PDF files: create, read, merge, fill, OCR, edit text. An agent skill from NousResearch/hermes-agent.

MITAuto-check passedDocuments & Office

Install PDF

skills CLI
$ npx skills add NousResearch/hermes-agent --skill pdf -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install NousResearch/hermes-agent pdf --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/NousResearch/hermes-agent.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/productivity/pdf .claude/skills/pdf && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
pdf
GitHub stars
252k
Token cost
~3.1k tokens
SKILL.md length
1,283 words
Files
22 (incl. scripts, references)
Skills in repo
29
Repo updated
First seen
Licence
MIT

At a glance

PDF files: create, read, merge, fill, OCR, edit text. An agent skill from NousResearch/hermes-agent.

  • Works in 9 steps: Inspect first. Run pdf_read.py file.pdf… → Create. Write a JSON spec with… → Extract. --text gives a JSON list of… → …
  • Tasks that involve PDF
  • SKILL.md covers When to Use, Prerequisites, How to Run and Quick Reference, plus 3 more sections
  • Runs Python scripts from its folder; calls python

What it does

PDF is an agent skill from NousResearch/hermes-agent. PDF files: create, read, merge, fill, OCR, edit text.

Its SKILL.md is about 3.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 23 other files, including scripts and reference files (for example `references/forms.md`, `references/nano-pdf-editing.md` and `references/ocr-extraction.md`).

It sits in Documents & Office, covering PDF. It works with pypdf and Python. The repository describes itself as: The agent that grows with you. The licence is MIT.

When your agent uses it

  • Tasks that involve PDF

Example prompts

  • “/pdf”

Requirements

  • Python 3

Workflow steps

9 steps, taken from the first numbered list in SKILL.md.

  1. Inspect first. Run pdf_read.py file.pdf --meta. Check encrypted (if true, decrypt first with pdf_secure.py --decrypt) and…
  2. Create. Write a JSON spec with write_file (elements: heading, paragraph, table, image, pagebreak; optional title/author metadata; page…
  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…
  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-)…
  5. Build forms. Write one form-spec JSON (fields with label_box/entry_box in PDF points — see references/forms.md), lint it with…
  6. Fill forms. List fields (--fields) to learn exact names and types, write a UTF-8 JSON of {"FieldName": "value"} with write_file…
  7. Metadata & attachments. pdf_meta.py --set-meta writes Title/Author/Subject/Keywords (DocInfo); --clear-meta drops them…
  8. Secure. Encrypt with distinct user/owner passwords and AES-256. To remove a password you know, --decrypt writes an unencrypted copy.
  9. Verify (see below) before reporting success.

What it can do on your machine

Read from SKILL.md and the folder at commit 0e37a43. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 14 files in scripts/ (Python, from the files we listed), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

PDF loads about 3.1k tokens when it runs, and up to ~5.8k if it reads all its reference files. Until then it costs about 14 tokens; SKILL.md has 1,283 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~14
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~5.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from NousResearch/hermes-agent at commit 0e37a43, republished under its MIT licence (© NousResearch). 1,283 words, ~3,114 tokens.

Download SKILL.mdSave it as .claude/skills/pdf/SKILL.md (or your agent's skills folder). This skill also uses 21 other files; get the full folder from GitHub.
name
pdf
description
PDF files: create, read, merge, fill, OCR, edit text.
version
1.1.0
author
Nous Research
license
MIT
platforms
linux, macos, windows

PDF Skill

Create PDFs from structured specs, build and fill AcroForm forms (with layout linting and visual overlays), extract text/tables/metadata, merge/split/rotate/watermark/stamp pages, export page images, manage metadata and attachments, and encrypt/decrypt — using pypdf, reportlab, and pdfplumber. Two absorbed capabilities live in references/ (read the matching file before those tasks):

  • Scanned/image-only PDFs and OCR (pymupdf fast path, marker-pdf quality path, scripts/extract_pymupdf.py + scripts/extract_marker.py): references/ocr-extraction.md
  • Editing text inside an existing PDF via natural-language prompts (nano-pdf CLI): references/nano-pdf-editing.md

When to Use

  • Generate a report, invoice, or multi-page document as PDF.
  • Build a fillable AcroForm (text/checkbox/radio/dropdown) from a JSON spec, linting the layout first.
  • Pull text, tables (JSON/CSV), metadata, or form-field values out of a PDF.
  • Merge, split, rotate, extract page subsets, watermark, stamp text/images at coordinates, bookmark, or compress PDFs.
  • Export pages as PNGs for visual review or for OCR hand-off; set/clear document metadata; add/extract file attachments.
  • Fill or flatten AcroForm forms; encrypt or decrypt with passwords.
  • NOT for scanned/image-only PDFs (use references/ocr-extraction.md) and NOT for pixel-perfect HTML-to-PDF rendering (use a headless browser).

Prerequisites

  • Python 3.10+ with pypdf, reportlab, pdfplumber: python -m pip install pypdf reportlab pdfplumber
  • Optional, for page rasterization (pdf_page_image.py, overlay rendering): python -m pip install pypdfium2, or poppler's pdftoppm on PATH. Scripts fall back pypdfium2 → pdftoppm and report {"rendered": false, "missing": [...]} (exit 0) when neither exists.
  • 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.

bash
python scripts/pdf_create.py spec.json -o out.pdf         # build PDF from JSON spec
python scripts/pdf_make_form.py formspec.json -o form.pdf # build fillable AcroForm from JSON spec
python scripts/pdf_form_layout.py formspec.json           # lint form layout BEFORE building
python scripts/pdf_form_layout.py formspec.json --render-overlay boxes.png [--pdf form.pdf]
python scripts/pdf_read.py doc.pdf --text                 # per-page text (JSON)
python scripts/pdf_read.py doc.pdf --tables --csv-dir t/  # tables to JSON + CSV files
python scripts/pdf_read.py doc.pdf --meta                 # metadata, page sizes, encrypted/scanned flags
python scripts/pdf_read.py form.pdf --fields              # form fields: name, type, value
python scripts/pdf_merge.py a.pdf b.pdf -o merged.pdf [--bookmarks]
python scripts/pdf_split.py doc.pdf --pages 1-3,7 -o part.pdf [--rotate 90]
python scripts/pdf_fill_form.py form.pdf --fields-json values.json -o filled.pdf [--flatten]
python scripts/pdf_secure.py doc.pdf --encrypt -o enc.pdf --user-password your-password
python scripts/pdf_secure.py enc.pdf --decrypt -o dec.pdf --password your-password
python scripts/pdf_watermark.py doc.pdf --stamp mark.pdf -o stamped.pdf [--under]
python scripts/pdf_stamp.py doc.pdf -o out.pdf --text "DRAFT" --x 150 --y 400 \
    --font-size 60 --rotation 45 --opacity 0.3 --color "#cc0000" [--pages 1-3]
python scripts/pdf_stamp.py doc.pdf -o out.pdf --image sig.png --x 400 --y 60 --width 120
python scripts/pdf_page_image.py doc.pdf --pages 1-3 --dpi 150 --out-dir imgs/
python scripts/pdf_meta.py doc.pdf --set-meta --title "T" --author "A" -o out.pdf
python scripts/pdf_meta.py doc.pdf --attach data.csv -o out.pdf
python scripts/pdf_meta.py doc.pdf --list-attachments | --extract-attachments dir/

Quick Reference

TaskToolCommand / API
Create doc (headings, tables, images)reportlab platypuspdf_create.py spec.json -o out.pdf
Build fillable formreportlab acroFormpdf_make_form.py formspec.json -o form.pdf
Lint form layout / overlay imagepure python + PILpdf_form_layout.py formspec.json [--render-overlay o.png]
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 / stamp PDF pagepypdfpdf_watermark.py f.pdf --stamp w.pdf
Stamp text/image at coordinatesreportlab + pypdfpdf_stamp.py f.pdf --text "Sign here" --x 400 --y 60
Pages → PNG (review / OCR hand-off)pypdfium2 or pdftoppmpdf_page_image.py f.pdf --pages 1-3 --out-dir imgs/
Set/clear metadata, attachmentspypdfpdf_meta.py --set-meta / --attach / --extract-attachments
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, export them with pdf_page_image.py --pages <scanned> --dpi 300 --out-dir imgs/ and hand the PNGs to the references/ocr-extraction.md 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; for one-liner stamps ("sign here", diagonal DRAFT, corner labels) use pdf_stamp.py with text or an image at explicit coordinates.
  5. Build forms. Write one form-spec JSON (fields with label_box/entry_box in PDF points — see references/forms.md), lint it with pdf_form_layout.py and fix every reported problem, optionally review the --render-overlay PNG with vision_analyze, then build with pdf_make_form.py and confirm with pdf_read.py --fields.
  6. Fill 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.
  7. Metadata & attachments. pdf_meta.py --set-meta writes Title/Author/Subject/Keywords (DocInfo); --clear-meta drops them; --attach/--list-attachments/--extract-attachments round-trip embedded files.
  8. Secure. Encrypt with distinct user/owner passwords and AES-256. To remove a password you know, --decrypt writes an unencrypted copy.
  9. Verify (see below) before reporting success.
Show full SKILL.md (568 more words)Show less

Pitfalls

  • Scanned PDFs: empty extract_text() plus page images means there is no text layer. Route to references/ocr-extraction.md; 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.
  • Rotated stamp text extraction: pdfplumber's line grouping scrambles rotated glyphs (a 45° "DRAFT" extracts as stray letters); verify rotated stamps with pypdf's extract_text() or a rendered image instead.
  • Radio groups: reportlab needs ≥2 radio() widgets per group, fills need the slashed export value ("/red"), and flatten fidelity is worst for radios — see references/forms.md.
  • Metadata scope: pdf_meta.py writes the classic DocInfo dictionary only; embedded XMP metadata (if any) is left untouched and may show different values in some viewers.
  • PDF/A is out of scope: pypdf/reportlab cannot produce or validate conformant PDF/A. If archival conformance is required, run Ghostscript via the terminal tool (e.g. gs -dPDFA=2 -dPDFACompatibilityPolicy=1 -sColorConversionStrategy=UseDeviceIndependentColor -sDEVICE=pdfwrite -o out.pdf in.pdf with a suitable ICC profile) and validate with veraPDF — both are external installs, and the result still needs validation, not assumption.
  • 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.
  • Form design loop: pdf_form_layout.py spec.json must exit 0; then --render-overlay boxes.png --pdf form.pdf and review the PNG with vision_analyze (red = entry boxes with field names, blue = label boxes) asking about overlaps, misalignment, and labels detached from their fields. Iterate spec → lint → overlay until clean.
  • After building a form: pdf_read.py form.pdf --fields lists every spec field with the right type and options.
  • After form fill: pdf_read.py filled.pdf --fields and compare values (exact match, including non-ASCII).
  • After stamping: re-extract text (pypdf for rotated stamps) or render the page with pdf_page_image.py and inspect with vision_analyze.
  • After metadata/attachment edits: pdf_read.py --meta / pdf_meta.py --list-attachments, and re-extract an attachment to byte-compare.
  • 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.

© NousResearch, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 21 other files (scripts, references) in skills/productivity/pdf of NousResearch/hermes-agent.

  • SKILL.md
  • LICENSE
  • references/forms.md
  • references/nano-pdf-editing.md
  • references/ocr-extraction.md
  • scripts/_raster.py
  • scripts/extract_marker.py
  • scripts/extract_pymupdf.py
  • scripts/pdf_create.py
  • scripts/pdf_fill_form.py
  • scripts/pdf_form_layout.py
  • scripts/pdf_make_form.py
  • scripts/pdf_merge.py
  • scripts/pdf_meta.py
  • scripts/pdf_page_image.py
  • scripts/pdf_read.py
  • scripts/pdf_secure.py
  • scripts/pdf_split.py
  • scripts/pdf_stamp.py
  • … and 3 more

Open the folder on GitHubat commit 0e37a43

Compare with similar skills

PDF next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

PDF compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
PDF this skillNousResearch/hermes-agent252k—~3.1kAutomated safety check: PassMIT
PDF ToolkitXiaomiMiMo/MiMo-Code14k—~1.7kAutomated safety check: PassApache-2.0
PDF Generation, Forms and Extractionpipeshub-ai/pipeshub-ai3.8k—~2.9kAutomated safety check: PassApache-2.0
PDF ExploreHughYau/AcademicForge2.6k—~3.1kAutomated safety check: PassApache-2.0
PDF Readerespennilsen/pi122—~1.6kAutomated safety check: PassMIT
PDF ExploreJimLiu/science-skills2272 repos~3.6kAutomated safety check: PassApache-2.0

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Works with

Questions about PDF

What does PDF do?

PDF files: create, read, merge, fill, OCR, edit text. An agent skill from NousResearch/hermes-agent. PDF is an agent skill from NousResearch/hermes-agent. PDF files: create, read, merge, fill, OCR, edit text.

When should I use PDF?

PDF fits situations like: tasks that involve PDF.

How do I install PDF in Claude Code?

Run `npx skills add NousResearch/hermes-agent --skill pdf -a claude-code`. Or copy the skill folder (skills/productivity/pdf in NousResearch/hermes-agent) into .claude/skills/pdf in your project. Claude Code loads it when a task matches its description.

How do I install PDF in Codex?

Run `npx skills add NousResearch/hermes-agent --skill pdf -a codex`. Or copy the skill folder (skills/productivity/pdf in NousResearch/hermes-agent) into .agents/skills/pdf in your project. Codex loads it when a task matches its description.

Can I use PDF in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NousResearch/hermes-agent --skill pdf -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/pdf, .gemini/skills/pdf, .github/skills/pdf and .opencode/skills/pdf in your project.

What does PDF need to run?

Going by SKILL.md and its folder, PDF needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does PDF access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is PDF safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does PDF use?

PDF is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does PDF use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.7k tokens, read only when the agent opens those files.

What are the alternatives to PDF?

Skills that share tags, products or a category with PDF: PDF Toolkit (XiaomiMiMo/MiMo-Code, 14k stars), PDF Generation, Forms and Extraction (pipeshub-ai/pipeshub-ai, 3.8k stars), PDF Explore (HughYau/AcademicForge, 2.6k stars) and PDF Reader (espennilsen/pi, 122 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains PDF?

NousResearch (a GitHub organization) maintains it in NousResearch/hermes-agent, which has 251,739 GitHub stars. The repository holds 29 skills in this directory. The repository was last updated on October 7, 2026.

Source: NousResearch/hermes-agent on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.