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-csv-generationnpx skills add cxcscmu/SkillLearnBench --skill python-csv-generationgit 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/python-csv-generation)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/python-csv-generation"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/python-csv-generation.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 | $0.00022 | $0.00873 |
| Opus 5 | $0.00011 | $0.00436 |
| Sonnet 5 | $0.00004 | $0.00175 |
| Haiku 4.5 | $0.00002 | $0.00087 |
Grade A, and why
python-csv-generation 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 — 122 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python CSV Generation
Overview
The Python csv module provides functionality to read and write CSV (Comma-Separated Values) files. CSV is a standard format for tabular data that's widely compatible with spreadsheet applications.
Installation
Built-in to Python, no installation needed.
Basic Usage
Writing CSV with DictWriter (Recommended)
import csv
# Data to write
data = [
{'frame_id': '/root/keyframes_001.png', 'coins': 5, 'enemies': 2, 'turtles': 1},
{'frame_id': '/root/keyframes_002.png', 'coins': 3, 'enemies': 1, 'turtles': 0},
{'frame_id': '/root/keyframes_003.png', 'coins': 7, 'enemies': 3, 'turtles': 2},
]
# Write to CSV file
with open('output.csv', 'w', newline='') as csvfile:
fieldnames = ['frame_id', 'coins', 'enemies', 'turtles']
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
# Write header row
writer.writeheader()
# Write data rows
writer.writerows(data)
Writing CSV with writer (Simple approach)
import csv
with open('output.csv', 'w', newline='') as csvfile:
writer = csv.writer(csvfile)
# Write header
writer.writerow(['frame_id', 'coins', 'enemies', 'turtles'])
# Write data rows
writer.writerow(['/root/keyframes_001.png', 5, 2, 1])
writer.writerow(['/root/keyframes_002.png', 3, 1, 0])
Complete Example: Frame Analysis Results
import csv
import os
from pathlib import Path
def write_counting_results(results, output_path):
"""
Write object counting results to CSV.
Args:
results: List of dicts with keys:
'frame_id', 'coins', 'enemies', 'turtles'
output_path: Path to output CSV file
"""
fieldnames = ['frame_id', 'coins', 'enemies', 'turtles']
with open(output_path, 'w', newline='') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(results)
print(f"Results written to {output_path}")
# Example usage
results = [
{
'frame_id': '/root/keyframes_001.png',
'coins': 5,
'enemies': 2,
'turtles': 1
},
{
'frame_id': '/root/keyframes_002.png',
'coins': 3,
'enemies': 1,
'turtles': 0
}
]
write_counting_results(results, '/root/counting_results.csv')
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 · 122 lines · 22 tokens per session scan A bd5bcadf1160
python-csv-generation is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 873 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.
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