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 cxcscmu/SkillLearnBench --skill pdf-calendar-parsinggit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/pdf-calendar-parsing)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/pdf-calendar-parsing"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/pdf-calendar-parsing/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/cxcscmu/skilllearnbench/pdf-calendar-parsing"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/pdf-calendar-parsing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00022 | $0.00964 |
| Opus 5 | $0.00011 | $0.00482 |
| Sonnet 5 | $0.00004 | $0.00193 |
| Haiku 4.5 | $0.00002 | $0.00096 |
Grade A, and why
pdf-calendar-parsing 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.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PDF Calendar Parsing Skill
Overview
This skill covers extracting calendar events and time blocks from PDF calendar documents using pdfplumber, a Python library for extracting text and tables from PDF files.
Installation
pip install pdfplumber
Key Concepts
How pdfplumber Works
- Opens PDF files and extracts text with position information
- Can identify text bounding boxes (x0, y0, x1, y1 coordinates)
- Supports extracting tables and structured data
Calendar Grid Extraction
For a calendar with hourly/time-based layout:
- Extract all text from the PDF with position data
- Identify time labels (hours on the left axis)
- Identify event blocks by text content and bounding boxes
- Calculate event duration by comparing Y-coordinates
Code Examples
Basic PDF Opening
import pdfplumber
with pdfplumber.open('/root/calendar.pdf') as pdf:
page = pdf.pages[0]
text = page.extract_text()
print(text)
Extracting Text with Positions
import pdfplumber
with pdfplumber.open('/root/calendar.pdf') as pdf:
page = pdf.pages[0]
# Get all text objects with their positions
for char in page.chars:
print(f"Text: {char['text']}, X: {char['x0']}, Y: {char['y0']}")
Identifying Calendar Events
import pdfplumber
def extract_calendar_events(pdf_path):
with pdfplumber.open(pdf_path) as pdf:
page = pdf.pages[0]
# Get all text with positions
text_data = page.extract_text_with_layout()
# Extract words and their bounding boxes
words = page.extract_words()
events = []
for word in words:
# word contains: 'text', 'x0', 'y0', 'x1', 'y1', 'size', 'font'
if word['text'] not in ['12am', '1am', '2am']: # Skip time labels
events.append({
'text': word['text'],
'x0': word['x0'],
'y0': word['y0'],
'x1': word['x1'],
'y1': word['y1']
})
return events
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 · 134 lines · 22 tokens per session scan A 9759125e1bd8
pdf-calendar-parsing is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 2mo ago), licensed MIT. It adds 22 tokens to every session and 964 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 2026-08-30.
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