geospatial-routing-data

geospatial-routing-data is a skill for Claude Code, Codex from benchflow-ai/skillsbench. It costs 65 tokens per session (1,416 once invoked), scanned A, original, Apache-2.0.

A guide for handling map locations and routes, including depots, stations, latitude and longitude, and distances between places.

In plain words
What is it for?
Preparing routing data, building distance tables, reconstructing route lengths, and checking optimization or route reports.
Why use it?
It prevents mix-ups between public station IDs and the internal numbers used by optimization programs, and catches invalid coordinates or inconsistent distance calculations.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Preparing routing data, building distance tables, reconstructing route lengths, and checking optimization or route reports.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/benchflow-ai/skillsbench/geospatial-routing-data
About the project

SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.

benchflow-ai/skillsbench · 1,748 stars · on GitHub · skillsbench.ai

Install

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.

Any agent
npx skills add benchflow-ai/skillsbench --skill geospatial-routing-data
Clone the repo
git clone --depth 1 https://github.com/benchflow-ai/skillsbench

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for geospatial-routing-data

README.md
[![agentmods](https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/geospatial-routing-data.svg)](https://agentmods.dev/skills/benchflow-ai/skillsbench/geospatial-routing-data)
Your own site
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/geospatial-routing-data"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/geospatial-routing-data.svg" alt="Measured on agentmods" height="20"></a>
Per session 65 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,416 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash 0e278fce8dbf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

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 8d 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.

Origin

Copies of this mod

3 near-identical copies found in the catalogue:

tasks/bike-rebalance/environment/skills/geospatial-routing-data/SKILL.md · 207 lines

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

Read the full file on GitHub · 207 lines

Changes

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.

  1. 8d ago First seen · 207 lines · 65 tokens per session scan A 0e278fce8dbf

Subscribe to this mod's changes

geospatial-routing-data is a skill published in the GitHub repository benchflow-ai/skillsbench (1,748 stars, last pushed 1mo ago), licensed Apache-2.0. 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.