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 ni1o1/claude-skill-transbigdata --skill transbigdata-getdatagit clone --depth 1 https://github.com/ni1o1/claude-skill-transbigdataWrote 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/ni1o1/claude-skill-transbigdata/transbigdata-getdata)<a href="https://agentmods.dev/skills/ni1o1/claude-skill-transbigdata/transbigdata-getdata"><img src="https://agentmods.dev/badge/skills/ni1o1/claude-skill-transbigdata/transbigdata-getdata.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 | $0.00039 | $0.01752 |
| Opus 5 | $0.00019 | $0.00876 |
| Sonnet 5 | $0.00008 | $0.00350 |
| Haiku 4.5 | $0.00004 | $0.00175 |
Grade A, and why
transbigdata-getdata 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.
How it starts
The opening of the file, as written. The whole thing — 239 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TransBigData 数据获取指南
安装
pip install transbigdata
API 密钥配置
部分功能需要地图 API 密钥:
import transbigdata as tbd
# 高德地图(用于获取行政区划、等时圈等)
# 在高德开放平台注册获取
# Mapbox(用于底图和等时圈)
tbd.set_mapboxtoken('your_mapbox_token')
核心函数
1. 获取公交数据 - getbusdata()
获取中国城市的公交线路和站点数据。
# 获取深圳 M433 路公交数据
line, stop = tbd.getbusdata(
city='深圳',
keywords=['M433'],
accurate=True, # 精确匹配
timeout=20
)
# line: 线路 GeoDataFrame (WGS84)
# stop: 站点 GeoDataFrame (WGS84)
获取多条线路:
line, stop = tbd.getbusdata(
city='深圳',
keywords=['M433', '1', '2', '地铁1号线'],
accurate=False # 模糊匹配
)
2. 获取行政区划 - getadmin()
获取中国行政区划边界数据。
# 获取深圳市边界
admin, districts = tbd.getadmin(
keyword='深圳',
ak='your_amap_key', # 高德 API Key
jscode='your_jscode', # 高德安全密钥(可选)
subdistricts=True, # 是否获取下级区划
timeout=20
)
# admin: 行政区边界 GeoDataFrame (WGS84)
# districts: 下级区划信息 DataFrame
按行政代码获取:
admin, districts = tbd.getadmin(
keyword='440300', # 深圳市行政代码
ak='your_amap_key'
)
3. 高德等时圈 - get_isochrone_amap()
获取指定点的公交可达范围。
isochrone = tbd.get_isochrone_amap(
lon=114.05, # 起点经度 (WGS84)
lat=22.55, # 起点纬度 (WGS84)
reachtime=30, # 可达时间(分钟)
ak='your_amap_key',
mode=2, # 0=公交, 1=地铁, 2=公交+地铁
timeout=20
)
# 返回 GeoDataFrame
4. Mapbox 等时圈 - get_isochrone_mapbox()
使用 Mapbox 获取等时圈(支持驾车/步行/骑行)。
# 先设置 token
tbd.set_mapboxtoken('your_mapbox_token')
isochrone = tbd.get_isochrone_mapbox(
lon=114.05,
lat=22.55,
reachtime=15, # 分钟
mode='driving', # 'driving', 'walking', 'cycling'
timeout=20
)
完整示例
示例 1: 获取并可视化公交线路
import matplotlib.pyplot as plt
import transbigdata as tbd
# 获取深圳地铁1号线数据
line, stop = tbd.getbusdata(
city='深圳',
keywords=['地铁1号线'],
accurate=True
)
# 可视化
fig, ax = plt.subplots(figsize=(12, 8))
bounds = line.total_bounds
bounds = [bounds[0]-0.05, bounds[1]-0.05, bounds[2]+0.05, bounds[3]+0.05]
tbd.plot_map(plt, bounds, zoom=12, style=4)
line.plot(ax=ax, color='blue', linewidth=3, label='线路')
stop.plot(ax=ax, color='red', markersize=50, label='站点')
plt.legend()
plt.title('深圳地铁1号线')
plt.show()
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.
- 8d ago First seen · 239 lines · 39 tokens per session scan A 437520de3a6a
transbigdata-getdata is a skill published in the GitHub repository ni1o1/claude-skill-transbigdata (4 stars, last pushed 7mo ago), licensed MIT. It adds 39 tokens to every session and 1,752 once invoked, about $0.0002 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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