[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KEYF1Y6G4G2CQQQTM98560":3,"skill-same:01M6KEYF1Y6G4G2CQQQTM98560":58},{"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":31,"linkOnly":32,"markdownContent":33,"markdownBody":34,"frontMatter":35,"refName":36,"commitSha":37,"skillDirectory":38,"forksCount":39,"repositoryHtmlUrl":40,"repositoryHomepageUrl":41,"ownerHtmlUrl":42,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":43,"install":44,"resources":57},"01M6KEYF1Y6G4G2CQQQTM98560","csv-reporting",null,"Output vulnerability reports as a formatted CSV file.","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-gemini-3.1-pro-preview\u002Fdependency-vulnerability-check\u002Fcsv-reporting\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000004","电子表格","SCENARIO","使用场景",[23,24,25,26,27,28,29,30],"csv","reporting","csv reporting","file","vulnerability","reports","as","output","2026-11-10 10:12:30",false,"---\nname: csv-reporting\ndescription: Output vulnerability reports as a formatted CSV file.\n---\n\n# Vulnerability CSV Reporting\n\nAfter parsing a vulnerability JSON report from a tool like Trivy, it is common to output the extracted findings to a CSV file for security audits or compliance checks.\n\n## Usage\n\nThis Python code snippet demonstrates how to export vulnerabilities to a CSV file according to a defined schema, such as: `Package,Version,CVE_ID,Severity,CVSS_Score,Fixed_Version,Title,Url`.\n\n```python\nimport csv\nimport json\n\ndef generate_csv(json_path, csv_path):\n    with open(json_path, 'r', encoding='utf-8') as f:\n        trivy_report = json.load(f)\n\n    # Required CSV fields\n    fieldnames = ['Package', 'Version', 'CVE_ID', 'Severity', 'CVSS_Score', 'Fixed_Version', 'Title', 'Url']\n\n    with open(csv_path, 'w', newline='', encoding='utf-8') as f:\n        writer = csv.DictWriter(f, fieldnames=fieldnames)\n        writer.writeheader()\n\n        for result in trivy_report.get('Results', []):\n            for vuln in result.get('Vulnerabilities', []):\n                # Only include HIGH and CRITICAL severities\n                severity = vuln.get('Severity', '')\n                if severity not in ['HIGH', 'CRITICAL']:\n                    continue\n\n                # Prepare the row\n                row = {\n                    'Package': vuln.get('PkgName', ''),\n                    'Version': vuln.get('InstalledVersion', ''),\n                    'CVE_ID': vuln.get('VulnerabilityID', ''),\n                    'Severity': severity,\n                    'CVSS_Score': extract_cvss(vuln),\n                    'Fixed_Version': vuln.get('FixedVersion', 'N\u002FA'),\n                    'Title': vuln.get('Title', vuln.get('Description', '')).replace('\\n', ' ').strip()[:100],\n                    'Url': vuln.get('PrimaryURL', '')\n                }\n                writer.writerow(row)\n\n# The extract_cvss function would be defined elsewhere or imported\n```\n\nThis ensures properly-formatted tabular reports. Note how it limits the output strictly to HIGH and CRITICAL vulnerabilities.\n","# Vulnerability CSV Reporting\n\nAfter parsing a vulnerability JSON report from a tool like Trivy, it is common to output the extracted findings to a CSV file for security audits or compliance checks.\n\n## Usage\n\nThis Python code snippet demonstrates how to export vulnerabilities to a CSV file according to a defined schema, such as: `Package,Version,CVE_ID,Severity,CVSS_Score,Fixed_Version,Title,Url`.\n\n```python\nimport csv\nimport json\n\ndef generate_csv(json_path, csv_path):\n    with open(json_path, 'r', encoding='utf-8') as f:\n        trivy_report = json.load(f)\n\n    # Required CSV fields\n    fieldnames = ['Package', 'Version', 'CVE_ID', 'Severity', 'CVSS_Score', 'Fixed_Version', 'Title', 'Url']\n\n    with open(csv_path, 'w', newline='', encoding='utf-8') as f:\n        writer = csv.DictWriter(f, fieldnames=fieldnames)\n        writer.writeheader()\n\n        for result in trivy_report.get('Results', []):\n            for vuln in result.get('Vulnerabilities', []):\n                # Only include HIGH and CRITICAL severities\n                severity = vuln.get('Severity', '')\n                if severity not in ['HIGH', 'CRITICAL']:\n                    continue\n\n                # Prepare the row\n                row = {\n                    'Package': vuln.get('PkgName', ''),\n                    'Version': vuln.get('InstalledVersion', ''),\n                    'CVE_ID': vuln.get('VulnerabilityID', ''),\n                    'Severity': severity,\n                    'CVSS_Score': extract_cvss(vuln),\n                    'Fixed_Version': vuln.get('FixedVersion', 'N\u002FA'),\n                    'Title': vuln.get('Title', vuln.get('Description', '')).replace('\\n', ' ').strip()[:100],\n                    'Url': vuln.get('PrimaryURL', '')\n                }\n                writer.writerow(row)\n\n# The extract_cvss function would be defined elsewhere or imported\n```\n\nThis ensures properly-formatted tabular reports. Note how it limits the output strictly to HIGH and CRITICAL vulnerabilities.\n",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fdependency-vulnerability-check\u002Fcsv-reporting",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":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill csv-reporting",{"tool":50,"command":51},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill csv-reporting",{"tool":53,"command":54},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill csv-reporting","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb1-one-shot-gemini-3.1-pro-preview\u002Fdependency-vulnerability-check\u002Fcsv-reporting\u002FSKILL.md",true,[],[59,75,100,118,137,149],{"skillNo":60,"name":61,"nameZh":6,"description":62,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":63,"categories":64,"keywords":68,"publishedAt":31},"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",[65],{"categoryNo":66,"categoryName":67,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体",[69,70,71,72,73,74],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":76,"name":77,"nameZh":6,"description":78,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":79,"categories":80,"keywords":84,"publishedAt":31},"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",[81],{"categoryNo":82,"categoryName":83,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",[85,86,87,88,89,90,91,92,93,94,95,96,97,98,99],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":101,"name":102,"nameZh":6,"description":103,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":104,"categories":105,"keywords":106,"publishedAt":31},"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",[],[107,108,109,110,111,112,113,88,114,115,89,116,94,117,86],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":119,"name":120,"nameZh":6,"description":121,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":122,"categories":123,"keywords":124,"publishedAt":31},"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",[],[125,30,126,94,127,128,129,130,131,132,133,134,116,135,136],"glm 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":31},"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":82,"categoryName":83,"taxonomyType":20,"taxonomyTypeDesc":21},[145,136,126,30,131,130,127,146,147,94,148],"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":31},"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",[],[156,126,130,157,145,136,94,30,158,159,160,161,162,163,127],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]