[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGPR66TFXSBCM0HCD3B0HN":3,"skill-same:01M6KGPR66TFXSBCM0HCD3B0HN":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":17,"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},"01M6KGPR66TFXSBCM0HCD3B0HN","question-parsing",null,"Load and parse questions from \u002Froot\u002Fquestion.txt, extract question IDs and their artifact\u002Fproduct context. Validate that all questions are correctly mapped before proceeding to data retrieval.","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\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fenterprise-information-search\u002Frun3_question-parsing\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31],"parsing","question","question parsing","validate","product","data","ids","context","extract","artifact","retrieval","questions","root","load","2026-11-10 10:12:30",false,"---\nname: question-parsing\ndescription: Load and parse questions from \u002Froot\u002Fquestion.txt, extract question IDs and their artifact\u002Fproduct context. Validate that all questions are correctly mapped before proceeding to data retrieval.\n---\n\n# Question Parsing Skill\n\n## Step 1: Load Question File\n- Read `\u002Froot\u002Fquestion.txt` completely\n- Handle any encoding issues (UTF-8)\n- Preserve all question entries\n\n## Step 2: Parse Each Question\nFor each line\u002Fentry in the file:\n- Extract the question ID (e.g., \"q1\", \"q2\", \"q3\")\n- Extract the question text\n- Identify the **artifact\u002Fproduct context** mentioned in the question\n- Identify the **entity type** being asked for (e.g., reviewers, contributors, participants)\n- Store parsed metadata in a structured dictionary\n\n## Step 3: Validate and Debug\n- Print all parsed questions with their IDs, artifact contexts, and entity types\n- Verify the total count matches expectations\n- Check for any malformed entries\n- Confirm key names and values are correctly extracted\n\n## Step 4: Return Parsed Questions\nReturn a dictionary mapping question IDs to:\n```python\n{\n  \"question_id\": \"q1\",\n  \"text\": \"full question text\",\n  \"artifact_context\": \"artifact or product name\",\n  \"entity_type\": \"reviewers|contributors|participants|etc\",\n  \"parsed_at\": timestamp\n}\n```","# Question Parsing Skill\n\n## Step 1: Load Question File\n- Read `\u002Froot\u002Fquestion.txt` completely\n- Handle any encoding issues (UTF-8)\n- Preserve all question entries\n\n## Step 2: Parse Each Question\nFor each line\u002Fentry in the file:\n- Extract the question ID (e.g., \"q1\", \"q2\", \"q3\")\n- Extract the question text\n- Identify the **artifact\u002Fproduct context** mentioned in the question\n- Identify the **entity type** being asked for (e.g., reviewers, contributors, participants)\n- Store parsed metadata in a structured dictionary\n\n## Step 3: Validate and Debug\n- Print all parsed questions with their IDs, artifact contexts, and entity types\n- Verify the total count matches expectations\n- Check for any malformed entries\n- Confirm key names and values are correctly extracted\n\n## Step 4: Return Parsed Questions\nReturn a dictionary mapping question IDs to:\n```python\n{\n  \"question_id\": \"q1\",\n  \"text\": \"full question text\",\n  \"artifact_context\": \"artifact or product name\",\n  \"entity_type\": \"reviewers|contributors|participants|etc\",\n  \"parsed_at\": timestamp\n}\n```",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fenterprise-information-search\u002Frun3_question-parsing",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":46,"prompt":56,"zipAvailable":57},[47,50,53],{"tool":48,"command":49},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill question-parsing",{"tool":51,"command":52},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill question-parsing",{"tool":54,"command":55},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill question-parsing","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fcxcscmu\u002FSkillLearnBench\u002Fblob\u002Fmain\u002Fskills\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fenterprise-information-search\u002Frun3_question-parsing\u002FSKILL.md",true,[],[60,78,103,121,140,152],{"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":71,"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":69,"taxonomyTypeDesc":70},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[72,73,74,75,76,77],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":79,"name":80,"nameZh":6,"description":81,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":82,"categories":83,"keywords":87,"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",[84],{"categoryNo":85,"categoryName":86,"taxonomyType":69,"taxonomyTypeDesc":70},"01JBSEEDCAT000000000000023","Skill与智能体开发",[88,89,90,91,92,93,94,95,96,97,98,99,100,101,102],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","for","user","it","requirements","essential","itineraries",{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"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",[],[110,111,112,113,114,115,116,91,117,118,92,119,97,120,89],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","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":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",[],[128,129,130,97,131,132,133,134,135,136,137,138,119,30,139],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","calibration",{"skillNo":141,"name":142,"nameZh":6,"description":143,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":144,"categories":145,"keywords":147,"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",[146],{"categoryNo":85,"categoryName":86,"taxonomyType":69,"taxonomyTypeDesc":70},[148,139,130,129,135,134,131,149,150,97,151],"glm calibration","setup","companion","metric",{"skillNo":153,"name":154,"nameZh":6,"description":155,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":156,"categories":157,"keywords":158,"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",[],[159,130,134,160,148,139,97,129,161,162,163,164,165,166,131],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]