geospatial-routing-data

geospatial-routing-data is a skill for Claude Code, Codex from xuansenpa1/skillrevise. It costs 65 tokens per session (1,416 once invoked), scanned A, a copy of geospatial-routing-data, MIT.

A guide for handling geographic routing data such as latitude and longitude, depot and station IDs, route sequences, and distance matrices. A distance matrix is a table showing the distance between each pair of locations.

In plain words
What is it for?
Preparing location data for route optimization, validating coordinates and unique IDs, mapping external IDs to internal indexes, calculating distances, reconstructing routes, and checking routing reports.
Why use it?
It helps prevent common routing errors, such as confusing public location IDs with internal array positions or using invalid coordinates or the wrong distance method.

Skill for Claude CodeCodex

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

Good fit Preparing location data for route optimization, validating coordinates and unique IDs, mapping external IDs to internal indexes, calculating distances, reconstructing routes, and checking routing reports.

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Install with agentmods
npx agentmods add skills/xuansenpa1/skillrevise/geospatial-routing-data
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 xuansenpa1/skillrevise --skill geospatial-routing-data
Clone the repo
git clone --depth 1 https://github.com/xuansenpa1/skillrevise

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/xuansenpa1/skillrevise/geospatial-routing-data/github.svg)](https://agentmods.dev/skills/xuansenpa1/skillrevise/geospatial-routing-data)
Your own site
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/geospatial-routing-data"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/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.

agentmods 80×15 button for geospatial-routing-data

Your own site · 80×15
<a href="https://agentmods.dev/skills/xuansenpa1/skillrevise/geospatial-routing-data"><img src="https://agentmods.dev/badge/skills/xuansenpa1/skillrevise/geospatial-routing-data.svg" alt="Reviewed on agentmods" width="80" 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.
Origin 100% copy Near-identical to another mod 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 9d ago against content hash 0e278fce8dbf, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 9d 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

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.

data/skillsbench/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. 9d 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 xuansenpa1/skillrevise (55 stars, last pushed 4d 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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