transbigdata-metroline

transbigdata-metroline is a skill for Claude Code from ni1o1/claude-skill-transbigdata. It costs 50 tokens per session (1,602 once invoked), scanned A, original, MIT.

A Python guide for modelling bus and subway networks as connected maps. It works with route and stop location data to analyse journeys and transfers.

In plain words
What is it for?
Use it to find shortest or alternative routes, estimate journey times, split subway lines into sections, and identify bus arrivals and departures from GPS traces.
Why use it?
It removes the need to build transport-network calculations from scratch when working with route, station, or bus GPS data.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the transbigdata plugin — 9 skills shipped together

Good fit Use it to find shortest or alternative routes, estimate journey times, split subway lines into sections, and identify bus arrivals and departures from GPS traces.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/ni1o1/claude-skill-transbigdata/transbigdata-metroline
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 ni1o1/claude-skill-transbigdata --skill transbigdata-metroline
Clone the repo
git clone --depth 1 https://github.com/ni1o1/claude-skill-transbigdata

Made for: Claude Code.

Or install transbigdata, the plugin that ships this one along with the rest of its 9 skills.

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 transbigdata-metroline

README.md
[![agentmods](https://agentmods.dev/badge/skills/ni1o1/claude-skill-transbigdata/transbigdata-metroline/github.svg)](https://agentmods.dev/skills/ni1o1/claude-skill-transbigdata/transbigdata-metroline)
Your own site
<a href="https://agentmods.dev/skills/ni1o1/claude-skill-transbigdata/transbigdata-metroline"><img src="https://agentmods.dev/badge/skills/ni1o1/claude-skill-transbigdata/transbigdata-metroline/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 transbigdata-metroline

Your own site · 80×15
<a href="https://agentmods.dev/skills/ni1o1/claude-skill-transbigdata/transbigdata-metroline"><img src="https://agentmods.dev/badge/skills/ni1o1/claude-skill-transbigdata/transbigdata-metroline.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,602 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 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 $0.00050 $0.01602
Opus 5 $0.00025 $0.00801
Sonnet 5 $0.00010 $0.00320
Haiku 4.5 $0.00005 $0.00160

Measured 12d ago against content hash c60df7066b86, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

transbigdata-metroline 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 12d 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.

skills/transbigdata-metroline/SKILL.md · 199 lines

How it starts

The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.

TransBigData 公交地铁网络指南

安装

pip install transbigdata networkx geopandas

地铁网络建模

1. 构建网络 - metro_network()

import transbigdata as tbd
import geopandas as gpd

# 加载线路和站点数据
line = gpd.read_file('metro_lines.shp')  # 需包含 linename, speed, stoptime
stop = gpd.read_file('metro_stops.shp')

# 构建网络图
G = tbd.metro_network(
    line,
    stop,
    transfertime=5  # 换乘时间(分钟)
)

数据要求:

  • line: 包含 linename(线路名)、speed(运行速度)、stoptime(停站时间)
  • stop: 站点位置数据

2. 最短路径查询 - get_shortest_path()

path = tbd.get_shortest_path(
    G,
    stop,
    ostation='福田',    # 起点站名
    dstation='罗湖'     # 终点站名
)
# 返回: 站点名列表

3. K 条最短路径 - get_k_shortest_paths()

paths = tbd.get_k_shortest_paths(
    G,
    stop,
    ostation='福田',
    dstation='罗湖',
    k=3  # 前3条最短路径
)

4. 路径耗时计算 - get_path_traveltime()

travel_time = tbd.get_path_traveltime(G, path)  # 返回分钟

5. 线路分割 - split_subwayline()

将地铁线路按站点分割为线段。

line_segments = tbd.split_subwayline(line, stop)

公交 GPS 数据处理

6. 到站信息识别 - busgps_arriveinfo()

从公交 GPS 轨迹识别到站/离站时间。

arrive_info = tbd.busgps_arriveinfo(
    bus_gps_data,
    line,                    # 公交线路 GeoDataFrame
    stop,                    # 公交站点 GeoDataFrame
    col=['VehicleNum', 'GPSTime', 'Lng', 'Lat'],
    stopbuffer=200,          # 站点缓冲区半径(米)
    mintime=300              # 最小停留时间(秒)
)

7. 单程时间计算 - busgps_onewaytime()

oneway_time = tbd.busgps_onewaytime(
    arrive_info,
    start='起点站',
    end='终点站',
    col=['VehicleNum', 'StopName', 'ArriveTime', 'LeaveTime']
)

完整示例:地铁网络分析

import pandas as pd
import geopandas as gpd
import transbigdata as tbd
from shapely.geometry import Point

# 1. 准备数据
# 线路数据
line_data = gpd.GeoDataFrame({
    'linename': ['1号线', '1号线', '2号线', '2号线'],
    'speed': [60, 60, 55, 55],  # km/h
    'stoptime': [0.5, 0.5, 0.5, 0.5]  # 分钟
})

# 站点数据
stop_data = gpd.GeoDataFrame({
    'stopname': ['A站', 'B站', 'C站', 'D站'],
    'linename': ['1号线', '1号线,2号线', '2号线', '1号线'],
    'geometry': [Point(114.0, 22.5), Point(114.05, 22.52),
                 Point(114.1, 22.55), Point(114.08, 22.48)]
}, crs='EPSG:4326')

# 2. 构建网络
G = tbd.metro_network(line_data, stop_data, transfertime=5)

# 3. 查询最短路径
path = tbd.get_shortest_path(G, stop_data, ostation='A站', dstation='C站')
print(f"最短路径: {' → '.join(path)}")

# 4. 计算耗时
time = tbd.get_path_traveltime(G, path)
print(f"预计耗时: {time:.1f} 分钟")

# 5. 查询多条路径
paths = tbd.get_k_shortest_paths(G, stop_data, ostation='A站', dstation='C站', k=3)
for i, p in enumerate(paths, 1):
    t = tbd.get_path_traveltime(G, p)
    print(f"方案{i}: {' → '.join(p)},耗时 {t:.1f} 分钟")

Read the full file on GitHub · 199 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. 12d ago First seen · 199 lines · 50 tokens per session scan A c60df7066b86

Subscribe to this mod's changes

transbigdata-metroline is a skill published in the GitHub repository ni1o1/claude-skill-transbigdata (4 stars, last pushed 7mo ago), licensed MIT. It adds 50 tokens to every session and 1,602 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 2026-08-31.

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