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Pdf

Create, read, merge, fill, and secure PDF files.

Skill metadata​

SourceBundled (installed by default)
Pathskills/productivity/pdf
Version1.0.0
AuthorNoora
LicenseMIT
Platformslinux, macos, windows
Tagspdf, documents, forms, reportlab, pypdf, pdfplumber
Related skillsdocx, xlsx, powerpoint, ocr-and-documents

Reference: full SKILL.md​

info

The following is the complete skill definition that Noora loads when this skill is triggered. This is what the agent sees as instructions when the skill is active.

PDF Skill

Create PDFs from structured specs, extract text/tables/metadata, merge/split/rotate/watermark pages, fill AcroForm form fields, and encrypt/decrypt — using pypdf, reportlab, and pdfplumber. Scanned (image-only) PDFs contain no text layer: OCR is explicitly out of scope here — when a page is image-only, stop and use the ocr-and-documents skill instead of pretending to extract text.

When to Use​

  • Generate a report, invoice, or multi-page document as PDF.
  • Pull text, tables (JSON/CSV), metadata, or form-field values out of a PDF.
  • Merge, split, rotate, extract page subsets, watermark, bookmark, or compress PDFs.
  • Fill or flatten AcroForm forms; encrypt or decrypt with passwords.
  • NOT for scanned/image-only PDFs (use ocr-and-documents) and NOT for pixel-perfect HTML-to-PDF rendering (use a headless browser).

Prerequisites​

  • Python 3.10+ with pypdf, reportlab, pdfplumber: python3 -m pip install pypdf reportlab pdfplumber
  • Each helper script checks imports lazily and prints an install hint if a dependency is missing.

How to Run​

All helpers live in scripts/ and are argparse CLIs — run them with the terminal tool; every one supports --help. They read/write JSON strictly as UTF-8, print JSON results to stdout, and exit non-zero on failure.

python3 scripts/pdf_create.py spec.json -o out.pdf         # build PDF from JSON spec
python3 scripts/pdf_read.py doc.pdf --text                 # per-page text (JSON)
python3 scripts/pdf_read.py doc.pdf --tables --csv-dir t/  # tables to JSON + CSV files
python3 scripts/pdf_read.py doc.pdf --meta                 # metadata, page sizes, encrypted/scanned flags
python3 scripts/pdf_read.py form.pdf --fields              # form fields: name, type, value
python3 scripts/pdf_merge.py a.pdf b.pdf -o merged.pdf [--bookmarks]
python3 scripts/pdf_split.py doc.pdf --pages 1-3,7 -o part.pdf [--rotate 90]
python3 scripts/pdf_fill_form.py form.pdf --fields-json values.json -o filled.pdf [--flatten]
python3 scripts/pdf_secure.py doc.pdf --encrypt -o enc.pdf --user-password your-password
python3 scripts/pdf_secure.py enc.pdf --decrypt -o dec.pdf --password your-password
python3 scripts/pdf_watermark.py doc.pdf --stamp mark.pdf -o stamped.pdf [--under]

Quick Reference​

TaskToolCommand / API
Create doc (headings, tables, images)reportlab platypuspdf_create.py spec.json -o out.pdf
Per-page textpdfplumberpdf_read.py f.pdf --text
Tables → JSON/CSVpdfplumberpdf_read.py f.pdf --tables
Metadata / sizes / encrypted / scannedpypdf + pdfplumberpdf_read.py f.pdf --meta
Merge (+ outline)pypdfpdf_merge.py a.pdf b.pdf -o m.pdf
Split / extract / rotatepypdfpdf_split.py f.pdf --pages 2-5 --rotate 90
List / fill / flatten formpypdfpdf_read.py --fields, pdf_fill_form.py
Encrypt / decrypt (AES-256)pypdfpdf_secure.py --encrypt/--decrypt
Watermark / stamppypdfpdf_watermark.py f.pdf --stamp w.pdf
Compress content streamspypdfpdf_split.py f.pdf --pages 1-N --compress

Procedure​

  1. Inspect first. Run pdf_read.py file.pdf --meta. Check encrypted (if true, decrypt first with pdf_secure.py --decrypt) and likely_scanned_pages. If pages are image-only, hand off to the ocr-and-documents skill — do not report empty text as "no content".
  2. Create. Write a JSON spec with write_file (elements: heading, paragraph, table, image, pagebreak; optional title/author metadata; page numbers are added automatically), then run pdf_create.py. Verify visually with vision_analyze on a rendered page image if layout matters.
  3. Extract. --text gives a JSON list of per-page strings; --tables gives row arrays per page and can also emit CSV files. Read results with read_file; never eyeball a binary PDF directly.
  4. Manipulate. pdf_merge.py concatenates and can add one bookmark per source file; pdf_split.py handles page ranges (1-based, e.g. 1-3,5,9-), rotation in 90° steps, and --compress. Watermark by preparing a single-page stamp PDF (e.g. via pdf_create.py) and overlaying it with pdf_watermark.py.
  5. Forms. List fields (--fields) to learn exact names and types, write a UTF-8 JSON of {"FieldName": "value"} with write_file (checkboxes accept true/false; radio/choice values must match the field's export options), then pdf_fill_form.py. Re-read with --fields to confirm values landed.
  6. Secure. Encrypt with distinct user/owner passwords and AES-256. To remove a password you know, --decrypt writes an unencrypted copy.
  7. Verify (see below) before reporting success.

Pitfalls​

  • Scanned PDFs: empty extract_text() plus page images means there is no text layer. Route to ocr-and-documents; do not fabricate text.
  • Flattening limits: pdf_fill_form.py --flatten uses pypdf's flatten support, which converts widget appearances into page content. It is reliable for plain text fields and checkboxes but can drop or misrender exotic widgets (rich text, custom appearance streams, some radio groups). Verify the flattened output visually with vision_analyze; for bulletproof flattening use an external renderer (e.g. Ghostscript or pdftoppm+reassembly) as a fallback.
  • NeedAppearances: after filling, viewers only render values if appearance streams exist. The fill script sets the AcroForm NeedAppearances flag so conforming viewers regenerate them; some minimal viewers ignore it — flatten if display fidelity matters.
  • Non-Latin form values: values are stored correctly (UTF-16), but the field's default font may lack glyphs, so a viewer can show blanks even though the data round-trips. Verify with --fields, not just visually.
  • Compression expectations: --compress only deflates content streams. Typical savings are 0–20%; it does nothing for PDFs dominated by images or already-compressed streams. It is not a substitute for image downsampling (Ghostscript territory).
  • Permission flags don't enforce: owner-password permission bits (no-print, no-copy) are polite requests that viewers may honor; any library (including pypdf) can read and strip them. Only the user password actually gates content via encryption. Never present permission flags as security.
  • Table extraction is heuristic: pdfplumber detects tables from ruling lines/word alignment; borderless or merged-cell tables may need table_settings tuning or manual cleanup.
  • Page indexing: helper CLIs take 1-based pages; pypdf APIs are 0-based. The scripts convert — don't double-convert.
  • Rotation must be a multiple of 90; encrypted inputs must be decrypted before any other operation.

Verification​

  • After create/merge/split: pdf_read.py out.pdf --meta — confirm page_count, and per-page rotation when you rotated.
  • After extraction: check the JSON is non-empty and spot-check a known string or cell.
  • After form fill: pdf_read.py filled.pdf --fields and compare values (exact match, including non-ASCII).
  • After encrypt: --meta shows "encrypted": true and opening without a password fails; after decrypt, text extraction matches the original.
  • For anything visual (watermarks, flattened forms), render and inspect with vision_analyze.