pdf-extraction

name	pdf-extraction
category	kumite-office
description	Extract text, tables, citations, forms, and metadata from PDFs as grounded context for downstream M365 Copilot work. Use whenever a source for research-synthesis, decision-memo, or document-summarization arrives as a PDF (contracts, regulatory filings, policy documents, vendor reports, scanned forms, fillable intake forms, academic papers), including encrypted, permission-restricted, hybrid native-plus-scan, or large multi-hundred-page packs. Triggers: "extract from this PDF", "summarize this filing", "pull table from PDF", "OCR this scan", "read the fillable form", "this PDF is password-protected".

PDF Extraction

PDFs are where evidence goes to die. Extract it cleanly before you synthesize, or you'll synthesize on top of OCR garbage.

Cowork skill. The deliverable is grounded context for downstream skills (synthesis, memo, summary), not the PDF itself. For programmatic PDF parsing, use the agents dojo.

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Quick Reference

Task	Approach
Triage a new PDF	Classify (native vs. scan); pull metadata; identify sections
Summarize a long filing	Section-by-section extraction -> synthesis, never one-shot summary
Pull a table	Request markdown / CSV; verify column alignment on a known row
Cite a clause	Quote verbatim with section reference, not page number
Multi-PDF research	Per-PDF metadata first -> cross-document synthesis matrix
Scanned doc	Verify OCR quality on three random sentences before trusting
Fillable form	Extract field values (not glyphs); pair each with its label; flag flattened forms
Encrypted / restricted	Note the restriction; request an unrestricted copy, don't OCR around copy-protection
Hybrid native + scan	Classify per section; OCR only the image pages; tag confidence per section

When to Use

• Any PDF that will become a source citation in a downstream deliverable.
• Multi-PDF research (regulatory filings, vendor responses, academic papers, due-diligence packs).
• Tables embedded in PDFs (financial statements, pricing schedules, org charts).
• Contracts where exact wording matters.

How to Use

1. Classify the PDF first. Native (selectable text) or scanned (image)? Try selecting a sentence — if it selects cleanly, it's native. Scanned PDFs need OCR; flag if quality is poor.
2. Pull metadata before content. Title, author, date, version, jurisdiction. Without these you cannot cite the source.
3. Extract by structure, not by page. Ask for sections / headings / clauses, not "page 3". Page numbers shift between versions.
4. Tables as tables. Request markdown or CSV — never paragraph form. Verify column alignment on a known row (first total, last row, a row with empty cells).
5. Quote, don't paraphrase, when the citation matters. Pull the verbatim sentence, then reference it in your synthesis.
6. Footnotes and endnotes — extract them with the body. They often hold the qualifications.
7. Hand off cleanly. The output of this skill is a structured context block (sections, quotes, tables) ready for research-synthesis, decision-memo, or document-summarization.

Hard Cases

Most PDFs extract cleanly with the steps above. These are the ones that quietly corrupt your evidence if you treat them like a plain native PDF.

Fillable forms (intake forms, applications, government PDFs). The thing you want is the field value, not the rendered glyphs sitting on top of the field. Extract values paired with their field labels (Applicant Name: Jordan Lee), not a free-floating list of strings. Two traps: a flattened form has been printed-to-PDF, so the values are now just text or an image and the label-to-value pairing is gone — treat it as native text or a scan and reconstruct pairs by position. A live form may also hold values you can't see on the page (cleared, off-page, or default-but-unsubmitted) — extract the visible answer and ignore stale defaults, and say so. Checkboxes and radio buttons read as state, not text: record Citizen: Yes / [X] Married, never the box character.

Encrypted or permission-restricted PDFs. If it asks for a password to open, you need the password — request it, don't guess. If it opens but blocks copy/extract (an owner-permission restriction), that is a signal, not a speed bump: do not OCR-screenshot around a copy restriction to launder protected content. Note the restriction in the context block and ask the owner for an extractable copy. Many "broken extraction" reports are really silent permission blocks.

Hybrid native + scanned. A native document with scanned exhibits stapled in (a typed contract with signed-signature-page images, a filing with photographed appendices) is the most-missed case. Classify per section, not per file: extract the native pages as text, OCR only the image pages, and tag confidence per section so downstream sees Body: verbatim next to Exhibit C: OCR-best-effort.

Rotated, skewed, or multi-orientation pages. Landscape tables dropped into a portrait filing, or a scan fed in sideways, will OCR into nonsense or reflow columns wrong. Confirm orientation before trusting any table from those pages.

Large packs (hundreds of pages). Don't one-shot a 400-page pack. Pull the metadata and the real section map first, then extract the sections the deliverable actually needs. A whole-document dump you can't verify is worse than three verified sections.

Examples

Don't: Anti-example	Do: Good example
"Summarize this PDF."	"Extract the executive summary, the risk-factors section, and Table 4 (revenue by segment) — preserve table structure."
Citing "page 7" of a 200-page filing.	Citing "Section 3.2 — Material Risks, paragraph 4."
Paraphrasing a contract clause for a memo.	Quoting the clause verbatim; paraphrasing only in the analysis around it.
Treating a scanned PDF as ground truth.	Verifying OCR on three random spots; flagging "OCR quality: medium" in the context block.
Dumping a fillable form's text as one flat list.	Pairing each value with its field label; recording checkbox state as [X] Married, not the box glyph.
OCR-screenshotting around a copy-restricted PDF.	Noting the permission restriction and requesting an extractable copy from the owner.

Critical Rules

• Native vs. scanned classification is step zero. Skip it and you risk citing OCR garbage as fact.
• Cite by structure (section / clause / table), never by page. Page numbers drift across PDF readers, exports, and versions.
• Quote verbatim when binding. Contracts, regulations, financial figures, named-entity references.
• Preserve tables as tables — markdown or CSV. Paragraph-ified tables lose the column relationships that matter.
• Tag confidence — "verbatim", "OCR-best-effort", "inferred-from-context". Downstream skills consume this tag.
• Sensitivity label travels with the content. A Confidential PDF stays Confidential when its content lands in a memo.

Common Pitfalls

• Treating an OCR'd scan as ground truth without spot-checking three random sentences.
• Re-flowing a multi-column PDF and not noticing the columns interleaved (very common with academic and legal docs).
• Losing footnotes / endnotes — that's often where the qualifications live.
• Tables that span pages reassembled with row offsets — totals end up off by one.
• Header/footer text (page number, doc title) interleaved into body text.
• Hyperlinks in the PDF lost during extraction.
• Encoding artifacts (fi ligature, smart quotes, em-dash) that break downstream search/find.
• Redacted regions that OCR happily "reads" as random characters.
• Flattened forms where the value-to-label pairing is already gone, read as a flat string list.
• A live form's stale default or off-page value extracted as if it were the submitted answer.
• A native filing with scanned exhibits classified once for the whole file, so the OCR'd exhibits get tagged verbatim.
• A permission-restricted PDF mistaken for a "broken" extraction instead of a deliberate copy block.

Anti-Patterns

• Pasting screenshots of PDF pages into chat instead of attaching the file.
• Asking Copilot to "summarize" before extraction — you can't verify what was summarized.
• Trusting an exec summary's numbers without cross-checking the body of the document.
• Using the PDF's auto-generated outline as the section structure when the author obviously hand-numbered sections differently.

Verify Before Hand-Off

• Source metadata captured (title, author, date, version, jurisdiction).
• Native / scanned classification recorded; OCR quality noted if scan. For hybrid files, classification + confidence recorded per section.
• Form fields extracted as label-value pairs; checkbox/radio state recorded as state; flattened-vs-live noted.
• Encryption / permission restriction noted; no copy-protection bypassed; extractable copy requested if blocked.
• Sections numbered as the author numbered them (not auto-generated).
• Tables preserved with correct column alignment; totals tie out.
• Verbatim quotes flagged; paraphrases flagged separately.
• Footnotes / endnotes extracted with the body.
• Sensitivity label propagated to the downstream artifact.
• Citation format ready for downstream skills.

Related

• research-synthesis, document-summarization, decision-memo
• cite-your-sources, protect-sensitive-info
• context-gathering