Pdf logo

Pdf

OrganizationPopular
HKUDS
pdf

Read, extract (text/tables), create, merge/split/rotate, watermark, encrypt, fill, and render-to-image .pdf files. Use whenever the user uploads a .pdf or asks to produce, edit, or pull data out of one.

Overview

PublisherHKUDS
RepositoryDeepTutor
Skill namepdf
Stars
39.9K
Forks
5K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by HKUDS on GitHub. Read the source before you install it.

Installation

Install the Pdf AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/HKUDS/DeepTutor.git /tmp/DeepTutor
mkdir -p .claude/skills
cp -r /tmp/DeepTutor/deeptutor/skills/builtin/pdf .claude/skills/pdf
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Pdf in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Pdf on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Pdf is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

PDF

Work PDFs in the sandbox with preinstalled Python libs. Pick the library by task:

  • Extract text/tables/layout/word-coordinates → pdfplumber; quick raw text or page ops → pypdf.
  • Merge / split / rotate / crop / watermark / encrypt / metadatapypdf.
  • Fill formspypdf (fillable AcroForm fields) or annotation overlay (flat forms).
  • Create from scratchreportlab.

Use exec with complete Python source (language: python). Prefer creating, reopening, and validating the PDF in one call; later calls can revise the same relative filename. Follow the turn's User workspace instructions for locating inputs, output boundaries, and presenting the finished file. Preserve an explicitly requested quantity (such as 500 words) and verify the count in the output before finishing. If execution fails or the artifact is missing, diagnose stderr/root cause and change strategy; do not retry identical code or reduce the requested scope without asking.

Extract text and tables (pdfplumber)

python
import pdfplumber

with pdfplumber.open("in.pdf") as pdf:
    for i, page in enumerate(pdf.pages, 1):
        print(f"--- page {i} ---")
        print(page.extract_text() or "")  # layout-aware text
        for t in page.extract_tables():  # list of tables; each is list[row]
            for row in t:
                print(row)

Tables → Excel (one worksheet per table):

python
import pdfplumber
from openpyxl import Workbook

workbook = Workbook()
workbook.remove(workbook.active)
table_number = 0
with pdfplumber.open("in.pdf") as pdf:
    for page_number, page in enumerate(pdf.pages, 1):
        for table in page.extract_tables():
            if not table:
                continue
            table_number += 1
            sheet = workbook.create_sheet(f"p{page_number}_table{table_number}"[:31])
            for row in table:
                sheet.append([cell or "" for cell in row])
if table_number:
    workbook.save("tables.xlsx")

Messy tables: pass strategies, or crop a region with page.within_bbox((x0, top, x1, bottom)) first:

python
ts = {
    "vertical_strategy": "lines",
    "horizontal_strategy": "lines",
    "snap_tolerance": 3,
    "intersection_tolerance": 15,
}
page.extract_tables(ts)

For very large PDFs where you only need raw text, pypdf's page.extract_text() is lighter.

Scanned / image-only PDFs (be honest)

If extract_text() returns empty or garbage (e.g. (cid:NN) runs) the page is scanned. No OCR engine (tesseract) is installed and network is off, so you cannot recover that text. Say so plainly and stop — do not fabricate content or attempt pip install.

Merge / split / rotate / crop / metadata (pypdf)

python
from pypdf import PdfReader, PdfWriter

# Merge
w = PdfWriter()
for f in ["a.pdf", "b.pdf"]:
    for p in PdfReader(f).pages:
        w.add_page(p)
w.write("merged.pdf")

# Split: one file per page
r = PdfReader("in.pdf")
for i, p in enumerate(r.pages, 1):
    w = PdfWriter()
    w.add_page(p)
    w.write(f"page_{i}.pdf")

# Rotate page 0 by 90 degrees clockwise
r = PdfReader("in.pdf")
w = PdfWriter()
r.pages[0].rotate(90)
w.add_page(r.pages[0])
w.write("rotated.pdf")
  • Metadata: PdfReader("in.pdf").metadata (.title, .author, ...).
  • Crop: set page.mediabox.left/bottom/right/top (points, origin y=0 at bottom).
  • Encrypt: w = PdfWriter(clone_from=PdfReader("in.pdf")); w.encrypt("userpw", "ownerpw"); w.write("enc.pdf").
  • Decrypt: r = PdfReader("enc.pdf"); r.decrypt("pw") if r.is_encrypted, then read/copy pages.

Watermark (stamp one page over every page):

python
from pypdf import PdfReader, PdfWriter

wm = PdfReader("stamp.pdf").pages[0]
r = PdfReader("in.pdf")
w = PdfWriter()
for p in r.pages:
    p.merge_page(wm)
    w.add_page(p)
w.write("stamped.pdf")

Create PDFs (reportlab)

Flowing document (preferred for text/reports/tables — handles pagination):

python
from reportlab.lib.pagesizes import letter
from reportlab.lib.styles import getSampleStyleSheet
from reportlab.lib import colors
from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer, Table, TableStyle

styles = getSampleStyleSheet()
story = [
    Paragraph("Report Title", styles["Title"]),
    Spacer(1, 12),
    Paragraph("Body text. " * 20, styles["Normal"]),
]
data = [["Product", "Q1", "Q2"], ["Widgets", "120", "135"]]
tbl = Table(data)
tbl.setStyle(
    TableStyle(
        [
            ("BACKGROUND", (0, 0), (-1, 0), colors.grey),
            ("TEXTCOLOR", (0, 0), (-1, 0), colors.whitesmoke),
            ("GRID", (0, 0), (-1, -1), 0.5, colors.black),
        ]
    )
)
story += [Spacer(1, 12), tbl]
SimpleDocTemplate("out.pdf", pagesize=letter).build(story)

Absolute placement (labels at fixed coordinates): use canvas.Canvas("out.pdf", pagesize=letter), c.drawString(x, y, "...") (origin bottom-left, points), c.showPage() per page, c.save().

Non-Latin text (Chinese / Japanese / Korean, Cyrillic, …)

reportlab's built-in fonts (Helvetica/Times/Courier) carry zero CJK glyphs, so any 中文/日本語/한국어 renders as empty boxes (□) baked permanently into the PDF. reportlab never auto-discovers system fonts — you MUST register a font that has the glyphs and set it on every style. Whenever the document may contain non-Latin text, register a CJK font first (it also covers Latin, so it is safe to use as the only font):

python
import os
from reportlab.pdfbase import pdfmetrics
from reportlab.pdfbase.ttfonts import TTFont


def register_cjk_font(name="CJK"):
    # TrueType ONLY — reportlab cannot embed CFF/OpenType outlines, so a .otf
    # like Noto Sans CJK fails with "postscript outlines are not supported".
    for path in [
        "/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc",  # Linux sandbox (fonts-wqy-zenhei)
        "/usr/share/fonts/truetype/wqy/wqy-microhei.ttc",
        "/System/Library/Fonts/STHeiti Light.ttc",  # macOS
        "/System/Library/Fonts/Hiragino Sans GB.ttc",
        "/System/Library/Fonts/Supplemental/Songti.ttc",
        "/System/Library/Fonts/Supplemental/Arial Unicode.ttf",
        "C:/Windows/Fonts/msyh.ttc",  # Windows
    ]:
        if os.path.exists(path):
            try:
                pdfmetrics.registerFont(TTFont(name, path, subfontIndex=0))
                return name
            except Exception:
                continue
    raise RuntimeError("No CJK-capable TrueType font found — do not emit tofu; say so.")


font = register_cjk_font()
styles = getSampleStyleSheet()
for s in styles.byName.values():  # make the CJK font the default everywhere
    s.fontName = font
# Tables don't read the stylesheet — set the font in the TableStyle too:
#   ("FONTNAME", (0, 0), (-1, -1), font)
# Canvas: c.setFont(font, size) before every drawString.

If register_cjk_font raises (no font on the host), do not ship a tofu PDF — tell the user the sandbox lacks a CJK font instead of producing garbage.

Gotcha: even with a good font, reportlab still needs markup for subscripts/superscripts. In Paragraph use Paragraph("H<sub>2</sub>O", styles["Normal"]), x<super>2</super>.

Markdown/HTML → PDF needs an external converter (soffice/pandoc) that is usually absent — command -v soffice / command -v pandoc and degrade to building the PDF directly with reportlab if neither is present.

Fill forms (pypdf)

First detect whether the PDF has real fillable (AcroForm) fields:

python
from pypdf import PdfReader

fields = PdfReader("form.pdf").get_fields()
print("fillable" if fields else "flat (no fields)")

Fillable — inspect field names/types, then fill and write:

python
from pypdf import PdfReader, PdfWriter

r = PdfReader("form.pdf")
for name, f in r.get_fields().items():
    print(name, f.get("/FT"), f.get("/_States_"))  # /Tx text, /Btn checkbox/radio, /Ch choice

w = PdfWriter(clone_from=r)
values = {"first_name": "Bart", "agree": "/Yes"}  # checkbox/radio: use its on-state, NOT True/False
for page in w.pages:
    w.update_page_form_field_values(page, values, auto_regenerate=False)
w.set_need_appearances_writer(True)  # force viewers to render the values
w.write("filled.pdf")

Checkbox/radio values are on-state strings, not booleans — read the field's /_States_ (e.g. /Yes, /On); /Off clears it.

Flat form (no fields) — overlay text with FreeText annotations at PDF coordinates. Get real coordinates from the layout with pdfplumber instead of guessing:

python
import pdfplumber

with pdfplumber.open("form.pdf") as pdf:
    pg = pdf.pages[0]
    for wd in pg.extract_words():  # each has x0, top, x1, bottom (TOP-left origin!)
        print(wd["text"], wd["x0"], wd["top"])
    for rc in pg.rects:  # small squares are likely checkboxes
        print("rect", rc["x0"], rc["top"], rc["x1"], rc["bottom"])

pdfplumber top is measured from the page top; pypdf rects are bottom-left, so convert: pdf_y = page_height - top. Place text just right of the matching label:

python
from pypdf import PdfReader, PdfWriter
from pypdf.annotations import FreeText

r = PdfReader("form.pdf")
w = PdfWriter()
w.append(r)
h = float(r.pages[0].mediabox.height)
top = 700  # pdfplumber 'top' of the label's row
w.add_annotation(
    page_number=0,
    annotation=FreeText(
        text="Smith",
        rect=(255, h - top - 14, 720, h - top),  # (x0, y0, x1, y1)
        font="Helvetica",
        font_size="10pt",
        font_color="000000",
        border_color=None,
        background_color=None,
    ),
)
w.write("filled.pdf")

Verify: re-open the output and re-read get_fields() values (fillable) or re-extract text (overlay) to confirm the values landed.

Page → image rendering (PyMuPDF)

PyMuPDF (imported as fitz, preinstalled) rasterizes pages — useful to inspect a PDF visually or to hand a page to an image-capable step. No external tools needed (poppler / pdf2image are absent; don't reach for them).

python
import fitz  # PyMuPDF

doc = fitz.open("in.pdf")
for i, page in enumerate(doc, 1):
    page.get_pixmap(dpi=150).save(f"page_{i}.png")  # higher dpi = sharper + larger

fitz also extracts text (page.get_text()) and can render a sub-region via page.get_pixmap(clip=fitz.Rect(x0, y0, x1, y1)). It does not OCR — a rendered scanned page is still just pixels (see Scanned PDFs above).

Frequently asked questions

What does the Pdf AI skill do?

Read, extract (text/tables), create, merge/split/rotate, watermark, encrypt, fill, and render-to-image .pdf files. Use whenever the user uploads a .pdf or asks to produce, edit, or pull data out of one.

Why use Pdf on TypingMind?

Because you install it once and use it with any model. Pdf is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Pdf in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/HKUDS/DeepTutor/tree/main/deeptutor/skills/builtin/pdf. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Pdf?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Pdf?

As many as you like. As long as a model supports skills, you can use Pdf with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Pdf AI skill free?

Yes. It is published on GitHub by HKUDS under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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