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 agentmods add skills/cxcscmu/skilllearnbench/python-docxnpx skills add cxcscmu/SkillLearnBench --skill python-docxgit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWhat 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 | $0.00023 | $0.00977 |
| Opus 5 | $0.00012 | $0.00489 |
| Sonnet 5 | $0.00005 | $0.00195 |
| Haiku 4.5 | $0.00002 | $0.00098 |
Grade A, and why
python-docx 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 2d 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python-docx: Working with Word Documents
Overview
The python-docx library allows you to create, read, and update Microsoft Word (.docx) files programmatically. It's essential for automating document generation, template filling, and Word document manipulation in Python.
Installation
pip install python-docx
Core Concepts
Document Structure
- Document: Root object representing the entire .docx file
- Paragraphs: Text blocks in the document
- Runs: Individual text segments within a paragraph (can have different formatting)
- Tables: Structured data with rows/cells
- Sections: Document sections with different headers/footers
Common Operations
Loading and Saving Documents
from docx import Document
# Open existing document
doc = Document('template.docx')
# Create new document
doc = Document()
# Save document
doc.save('output.docx')
Working with Paragraphs and Text
# Access all paragraphs
for para in doc.paragraphs:
print(para.text)
# Add new paragraph
new_para = doc.add_paragraph('Text here')
# Add text with formatting
run = new_para.add_run('Bold text')
run.bold = True
Finding and Replacing Text
# Search through all paragraphs and runs
for para in doc.paragraphs:
for run in para.runs:
if '{{PLACEHOLDER}}' in run.text:
run.text = run.text.replace('{{PLACEHOLDER}}', 'replacement')
# Note: Text can be split across multiple runs!
# A safer approach:
full_text = ''.join(run.text for run in para.runs)
if '{{PLACEHOLDER}}' in full_text:
# Clear all runs and recreate with replacement
for run in para.runs:
run.text = ''
para.text = full_text.replace('{{PLACEHOLDER}}', 'replacement')
Working with Tables
# Access table rows and cells
for table in doc.tables:
for row in table.rows:
for cell in row.cells:
print(cell.text)
# Replace text in cell
cell.text = cell.text.replace('{{PLACEHOLDER}}', 'value')
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.
- 2d ago First seen · 141 lines · 23 tokens per session scan A cc88272c4d6a
python-docx is a skill published in the GitHub repository cxcscmu/SkillLearnBench (80 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 977 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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