Yes, Claude Opus 4.5 works well as the “brain” for spreadsheet and Excel automation, especially when paired with a small amount of glue code or an automation framework. The model is strong at understanding tabular structures, reasoning about formulas, and planning multi-step edits. You can send it CSV extracts, table-like text, or descriptions of a workbook (“Sheet1 has sales by region, Sheet2 has targets”) and ask it to propose formulas, pivot-table designs, or cell-level edits. With its large context window, Opus 4.5 can keep multiple sheets or large tables in working memory during a single request, which is important for real spreadsheets rather than toy examples.
In practice, the most robust pattern is tool-orchestrated automation, not “just text in, text out.” For example, your agent might (1) export a sheet as CSV or use an API like Microsoft Graph or the Excel JavaScript API; (2) send the relevant structure and user intent to Claude Opus 4.5; (3) receive back a plan plus code (e.g., Office Scripts, Python with openpyxl, or a list of cell-level operations); and (4) execute those changes programmatically. This keeps Claude focused on planning, formula design, and consistency checks, while the execution layer actually manipulates the workbook. It also allows you to validate the plan (lint formulas, run tests, confirm data ranges) before applying it.
For more advanced setups, you can combine spreadsheet automation with retrieval from a vector database such as Milvus or Zilliz Cloud. For example, you might store embeddings of common templates, KPI definitions, or domain-specific formula patterns. When a user asks, “Build me the same cohort analysis sheet we used for product X but for this new dataset,” your orchestrator can retrieve the relevant template description from Milvus/Zilliz Cloud and feed it into Opus 4.5 alongside the new data. This makes spreadsheet automation feel more like reusing institutional knowledge rather than reinventing each workbook from scratch.