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 Raidriar7170/hermes-skilleval --skill geospatial-routing-datagit clone --depth 1 https://github.com/Raidriar7170/hermes-skillevalWrote 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/raidriar7170/hermes-skilleval/geospatial-routing-data)<a href="https://agentmods.dev/skills/raidriar7170/hermes-skilleval/geospatial-routing-data"><img src="https://agentmods.dev/badge/skills/raidriar7170/hermes-skilleval/geospatial-routing-data/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/raidriar7170/hermes-skilleval/geospatial-routing-data"><img src="https://agentmods.dev/badge/skills/raidriar7170/hermes-skilleval/geospatial-routing-data.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 | $0.00065 | $0.01416 |
| Opus 5 | $0.00032 | $0.00708 |
| Sonnet 5 | $0.00013 | $0.00283 |
| Haiku 4.5 | $0.00006 | $0.00142 |
Grade A, and why
geospatial-routing-data 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.
This is a copy
100% identical to geospatial-routing-data — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Geospatial Routing Data
Use this skill before building a routing model or validating a routing report that contains coordinates, depots, station IDs, and route sequences.
The main risk is mixing user-facing IDs with internal array indices or using a different distance metric from the task.
Parse Data Safely
Load structured data with a parser and build explicit mappings:
import json
from pathlib import Path
data = json.loads(Path("/root/data.json").read_text())
stations_data = data["stations"]
station_ids = [int(s["id"]) for s in stations_data]
if len(station_ids) != len(set(station_ids)):
raise ValueError("duplicate station ids")
id_to_idx = {sid: idx for idx, sid in enumerate(station_ids)}
idx_to_id = {idx: sid for sid, idx in id_to_idx.items()}
Use internal indices in optimization variables. Use original station IDs in final reports.
Coordinate Validation
Check coordinates before building distances:
def parse_location(record, label):
lat = float(record["latitude"])
lon = float(record["longitude"])
if not (-90.0 <= lat <= 90.0):
raise ValueError(f"{label} latitude out of range: {lat}")
if not (-180.0 <= lon <= 180.0):
raise ValueError(f"{label} longitude out of range: {lon}")
return {"latitude": lat, "longitude": lon}
depot = parse_location(data["depot"], "depot")
station_locations = [parse_location(s, f"station {s['id']}") for s in stations_data]
Latitude and longitude are degrees. Convert to radians only inside the distance function.
Great-Circle Distance
Match the task's declared distance metric. If the task specifies an Earth radius, use that exact value.
For great-circle miles with Earth radius 3960.0, use:
import math
def great_circle_miles(a, b, radius=3960.0):
lat1 = float(a["latitude"])
lon1 = float(a["longitude"])
lat2 = float(b["latitude"])
lon2 = float(b["longitude"])
deg_to_rad = math.pi / 180.0
phi1 = (90.0 - lat1) * deg_to_rad
phi2 = (90.0 - lat2) * deg_to_rad
theta1 = lon1 * deg_to_rad
theta2 = lon2 * deg_to_rad
cos_arc = (
math.sin(phi1) * math.sin(phi2) * math.cos(theta1 - theta2)
+ math.cos(phi1) * math.cos(phi2)
)
cos_arc = max(-1.0, min(1.0, cos_arc))
return math.acos(cos_arc) * radius
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 · 207 lines · 65 tokens per session scan A 0e278fce8dbf
geospatial-routing-data is a skill published in the GitHub repository Raidriar7170/hermes-skilleval (123 stars, last pushed 1mo ago), licensed MIT. It adds 65 tokens to every session and 1,416 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to geospatial-routing-data, differing in 0 lines, and is treated as a copy.
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