdf with OCR)\n- Check text length to verify successful extraction\n- Handle encoding issues gracefully\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb2-self-feedback-claude-haiku-4-5\u002Forganize-messy-files\u002Frun2_pdf-extraction",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":52,"prompt":62,"zipAvailable":63},[53,56,59],{"tool":54,"command":55},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill run2_pdf-extraction",{"tool":57,"command":58},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill run2_pdf-extraction",{"tool":60,"command":61},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill run2_pdf-extraction","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb2-self-feedback-claude-haiku-4-5\u002Forganize-messy-files\u002Frun2_pdf-extraction\u002FSKILL.md",true,[],[66,82,106,124,144,156],{"skillNo":67,"name":68,"nameZh":6,"description":69,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":70,"categories":71,"keywords":75,"publishedAt":38},"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",[72],{"categoryNo":73,"categoryName":74,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[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":38},"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":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[92,93,94,95,96,97,98,99,100,36,101,102,103,104,105],"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":38},"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",[],[113,114,115,116,117,118,119,95,120,121,96,122,36,123,93],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":125,"name":126,"nameZh":6,"description":127,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":128,"categories":129,"keywords":130,"publishedAt":38},"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",[],[131,132,133,36,134,135,136,137,138,139,140,141,122,142,143],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","root","calibration",{"skillNo":145,"name":146,"nameZh":6,"description":147,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":148,"categories":149,"keywords":151,"publishedAt":38},"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",[150],{"categoryNo":89,"categoryName":90,"taxonomyType":20,"taxonomyTypeDesc":21},[152,143,133,132,138,137,134,153,154,36,155],"glm calibration","setup","companion","metric",{"skillNo":157,"name":158,"nameZh":6,"description":159,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":160,"categories":161,"keywords":162,"publishedAt":38},"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",[],[163,133,137,164,152,143,36,132,165,166,167,168,169,170,134],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]