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-coordinatesgit 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-coordinates)<a href="https://agentmods.dev/skills/ni1o1/claude-skill-transbigdata/transbigdata-coordinates"><img src="https://agentmods.dev/badge/skills/ni1o1/claude-skill-transbigdata/transbigdata-coordinates.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 | $0.00045 | $0.01305 |
| Opus 5 | $0.00023 | $0.00652 |
| Sonnet 5 | $0.00009 | $0.00261 |
| Haiku 4.5 | $0.00005 | $0.00130 |
Grade A, and why
transbigdata-coordinates 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
TransBigData 坐标转换指南
安装
pip install transbigdata
中国常用坐标系说明
| 坐标系 | 说明 | 常见来源 |
|---|---|---|
| WGS84 | GPS原始坐标,国际标准 | GPS设备、OpenStreetMap |
| GCJ02 | 国测局坐标(火星坐标) | 高德地图、腾讯地图 |
| BD09 | 百度坐标 | 百度地图 |
| BD09MC | 百度墨卡托坐标 | 百度地图API |
坐标转换函数
WGS84 转换
import transbigdata as tbd
# WGS84 → GCJ02
data['Lng_gcj'], data['Lat_gcj'] = tbd.wgs84togcj02(data['Lng'], data['Lat'])
# WGS84 → BD09
data['Lng_bd'], data['Lat_bd'] = tbd.wgs84tobd09(data['Lng'], data['Lat'])
GCJ02 转换
# GCJ02 → WGS84
data['Lng_wgs'], data['Lat_wgs'] = tbd.gcj02towgs84(data['Lng'], data['Lat'])
# GCJ02 → BD09
data['Lng_bd'], data['Lat_bd'] = tbd.gcj02tobd09(data['Lng'], data['Lat'])
BD09 转换
# BD09 → WGS84
data['Lng_wgs'], data['Lat_wgs'] = tbd.bd09towgs84(data['Lng'], data['Lat'])
# BD09 → GCJ02
data['Lng_gcj'], data['Lat_gcj'] = tbd.bd09togcj02(data['Lng'], data['Lat'])
# BD09MC → BD09
data['Lng_bd'], data['Lat_bd'] = tbd.bd09mctobd09(data['x'], data['y'])
GeoDataFrame 坐标转换
transform_shape() - 批量转换几何对象
import geopandas as gpd
# 加载 GCJ02 坐标的数据
gdf = gpd.read_file('data_gcj02.shp')
# 转换为 WGS84
gdf_wgs84 = tbd.transform_shape(gdf, method=tbd.gcj02towgs84)
距离计算
getdistance() - 两点间距离
计算 WGS84 坐标系下两点间的球面距离。
# 单点距离
distance = tbd.getdistance(lon1, lat1, lon2, lat2) # 返回米
# 向量化计算
data['distance'] = tbd.getdistance(
data['slon'], data['slat'],
data['elon'], data['elat']
)
完整示例
示例 1:高德数据转 WGS84
import pandas as pd
import transbigdata as tbd
# 加载高德地图采集的数据(GCJ02坐标)
data = pd.read_csv('amap_data.csv')
# 转换为 WGS84
data['Lng_wgs'], data['Lat_wgs'] = tbd.gcj02towgs84(data['Lng'], data['Lat'])
# 保存
data.to_csv('data_wgs84.csv', index=False)
示例 2:百度数据转 WGS84
import pandas as pd
import transbigdata as tbd
# 加载百度地图数据(BD09坐标)
data = pd.read_csv('baidu_data.csv')
# 转换为 WGS84
data['Lng_wgs'], data['Lat_wgs'] = tbd.bd09towgs84(data['Lng'], data['Lat'])
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 · 177 lines · 45 tokens per session scan A 28e48df645b2
transbigdata-coordinates is a skill published in the GitHub repository ni1o1/claude-skill-transbigdata (4 stars, last pushed 7mo ago), licensed MIT. It adds 45 tokens to every session and 1,305 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.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
mapping-to-snomed
Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…