PDF Table Data Conversion Agent Skill

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

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.