[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6KJSV63TSFM5PWJRP2HJREA":3,"skill-same:01M6KJSV63TSFM5PWJRP2HJREA":65},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":33,"linkOnly":34,"markdownContent":35,"markdownBody":36,"frontMatter":37,"refName":38,"commitSha":39,"skillDirectory":40,"forksCount":13,"repositoryHtmlUrl":41,"repositoryHomepageUrl":6,"ownerHtmlUrl":42,"ownerBlogUrl":6,"ownerDescription":43,"updatedAt":44,"install":45,"resources":58},"01M6KJSV63TSFM5PWJRP2HJREA","prioritize-context-data",null,"Ensures agents check and use provided context files for data before attempting external searches.","AG064\u002FOpenSpace","AG064","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F184930299?v=4","01M6KHVFG7TB73NHX3RDYSTMH2","01M6KHVFMM7XA2PZA9Z83PHS3V",0,"MIT","https:\u002F\u002Fgithub.com\u002FAG064\u002FOpenSpace\u002Fblob\u002Fmain\u002Fgdpval_bench\u002Fskills\u002Fprioritize-context-data\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[23,24,25,26,27,28,29,30,31,32],"data","prioritize","context","prioritize context data","workflow","check","use","files","context files","for","2026-11-10 10:12:30",false,"---\nname: prioritize-context-data\ndescription: Ensures agents check and use provided context files for data before attempting external searches.\ncategory: workflow\n---\n\n# Prioritize Context Data\n\n## Objective\nPrevent data hallucination and inefficiency by mandating that agents inspect and utilize provided reference files (CSV, XLSX, PDF, TXT, etc.) before attempting web searches or generating synthetic data.\n\n## Critical Rule\n**If a reference file is provided in the task context, it is the source of truth.** Do not fabricate data or search the web for information that exists within the provided attachments.\n\n## Workflow Steps\n\n### 1. Scan Context for Attachments\nAt the start of every task, explicitly list all files provided in the context window or attachment panel.\n- Check for spreadsheets (`.xlsx`, `.csv`), documents (`.pdf`, `.docx`), or data dumps (`.json`, `.txt`).\n- Note the filename and inferred content type.\n\n### 2. Evaluate Relevance\nDetermine if any provided file contains the data required to complete the task.\n- **Match Keywords:** Do filenames or column headers match task requirements?\n- **Check Scope:** Does the data cover the necessary timeframe or地域 (region)?\n\n### 3. Extract Data First\nIf relevant files are found:\n- Read the file content using appropriate tools (e.g., `read_file`, `pandas`, `pdf_reader`).\n- Extract the specific data points needed.\n- **Do not** proceed to web search until you have confirmed the file lacks the necessary information.\n\n### 4. Cite Source Explicitly\nWhen presenting data in the final output:\n- Explicitly state which file the data came from.\n- Example: \"According to `Massabama_active_listings.xlsx`...\"\n- This verifies to the user that real data was used, not hallucinated.\n\n### 5. Fallback to Search (Only if Necessary)\nIf the provided files do not contain the specific data needed:\n- State clearly what was missing from the files.\n- Then proceed with web search or estimation.\n- Mark any non-file data as \"External Search\" or \"Estimated\".\n\n## Checklist\n\n- [ ] Did I list all attached files?\n- [ ] Did I open and read the relevant files?\n- [ ] Did I verify the data exists in the files before searching?\n- [ ] Did I cite the file name in my output?\n- [ ] Did I avoid fabricating numbers that should have come from the file?\n\n## Example Usage\n\n**Task:** Create a report on active property listings.\n**Context:** `Massabama_active_listings.xlsx` is attached.\n\n**Incorrect Approach:**\n- Ignore the Excel file.\n- Search web for \"Massabama property listings\".\n- Fabricate numbers based on search snippets.\n- **Result:** Inaccurate data, ignored source of truth.\n\n**Correct Approach:**\n- Identify `Massabama_active_listings.xlsx` in context.\n- Load the Excel file.\n- Count rows and sum prices directly from the sheet.\n- Output: \"Based on the 50 entries in `Massabama_active_listings.xlsx`, the total value is...\"\n- **Result:** Accurate data, verified source.\n\n## Warnings\n\n- **Hallucination Risk:** Ignoring provided files is a primary cause of data fabrication.\n- **Efficiency:** Reading a local file is faster and more reliable than web scraping.\n- **User Expectation:** Users attach files expecting them to be used. Ignoring them is a failure of instruction following.","# Prioritize Context Data\n\n## Objective\nPrevent data hallucination and inefficiency by mandating that agents inspect and utilize provided reference files (CSV, XLSX, PDF, TXT, etc.) before attempting web searches or generating synthetic data.\n\n## Critical Rule\n**If a reference file is provided in the task context, it is the source of truth.** Do not fabricate data or search the web for information that exists within the provided attachments.\n\n## Workflow Steps\n\n### 1. Scan Context for Attachments\nAt the start of every task, explicitly list all files provided in the context window or attachment panel.\n- Check for spreadsheets (`.xlsx`, `.csv`), documents (`.pdf`, `.docx`), or data dumps (`.json`, `.txt`).\n- Note the filename and inferred content type.\n\n### 2. Evaluate Relevance\nDetermine if any provided file contains the data required to complete the task.\n- **Match Keywords:** Do filenames or column headers match task requirements?\n- **Check Scope:** Does the data cover the necessary timeframe or地域 (region)?\n\n### 3. Extract Data First\nIf relevant files are found:\n- Read the file content using appropriate tools (e.g., `read_file`, `pandas`, `pdf_reader`).\n- Extract the specific data points needed.\n- **Do not** proceed to web search until you have confirmed the file lacks the necessary information.\n\n### 4. Cite Source Explicitly\nWhen presenting data in the final output:\n- Explicitly state which file the data came from.\n- Example: \"According to `Massabama_active_listings.xlsx`...\"\n- This verifies to the user that real data was used, not hallucinated.\n\n### 5. Fallback to Search (Only if Necessary)\nIf the provided files do not contain the specific data needed:\n- State clearly what was missing from the files.\n- Then proceed with web search or estimation.\n- Mark any non-file data as \"External Search\" or \"Estimated\".\n\n## Checklist\n\n- [ ] Did I list all attached files?\n- [ ] Did I open and read the relevant files?\n- [ ] Did I verify the data exists in the files before searching?\n- [ ] Did I cite the file name in my output?\n- [ ] Did I avoid fabricating numbers that should have come from the file?\n\n## Example Usage\n\n**Task:** Create a report on active property listings.\n**Context:** `Massabama_active_listings.xlsx` is attached.\n\n**Incorrect Approach:**\n- Ignore the Excel file.\n- Search web for \"Massabama property listings\".\n- Fabricate numbers based on search snippets.\n- **Result:** Inaccurate data, ignored source of truth.\n\n**Correct Approach:**\n- Identify `Massabama_active_listings.xlsx` in context.\n- Load the Excel file.\n- Count rows and sum prices directly from the sheet.\n- Output: \"Based on the 50 entries in `Massabama_active_listings.xlsx`, the total value is...\"\n- **Result:** Accurate data, verified source.\n\n## Warnings\n\n- **Hallucination Risk:** Ignoring provided files is a primary cause of data fabrication.\n- **Efficiency:** Reading a local file is faster and more reliable than web scraping.\n- **User Expectation:** Users attach files expecting them to be used. 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Reference for extracting patterns — vanilla TypeScript, Panel class hierarchy, service layer, CSS grid, API edge functions, refresh scheduler, and 60+ data source integrations.","https:\u002F\u002Fgithub.com\u002FAG064\u002FOpenSpace\u002Fblob\u002Fmain\u002Fshowcase\u002Fskills\u002Fworldmonitor-reference\u002FSKILL.md",[119],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[121,122,123,124,86,125,126,127,128,129,32,130,131,132,133,134,135,136,23,137],"reference","worldmonitor","worldmonitor reference","dashboard","css","architecture","extracting","hierarchy","source","layer","panel","patterns","edge functions","integrations","open","api","scheduler",{"skillNo":139,"name":140,"nameZh":6,"description":141,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":142,"categories":143,"keywords":144,"publishedAt":33},"01M6KKM39PHYK627F19GE2B9XN","virtual-list","Implement efficient virtual scrolling for rendering large lists with DOM recycling, chunk-based rendering, and performance optimizations","https:\u002F\u002Fgithub.com\u002FAG064\u002FOpenSpace\u002Fblob\u002Fmain\u002Fshowcase\u002Fskills\u002Fvirtual-list\u002FSKILL.md",[],[145,146,147,148,32,149,103,150,151,152,153],"virtual list","list","virtual","chunk","dom","rendering","recycling","implement","performance",{"skillNo":155,"name":156,"nameZh":6,"description":157,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":9,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":158,"categories":159,"keywords":160,"publishedAt":33},"01M6KKKY7T2P61Y0JH54CDEJKQ","verify-directory-structure","Diagnose and resolve file access issues by first verifying the directory structure.","https:\u002F\u002Fgithub.com\u002FAG064\u002FOpenSpace\u002Fblob\u002Fmain\u002Fshowcase\u002Fskills\u002Fverify-directory-structure\u002FSKILL.md",[],[161,162,163,164,165,166,84,167,168,169],"directory","verify","structure","verify directory structure","access","first","issues","diagnose","file access"]