Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add xuansenpa1/skillrevise --skill pdf-editinggit clone --depth 1 https://github.com/xuansenpa1/skillreviseWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/xuansenpa1/skillrevise/pdf-editing)<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/pdf-editing"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/pdf-editing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/pdf-editing"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/pdf-editing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00017 | $0.02777 |
| Opus 5 | $0.00009 | $0.01388 |
| Sonnet 5 | $0.00003 | $0.00555 |
| Haiku 4.5 | $0.00002 | $0.00278 |
Grade A, and why
pdf-editing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
100% identical to pdf-editing — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Editing Skill
CRITICAL RULES - READ FIRST
NEVER DO THESE:
- NEVER use strikethrough lines to cross out text
- NEVER rasterize or flatten the PDF to images
- NEVER convert PDF pages to PNG/JPG and draw on them
- NEVER use pdf-to-image-to-pdf workflows
- NEVER use add_redact_annot() with BLACK fill (use WHITE fill instead)
- NEVER add text NEXT TO old values - REPLACE them at the SAME position
TWO APPROACHES - CHOOSE THE RIGHT ONE:
-
For REPLACING text (e.g., updating name, email, DOB):
- Use
draw_rect()with white fill to cover old text - Use
insert_text()at the SAME position - Text layer is preserved
- Use
-
For TRUE REDACTION of sensitive data (e.g., student ID):
- Use
add_redact_annot(rect, fill=(1,1,1))with WHITE fill - Call
apply_redactions()to REMOVE text from PDF structure - Then
insert_text()to add masked value (e.g., "****5678") - Original text is completely removed, not just covered
- Use
Overview
USE PYTHON WITH PyMuPDF (fitz) - it is pre-installed and produces the best results.
PyMuPDF preserves the text layer properly, making text extractable after editing. JavaScript libraries like pdf-lib may create text that tools like pypdf cannot extract.
# PyMuPDF is already installed - just use it
python3 -c "import fitz; print('PyMuPDF ready')"
Reading PDF Content
import fitz
doc = fitz.open("input.pdf")
page = doc[0]
# Extract all text to understand the document
text = page.get_text()
print(text)
Finding Text Positions
# search_for() returns list of rectangles where text is found
rects = page.search_for("Label Text")
if rects:
rect = rects[0]
# rect.x0, rect.y0 = top-left corner
# rect.x1, rect.y1 = bottom-right corner
print(f"Found at: ({rect.x0}, {rect.y0}) to ({rect.x1}, {rect.y1})")
Inserting Text
# Insert text at a specific position
page.insert_text(
(x_position, y_position), # coordinates
"text to insert",
fontsize=11,
color=(0, 0, 0) # black
)
doc.save("output.pdf")
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 330 lines · 17 tokens per session scan A 7e0f4a10b794
pdf-editing is a skill published in the GitHub repository xuansenpa1/skillrevise (55 stars, last pushed 4d ago), licensed MIT. It adds 17 tokens to every session and 2,777 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to pdf-editing, differing in 0 lines, and is treated as a copy.
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