Turn tables locked inside a PDF into a clean, nicely formatted spreadsheet the user can open in Excel or feed to another tool. The user is asking for the data as a file, not a prose answer — so your job is to locate the source PDF, get its full content (not just retrieved chunks), extract the relevant table(s), and hand back a formatted workbook.

When this applies (and when it does not)

• Applies — the user wants the tabular data itself as a file: “pull out / extract / export / convert / get me a spreadsheet of …”, “put the rebate schedule in Excel”, “give me the line items from this contract”.
• Applies to follow-ups too — even if you already answered or summarized the data in the chat, a subsequent “give me that as a file / spreadsheet / Excel” is still an extraction request. Invoke this skill and rebuild the file from the source document — do not assemble a spreadsheet from the text already in the conversation, which may be partial, truncated, or reformatted.
• Does not apply — the user is asking a question to be answered in the conversation: “what are the rebates for Contoso?”, “how much is the Q3 discount?”. Answer those inline from knowledge within reason; do not run an extraction or produce a file.

If a request is ambiguous (e.g. “show me the rebates”), prefer a short inline answer and offer to export it as a file if they want the full table.

Instructions

1. Identify the target document. Let the user ask naturally — they need not name an exact file. Use the cues in their request (customer/contract name, topic, document title, or a file they referenced) to search knowledge for the best-matching PDF. If one document is a clear match, proceed with it. Only when the match is genuinely ambiguous — several plausible documents, or none obvious — ask the user a brief question to confirm which one they mean.
2. Get the FULL document, not chunks. Retrieved knowledge chunks are enough to answer a question but not to extract a whole table — rows are routinely split across chunks — so work from the complete PDF in the agent’s container. A SharePoint knowledge source handles this naturally: let it search and pull the full file down. If the maker collects files another way (direct upload, a connector, a different store), use whatever mechanism is available to get the full PDF locally. The rest of these steps are the same once the file is local.
3. Extract the table(s) — script first. Run the bundled scripts/extract_tables.py against the downloaded PDF to pull tabular data out deterministically (see Bundled files). This is the default path because it lifts the grid verbatim — no dropped or “tidied” rows — which matters most for long schedules. It writes a formatted .xlsx workbook by default, with one tab per detected table (so multiple tables and page splits stay cleanly separated). Narrow to the relevant table when the user named a specific one (e.g. rebates, pricing, line items) using the --contains filter; otherwise extract all detected tables.
4. Fall back to reading it yourself when the script can’t cope. pdfplumber relies on a text layer and clean rules, so it under-performs on scanned / image-only PDFs (no text to extract) and borderless or merged-cell tables (misaligned output). When the script returns nothing, or the output is clearly garbled/misaligned versus the PDF, read the table directly from the document and build the workbook yourself following the cleaning rules below. Use your judgement to pick the right table when a keyword filter is too blunt.
5. Clean the output. Ensure a single header row per table, trim stray whitespace, drop fully empty rows (but keep empty columns — they preserve the table’s structure and alignment), and keep numbers/currency/dates as they appear in the source (do not invent, reformat, or “correct” values). If cells were merged or a header spans multiple rows, flatten to one clear header row. Put each distinct table on its own sheet/tab; if one logical table is split across pages, you may merge the parts into a single sheet.
6. Respond with a recap, then the file. Open your reply with a short, natural summary of what the user asked for and what you pulled — restate the request in your own words (e.g. “Here’s the full rebate schedule from the Contoso contract you asked for”) — then attach the full extracted data as a formatted .xlsx workbook. The complete data lives in the file, not the chat: don’t paste the whole table inline or truncate it. When multiple tables were extracted, give each its own clearly named tab and briefly say what each contains. Then offer a .csv as an alternative (“Let me know if this works, or if you’d prefer a CSV instead”) — produce it with --format csv (or both) if they say yes, one CSV per table.
7. Confirm and flag gaps. Give a one-line summary (which document, which table(s)/tab(s), rows × columns) and note whether the script or a manual read produced it. Call out anything uncertain — a table that spanned pages, cells that failed to parse, or a table the tool could not detect — so the user can verify rather than trust silently.

Bundled files

The attached .zip includes:

• scripts/extract_tables.py — extracts tables from a local PDF using pdfplumber (available natively in the agent container). Run it as python scripts/extract_tables.py <document.pdf> --out-dir out. By default it writes a formatted .xlsx workbook — one tab per table, with a bold frozen header row, an autofilter, and sized columns. Useful flags:
  • --format xlsx|csv|both — output format (default xlsx; csv writes one CSV per table; both writes the workbook and the CSVs),
  • --pages 2-5 (or --pages 3) to limit to specific pages,
  • --contains rebate to keep only tables whose text contains a keyword,
  • --out-dir out to choose where the files are written. It prints a JSON summary of everything produced (workbook path, per-table sheet names, any CSV paths, source page, row/column counts) so you can report back accurately. If it produces no tables or clearly mangles them (scanned or borderless PDFs), fall back to reading the tables yourself per step 4.

Guardrails

• Never fabricate rows, values, or headers. If a cell is unreadable, leave it empty and flag it rather than guessing.
• Do not answer extraction requests from retrieved chunks alone — always work from the full downloaded document so tables aren’t truncated.
• Never build the file from the chat. When the user asks for a file after you’ve already answered or summarized in conversation, still run this skill and extract from the source document — do not hand-assemble a spreadsheet from the text in the chat, which may be partial or reformatted.
• Do not turn a plain question into a file dump; only produce a file when the user actually wants the data as a file.
• Do not expand scope beyond what was asked (don’t export every table when the user asked only for the rebates).

Tone

Precise and practical. Prefer surfacing uncertainty over confidently returning a table that may be incomplete.