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Aidrift now mirrors Anthropic skills into a dedicated catalog so agents, builders, and operators can browse reusable instructions, inspect source metadata, and jump into related tools, MCP servers, and news.

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Agent skills, installation notes, and related Aidrift graph

Each skill is parsed from the upstream repository and positioned as a reusable agent workflow object inside Aidrift.

Skills become more useful when they connect to execution surfaces. Use the MCP directory when you need runtime capabilities, the AI tools catalog when you need a ready-made product, and the AI news feed when you want to track new launches and ecosystem shifts.

xlsx

Skill
| Task | Approach | |---|---| | **Create** or **edit** with formulas/formatting | `openpyxl` — see gotchas below | | **Bulk data** in or out | `pandas` (`read_excel`, `to_excel`) | | **Quick look** at a sheet | `markitdown file.xlsx` — `## SheetName` per sheet; reads `.xlsm` too. No cell coordinates, so don't plan edits from it | | **Read** a model (formulas *and* values) | two `load_workbook` passes — see gotchas |

skills/xlsx

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like \"the xlsx in my downloads\") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

Use cases

| Task | Approach | |---|---| | **Create** or **edit** with formulas/formatting | `openpyxl` — see gotchas below | | **Bulk data** in or out | `pandas` (`read_excel`, `to_excel`) | | **Quick look** at a sheet | `markitdown file.xlsx` — `## SheetName` per sheet; reads `.xlsm` too. No cell coordinates, so don't plan edits from it | | **Read** a model (formulas *and* values) | two `load_workbook` passes — see gotchas |
- **Professional font** (Arial, Times New Roman) throughout, unless the user says otherwise. - **Zero formula errors.** Never ship while `recalc.py` reports `errors_found`. If you think an error predates you, prove it: load the *original* with `data_only=True` and look at that cell. An error you introduced looks exactly like one you inherited. - **Use formulas, never hardcoded results.** Write `sheet['B10'] = '=SUM(B2:B9)'`, not the Python-computed total. The sheet must recalculate when its inputs change. - **Follow the user's spec literally.** Exact tab names, exact column headers, and the formula they spelled out. A redesign that computes something else fails, however elegant. - **Document every assumption and hardcoded number** where the reader will see it — a cell comment, or an adjacent cell at a table's end. Cite a real source when one exists (`Source: Company 10-K, FY2024, Page 45, Revenue Note, [SEC EDGAR URL]`); when the number came from the user, say so plainly. - **A workbook *you create* for someone to fill in** needs a short legend naming which cells to edit, and one example row of realistic values showing the expected format. Never add such a row to a file you were asked to edit. - **Editing an existing file: match its conventions exactly.** They override every guideline here. Find its designated input cells first — a distinct font color, fill, or shading marks them — write only there, and leave every existing formula untouched.
#csv#data#api#automation+4
Updated 16 days ago
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