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 ladla90077-web/solidworks-mcp --skill pdfgit clone --depth 1 https://github.com/ladla90077-web/solidworks-mcpWrote 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/ladla90077-web/solidworks-mcp/pdf)<a href="https://agentmods.dev/skills/ladla90077-web/solidworks-mcp/pdf"><img src="https://agentmods.dev/badge/skills/ladla90077-web/solidworks-mcp/pdf/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/ladla90077-web/solidworks-mcp/pdf"><img src="https://agentmods.dev/badge/skills/ladla90077-web/solidworks-mcp/pdf.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 | $0.00136 | $0.02134 |
| Opus 5 | $0.00068 | $0.01067 |
| Sonnet 5 | $0.00027 | $0.00427 |
| Haiku 4.5 | $0.00014 | $0.00213 |
Grade A, and why
PDF scanned grade A with 1 finding 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run([soffice, "--headless", "--convert-to", "pdf", How it starts
The opening of the file, as written. The whole thing — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Working with PDFs (.pdf)
You read and build PDFs by writing Python code, not by filling in a fixed
schema. The docgen tool's run_python runs your code in the app's bundled
interpreter, which has these libraries pre-installed:
fitz(PyMuPDF) — the one tool for almost everything: extract text and tables, merge/split/reorder/rotate/delete pages, watermark, extract images, create new PDFs, render pages to PNG, and OCR scanned pages.PIL(Pillow) — image ops (resize, convert, compose) for images you pull out of or drop into a PDF.pptx,docx,openpyxl,pandas— if the source or target drifts to Office formats. To turn one of those into a PDF, export via LibreOffice (see the "Office → PDF" note below).
run_python is real execution with full filesystem access. print(...) is
captured and returned to you. Iterate: write code → run → inspect → fix.
There is no pypdf / reportlab / pdfplumber in the bundle — don't import
them. PyMuPDF covers all of those use cases; use fitz.
Reading / extracting text
To dump text, use the files read tool on the .pdf, or write a script:
import fitz
doc = fitz.open("/path/to/document.pdf")
for i, page in enumerate(doc, 1):
print(f"## Page {i}")
print(page.get_text())
page.get_text("text") gives plain reading order; "words" / "dict" /
"blocks" give positions when you need layout. For a scanned (image-only) PDF
get_text() returns empty — see OCR below.
Extracting tables
import fitz
doc = fitz.open("/path/to/document.pdf")
for i, page in enumerate(doc, 1):
for t, table in enumerate(page.find_tables().tables, 1):
print(f"## Page {i} table {t}")
for row in table.extract():
print(row)
# table.to_pandas() gives a DataFrame if you want to write .csv/.xlsx
Merge / split / reorder / delete pages
import fitz
# Merge several PDFs into one
out = fitz.open()
for path in ["a.pdf", "b.pdf", "c.pdf"]:
with fitz.open(path) as src:
out.insert_pdf(src)
out.save("/abs/path/merged.pdf")
# Split: one file per page
src = fitz.open("input.pdf")
for i in range(src.page_count):
one = fitz.open()
one.insert_pdf(src, from_page=i, to_page=i)
one.save(f"/abs/path/page_{i+1}.pdf")
# Reorder / subset: select() takes the new page order (0-based)
src.select([2, 0, 1]) # keep+reorder these pages
src.delete_page(0) # or delete_pages(from_page=, to_page=)
src.save("/abs/path/reordered.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 · 222 lines · 136 tokens per session scan A 25bdd19ddff1
PDF is a skill published in the GitHub repository ladla90077-web/solidworks-mcp (3 stars, last pushed 2mo ago), licensed MIT. It adds 136 tokens to every session and 2,134 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
pydicom
Use pydicom to read, inspect, write, transform, and safely preflight local DICOM datasets and pixel data. Applies to DICOM metadata, transfer syntaxes, compression plugins, frames, private elements, JSON, and bounded de-identification review.
foundry-hosted-agent-validation
Step-by-step process for validating a Python Foundry hosted agent sample (under python/samples/04-hosting/foundry-hosted-agents/) end to end — running it locally (native runtime and azd ai agent run) and after deploying it to an Azure AI Foundry project with azd. Use this when asked to validate a hosted agent sample.
skill-doc-delivery
Convert markdown to DOCX, PPTX, XLSX, PDF office documents — use when you need exportable deliverables.
pdf-extract-create-workflow
Complete PDF lifecycle: download, extract, and generate structured documents with reportlab.
document-direct-python
Use direct Python execution for reliable document creation including spreadsheets, PDFs, and structured reports.
parse-document
Convert a PDF, scan, image of a page, or office file to clean markdown through the connected Superlinked MCP edge, so the source document is not read into model context directly. Use when the user asks to read, parse, OCR, extract from, summarize, or answer questions about a document.