[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KGPMB48ZQ80084YYRNSNP2":3,"skill-same:01M6KGPMB48ZQ80084YYRNSNP2":68},{"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":41,"linkOnly":42,"markdownContent":43,"markdownBody":44,"frontMatter":45,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":51,"ownerHtmlUrl":52,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":53,"install":54,"resources":67},"01M6KGPMB48ZQ80084YYRNSNP2","product-grounded-artifact-retrieval",null,"Load enterprise data from \u002Froot\u002FDATA, identify the correct artifact version for each product mentioned in questions, and reject cross-product distractors. Apply strict 2-signal product grounding (artifact metadata + question context).","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\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fenterprise-information-search\u002Frun3_product-grounded-artifact-retrieval\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000031","产品经理","PROFESSION","适用职业",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40],"artifact","product grounded artifact retrieval","retrieval","product","correct","question","context","artifact metadata","version","data","Enterprise","questions","grounding","root","load","for","apply","metadata","2026-11-10 10:12:30",false,"---\nname: product-grounded-artifact-retrieval\ndescription: Load enterprise data from \u002Froot\u002FDATA, identify the correct artifact version for each product mentioned in questions, and reject cross-product distractors. Apply strict 2-signal product grounding (artifact metadata + question context).\n---\n\n# Product-Grounded Artifact Retrieval Skill\n\n## Step 1: Load Enterprise Data\n- Read all files from `\u002Froot\u002FDATA` directory\n- Index artifacts by product name and version (draft vs. final)\n- Track artifact metadata: creation date, status, product association\n- Build a product-to-artifacts mapping\n\n## Step 2: Two-Signal Product Grounding\nFor each question's artifact context:\n- **Signal 1**: Match product name from question to artifact metadata\n- **Signal 2**: Verify artifact status\u002Fversion (prefer final versions unless explicitly asking for draft)\n- Reject any artifacts from different products, even if semantically similar\n- Reject draft versions unless the question specifically asks for drafts\n- Document which artifacts were rejected and why\n\n## Step 3: Validate Product Isolation\n- Ensure no cross-product confusion (e.g., Product A reviewers mixed with Product B)\n- If multiple artifact versions exist for a product, select the correct one based on question intent\n- Fail explicitly if product grounding is ambiguous\n\n## Step 4: Return Grounded Artifacts\nReturn a mapping of:\n```python\n{\n  \"question_id\": \"q1\",\n  \"artifact_id\": \"artifact_name\",\n  \"product\": \"product_name\",\n  \"version\": \"final|draft\",\n  \"valid\": true\u002Ffalse,\n  \"rejection_reason\": \"if not valid\"\n}\n```","# Product-Grounded Artifact Retrieval Skill\n\n## Step 1: Load Enterprise Data\n- Read all files from `\u002Froot\u002FDATA` directory\n- Index artifacts by product name and version (draft vs. final)\n- Track artifact metadata: creation date, status, product association\n- Build a product-to-artifacts mapping\n\n## Step 2: Two-Signal Product Grounding\nFor each question's artifact context:\n- **Signal 1**: Match product name from question to artifact metadata\n- **Signal 2**: Verify artifact status\u002Fversion (prefer final versions unless explicitly asking for draft)\n- Reject any artifacts from different products, even if semantically similar\n- Reject draft versions unless the question specifically asks for drafts\n- Document which artifacts were rejected and why\n\n## Step 3: Validate Product Isolation\n- Ensure no cross-product confusion (e.g., Product A reviewers mixed with Product B)\n- If multiple artifact versions exist for a product, select the correct one based on question intent\n- Fail explicitly if product grounding is ambiguous\n\n## Step 4: Return Grounded Artifacts\nReturn a mapping of:\n```python\n{\n  \"question_id\": \"q1\",\n  \"artifact_id\": \"artifact_name\",\n  \"product\": \"product_name\",\n  \"version\": \"final|draft\",\n  \"valid\": true\u002Ffalse,\n  \"rejection_reason\": \"if not valid\"\n}\n```",{"name":5,"description":7},"main","726e1f79b96b0cb3d729d0e55a1c847e0b5353ae","skills\u002Fb3-teacher-feedback-claude-haiku-4-5\u002Fenterprise-information-search\u002Frun3_product-grounded-artifact-retrieval",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":55,"prompt":65,"zipAvailable":66},[56,59,62],{"tool":57,"command":58},"npx","npx skills add cxcscmu\u002FSkillLearnBench --skill product-grounded-artifact-retrieval",{"tool":60,"command":61},"bunx","bunx skills add cxcscmu\u002FSkillLearnBench --skill product-grounded-artifact-retrieval",{"tool":63,"command":64},"pnpm","pnpm dlx skills add cxcscmu\u002FSkillLearnBench --skill product-grounded-artifact-retrieval","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_product-grounded-artifact-retrieval\u002FSKILL.md",true,[],[69,87,111,129,148,160],{"skillNo":70,"name":71,"nameZh":6,"description":72,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":73,"categories":74,"keywords":80,"publishedAt":41},"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",[75],{"categoryNo":76,"categoryName":77,"taxonomyType":78,"taxonomyTypeDesc":79},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",[81,82,83,84,85,86],"object counter","object","vision","computer","Computer Vision","image",{"skillNo":88,"name":89,"nameZh":6,"description":90,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":91,"categories":92,"keywords":96,"publishedAt":41},"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",[93],{"categoryNo":94,"categoryName":95,"taxonomyType":78,"taxonomyTypeDesc":79},"01JBSEEDCAT000000000000023","Skill与智能体开发",[97,98,99,100,101,102,103,104,105,38,106,107,108,109,110],"workflow","planning","planning workflow","travel","travel planning","travel planning workflow","structured","performing","plan","user","it","requirements","essential","itineraries",{"skillNo":112,"name":113,"nameZh":6,"description":114,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":115,"categories":116,"keywords":117,"publishedAt":41},"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",[],[118,119,120,121,122,123,124,100,125,126,101,127,38,128,98],"search","tool","dataset","bundled dataset search tool","restaurants","Tools","up","look","flights","you","database",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":41},"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",[],[136,137,138,38,139,140,141,142,143,144,145,146,127,36,147],"glm output","output","glm","tasks","metrics","json","basics","guidance","matching","verifier","processing","calibration",{"skillNo":149,"name":150,"nameZh":6,"description":151,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":152,"categories":153,"keywords":155,"publishedAt":41},"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",[154],{"categoryNo":94,"categoryName":95,"taxonomyType":78,"taxonomyTypeDesc":79},[156,147,138,137,143,142,139,157,158,38,159],"glm calibration","setup","companion","metric",{"skillNo":161,"name":162,"nameZh":6,"description":163,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":164,"categories":165,"keywords":166,"publishedAt":41},"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",[],[167,138,142,168,156,147,38,137,169,170,171,172,173,174,139],"glm basics","inspect","relevant","files","good","use","how","Evaluation"]