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 geojson-processinggit 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/geojson-processing)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/geojson-processing"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/geojson-processing.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 | $0.00371 |
| Opus 5 | $0.00011 | $0.00186 |
| Sonnet 5 | $0.00004 | $0.00074 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
geojson-processing 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.
What it actually says
GeoJSON Processing with GeoPandas
Loading GeoJSON Files
import geopandas as gpd
# Standard GeoJSON
gdf = gpd.read_file("file.geojson")
# USGS Earthquake GeoJSON specifics:
# - Coordinates are [longitude, latitude, depth]
# - Time is in milliseconds since epoch
# - Properties include: mag, place, time, type, id
USGS Earthquake Time Conversion
import pandas as pd
from datetime import datetime, timezone
# Convert epoch ms to ISO 8601
timestamp_ms = 1735537742808
dt = datetime.fromtimestamp(timestamp_ms / 1000, tz=timezone.utc)
iso_str = dt.strftime("%Y-%m-%dT%H:%M:%SZ")
Extracting Coordinates from GeoJSON Points
# After loading with GeoPandas
gdf["longitude"] = gdf.geometry.x
gdf["latitude"] = gdf.geometry.y
Writing Results to JSON
import json
result = {
"id": "us6000pgf9",
"place": "13 km NW of Port-Vila, Vanuatu",
"time": "2024-12-30T03:29:02Z",
"magnitude": 5.1,
"latitude": -17.6555,
"longitude": 168.2183,
"distance_km": 1234.56
}
with open("answer.json", "w") as f:
json.dump(result, f, indent=2)
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 · 60 lines · 22 tokens per session scan A 8086757217a8
geojson-processing 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 371 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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