SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill pdf-editinggit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/pdf-editing)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/pdf-editing"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/pdf-editing.svg" alt="Measured on agentmods" 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 3d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- pdf-editing — 100% identical, 0 lines differ
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.
- 3d ago First seen · 330 lines · 17 tokens per session scan A 7e0f4a10b794
pdf-editing is a skill published in the GitHub repository benchflow-ai/skillsbench (1,747 stars, last pushed 1mo ago), licensed Apache-2.0. 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
document-organization-pipeline
End-to-end pipeline for extracting, classifying, and organizing documents by subject.
pdf-calendar-parsing
Extract calendar events, blocks, and time slots from PDF calendar files using pdfplumber.
file-organizer-by-subject
Organize files into subject folders using keyword-based classification of titles and abstracts, with fallback to full text extraction.
pdf-form-filling
Fill PDF form fields programmatically using Python libraries like pypdf or pdfrw.
pdf-text-extraction
Extract text content from PDF files for analysis and classification.
pdf-calendar-extractor
Extract text and identifying colored regions (e.g., rectangles) from a PDF using pdfplumber.