{ "schemaVersion": "1.0", "item": { "slug": "excel-xlsx", "name": "Excel / XLSX", "source": "tencent", "type": "skill", "category": "数据分析", "sourceUrl": "https://clawhub.ai/ivangdavila/excel-xlsx", "canonicalUrl": "https://clawhub.ai/ivangdavila/excel-xlsx", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/excel-xlsx", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=excel-xlsx", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "SKILL.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "sourceHealth": { "source": "tencent", "status": "healthy", "reason": "direct_download_ok", "recommendedAction": "download", "checkedAt": "2026-04-23T16:43:11.935Z", "expiresAt": "2026-04-30T16:43:11.935Z", "httpStatus": 200, "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentType": "application/zip", "probeMethod": "head", "details": { "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=4claw-imageboard", "contentDisposition": "attachment; filename=\"4claw-imageboard-1.0.1.zip\"", "redirectLocation": null, "bodySnippet": null }, "scope": "source", "summary": "Source download looks usable.", "detail": "Yavira can redirect you to the upstream package for this source.", "primaryActionLabel": "Download for OpenClaw", "primaryActionHref": "/downloads/excel-xlsx" }, "validation": { "installChecklist": [ "Use the Yavira download entry.", "Review SKILL.md after the package is downloaded.", "Confirm the extracted package contains the expected setup assets." ], "postInstallChecks": [ "Confirm the extracted package includes the expected docs or setup files.", "Validate the skill or prompts are available in your target agent workspace.", "Capture any manual follow-up steps the agent could not complete." ] }, "downloadPageUrl": "https://openagent3.xyz/downloads/excel-xlsx", "agentPageUrl": "https://openagent3.xyz/skills/excel-xlsx/agent", "manifestUrl": "https://openagent3.xyz/skills/excel-xlsx/agent.json", "briefUrl": "https://openagent3.xyz/skills/excel-xlsx/agent.md" }, "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "When to Use", "body": "Use when the main artifact is a Microsoft Excel workbook or spreadsheet file, especially when formulas, dates, formatting, merged cells, workbook structure, or cross-platform behavior matter." }, { "title": "1. Choose the workflow by job, not by habit", "body": "Use pandas for analysis, reshaping, and CSV-like tasks.\nUse openpyxl when formulas, styles, sheets, comments, merged cells, or workbook preservation matter.\nTreat CSV as plain data exchange, not as an Excel feature-complete format.\nReading values, preserving a live workbook, and building a model from scratch are different spreadsheet jobs." }, { "title": "2. Dates are serial numbers with legacy quirks", "body": "Excel stores dates as serial numbers, not real date objects.\nThe 1900 date system includes the false leap-day bug, and some workbooks use the 1904 system.\nTime is fractional day data, so formatting and conversion both matter.\nDate correctness is not enough if the number format still displays the wrong thing to the user." }, { "title": "3. Keep calculations in Excel when the workbook should stay live", "body": "Write formulas into cells instead of hardcoding derived results from Python.\nUse references to assumption cells instead of magic numbers inside formulas.\nCached formula values can be stale, so do not trust them blindly after edits.\nCheck copied formulas for wrong ranges, wrong sheets, and silent off-by-one drift before delivery.\nAbsolute and relative references are part of the logic, so copied formulas can be wrong even when they still \"work\".\nTest new formulas on a few representative cells before filling them across a whole block.\nVerify denominators, named ranges, and precedent cells before shipping formulas that depend on them.\nA workbook should ship with zero formula errors, not with known #REF!, #DIV/0!, #VALUE!, #NAME?, or circular-reference fallout left for the user to fix.\nFor model-style work, document non-obvious hardcodes, assumptions, or source inputs in comments or nearby notes." }, { "title": "4. Protect data types before Excel mangles them", "body": "Long identifiers, phone numbers, ZIP codes, and leading-zero values should usually be stored as text.\nExcel silently truncates numeric precision past 15 digits.\nMixed text-number columns need explicit handling on read and on write.\nScientific notation, auto-parsed dates, and stripped leading zeros are common corruption, not cosmetic issues." }, { "title": "5. Preserve workbook structure before changing content", "body": "Existing templates override generic styling advice.\nOnly the top-left cell of a merged range stores the value.\nHidden rows, hidden columns, named ranges, and external references can still affect formulas and outputs.\nShared strings, defined names, and sheet-level conventions can matter even when the visible cells look simple.\nMatch styles for newly filled cells instead of quietly introducing a new visual system.\nIf the workbook is a template, preserve sheet order, widths, freezes, filters, print settings, validations, and visual conventions unless the task explicitly changes them.\nConditional formatting, filters, print areas, and data validation often carry business meaning even when users only mention the numbers.\nIf there is no existing style guide and the file is a model, keep editable inputs visually distinguishable from formulas, but never override an established template to force a generic house style." }, { "title": "6. Recalculate and review before delivery", "body": "Formula strings alone are not enough if the recipient needs current values.\nopenpyxl preserves formulas but does not calculate them.\nVerify no #REF!, #DIV/0!, #VALUE!, #NAME?, or circular-reference fallout remains.\nIf layout matters, render or visually review the workbook before calling it finished.\nBe careful with read modes: opening a workbook for values only and then saving can flatten formulas into static values.\nIf assumptions or hardcoded overrides must stay, make them obvious enough that the next editor can audit the workbook." }, { "title": "7. Scale the workflow to the file size", "body": "Large workbooks can fail for boring reasons: memory spikes, padded empty rows, and slow full-sheet reads.\nUse streaming or chunked reads when the file is big enough that loading everything at once becomes fragile.\nLarge-file workflows also need narrower reads, explicit dtypes, and sheet targeting to avoid accidental damage." }, { "title": "Common Traps", "body": "Type inference on read can leave numbers as text or convert IDs into damaged numeric values.\nColumn indexing varies across tools, so off-by-one mistakes are common in generated formulas.\nNewlines in cells need wrapping to display correctly.\nExternal references break easily when source files move.\nPassword protection in old Excel workflows is not serious security.\n.xlsm can contain macros, and .xls remains a tighter legacy format.\nLarge files may need streaming reads or more careful memory handling.\nGoogle Sheets and LibreOffice can reinterpret dates, formulas, or styling differently from Excel.\nDynamic array or newer Excel functions like FILTER, XLOOKUP, SORT, or SEQUENCE may fail or degrade in older viewers.\nA workbook can look fine while still carrying stale cached values from a prior recalculation.\nSaving the wrong workbook view can replace formulas with cached values and quietly destroy a live model.\nCopying formulas without checking relative references can push one bad range across an entire block.\nHidden sheets, named ranges, validations, and merged areas often keep business logic that is invisible in a quick skim.\nA workbook can appear numerically correct while still failing because filters, conditional formats, print settings, or data validation were stripped.\nA workbook can be numerically correct and still fail visually because wrapped text, clipped labels, or narrow columns were never reviewed." }, { "title": "Related Skills", "body": "Install with clawhub install if user confirms:\n\ncsv — Plain-text tabular import and export workflows.\ndata — General data handling patterns before spreadsheet output.\ndata-analysis — Higher-level analysis that can feed workbook deliverables." }, { "title": "Feedback", "body": "If useful: clawhub star excel-xlsx\nStay updated: clawhub sync" } ], "body": "When to Use\n\nUse when the main artifact is a Microsoft Excel workbook or spreadsheet file, especially when formulas, dates, formatting, merged cells, workbook structure, or cross-platform behavior matter.\n\nCore Rules\n1. Choose the workflow by job, not by habit\nUse pandas for analysis, reshaping, and CSV-like tasks.\nUse openpyxl when formulas, styles, sheets, comments, merged cells, or workbook preservation matter.\nTreat CSV as plain data exchange, not as an Excel feature-complete format.\nReading values, preserving a live workbook, and building a model from scratch are different spreadsheet jobs.\n2. Dates are serial numbers with legacy quirks\nExcel stores dates as serial numbers, not real date objects.\nThe 1900 date system includes the false leap-day bug, and some workbooks use the 1904 system.\nTime is fractional day data, so formatting and conversion both matter.\nDate correctness is not enough if the number format still displays the wrong thing to the user.\n3. Keep calculations in Excel when the workbook should stay live\nWrite formulas into cells instead of hardcoding derived results from Python.\nUse references to assumption cells instead of magic numbers inside formulas.\nCached formula values can be stale, so do not trust them blindly after edits.\nCheck copied formulas for wrong ranges, wrong sheets, and silent off-by-one drift before delivery.\nAbsolute and relative references are part of the logic, so copied formulas can be wrong even when they still \"work\".\nTest new formulas on a few representative cells before filling them across a whole block.\nVerify denominators, named ranges, and precedent cells before shipping formulas that depend on them.\nA workbook should ship with zero formula errors, not with known #REF!, #DIV/0!, #VALUE!, #NAME?, or circular-reference fallout left for the user to fix.\nFor model-style work, document non-obvious hardcodes, assumptions, or source inputs in comments or nearby notes.\n4. Protect data types before Excel mangles them\nLong identifiers, phone numbers, ZIP codes, and leading-zero values should usually be stored as text.\nExcel silently truncates numeric precision past 15 digits.\nMixed text-number columns need explicit handling on read and on write.\nScientific notation, auto-parsed dates, and stripped leading zeros are common corruption, not cosmetic issues.\n5. Preserve workbook structure before changing content\nExisting templates override generic styling advice.\nOnly the top-left cell of a merged range stores the value.\nHidden rows, hidden columns, named ranges, and external references can still affect formulas and outputs.\nShared strings, defined names, and sheet-level conventions can matter even when the visible cells look simple.\nMatch styles for newly filled cells instead of quietly introducing a new visual system.\nIf the workbook is a template, preserve sheet order, widths, freezes, filters, print settings, validations, and visual conventions unless the task explicitly changes them.\nConditional formatting, filters, print areas, and data validation often carry business meaning even when users only mention the numbers.\nIf there is no existing style guide and the file is a model, keep editable inputs visually distinguishable from formulas, but never override an established template to force a generic house style.\n6. Recalculate and review before delivery\nFormula strings alone are not enough if the recipient needs current values.\nopenpyxl preserves formulas but does not calculate them.\nVerify no #REF!, #DIV/0!, #VALUE!, #NAME?, or circular-reference fallout remains.\nIf layout matters, render or visually review the workbook before calling it finished.\nBe careful with read modes: opening a workbook for values only and then saving can flatten formulas into static values.\nIf assumptions or hardcoded overrides must stay, make them obvious enough that the next editor can audit the workbook.\n7. Scale the workflow to the file size\nLarge workbooks can fail for boring reasons: memory spikes, padded empty rows, and slow full-sheet reads.\nUse streaming or chunked reads when the file is big enough that loading everything at once becomes fragile.\nLarge-file workflows also need narrower reads, explicit dtypes, and sheet targeting to avoid accidental damage.\nCommon Traps\nType inference on read can leave numbers as text or convert IDs into damaged numeric values.\nColumn indexing varies across tools, so off-by-one mistakes are common in generated formulas.\nNewlines in cells need wrapping to display correctly.\nExternal references break easily when source files move.\nPassword protection in old Excel workflows is not serious security.\n.xlsm can contain macros, and .xls remains a tighter legacy format.\nLarge files may need streaming reads or more careful memory handling.\nGoogle Sheets and LibreOffice can reinterpret dates, formulas, or styling differently from Excel.\nDynamic array or newer Excel functions like FILTER, XLOOKUP, SORT, or SEQUENCE may fail or degrade in older viewers.\nA workbook can look fine while still carrying stale cached values from a prior recalculation.\nSaving the wrong workbook view can replace formulas with cached values and quietly destroy a live model.\nCopying formulas without checking relative references can push one bad range across an entire block.\nHidden sheets, named ranges, validations, and merged areas often keep business logic that is invisible in a quick skim.\nA workbook can appear numerically correct while still failing because filters, conditional formats, print settings, or data validation were stripped.\nA workbook can be numerically correct and still fail visually because wrapped text, clipped labels, or narrow columns were never reviewed.\nRelated Skills\n\nInstall with clawhub install if user confirms:\n\ncsv — Plain-text tabular import and export workflows.\ndata — General data handling patterns before spreadsheet output.\ndata-analysis — Higher-level analysis that can feed workbook deliverables.\nFeedback\nIf useful: clawhub star excel-xlsx\nStay updated: clawhub sync" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/ivangdavila/excel-xlsx", "publisherUrl": "https://clawhub.ai/ivangdavila/excel-xlsx", "owner": "ivangdavila", "version": "1.0.2", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/excel-xlsx", "downloadUrl": "https://openagent3.xyz/downloads/excel-xlsx", "agentUrl": "https://openagent3.xyz/skills/excel-xlsx/agent", "manifestUrl": "https://openagent3.xyz/skills/excel-xlsx/agent.json", "briefUrl": "https://openagent3.xyz/skills/excel-xlsx/agent.md" } }