earth2studio-data-fetch
Fetch weather/climate data via Earth2Studio data sources for specific variables and times. Do NOT use for inference pipelines, model discovery, or installation.
Install / Use
npx skills add NVIDIA/skills --skill earth2studio-data-fetchInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
AutomationSupported Platforms
Our assessment of earth2studio-data-fetch
earth2studio-data-fetch scores 92/100 on our quality scale, 713th of 2,125 Automation skills we index (top 34%).
Its SKILL.md is 8.5 KB long, well organised into 21 sections with 2 code examples: a thorough specification that gives an agent plenty to work with.
With 3,421 GitHub stars, it is one of the more widely adopted skills in the catalogue.
Maintenance, license and trust
- The repository was last updated 5 days ago, so earth2studio-data-fetch is actively maintained.
- It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.
earth2studio-data-fetch compared with similar skills
All 4 of these similar skills score higher than earth2studio-data-fetch; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| earth2studio-data-fetch (this skill)by NVIDIA | 92 | 3.4k | 5d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 86.0k | 13d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 74.0k | today | CLAUDE.md |
| rufloby ruvnet | 100 | 73.4k | today | CLAUDE.md |
| crawl4aiby unclecode | 100 | 84.4k | 3d ago | MCP Server |
Frequently asked questions
- How do I install earth2studio-data-fetch?
- Run
npx skills add NVIDIA/skills --skill earth2studio-data-fetch. The install tabs above show the steps for each supported agent. - Which AI agents does earth2studio-data-fetch work with?
- It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
- Is earth2studio-data-fetch safe to use?
- It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is earth2studio-data-fetch still maintained?
- The repository was last updated 5 days ago, so earth2studio-data-fetch is actively maintained.
Skill content
View source on GitHubname: earth2studio-data-fetch version: 0.16.0 license: Apache-2.0 metadata: author: NVIDIA Earth-2 Team tags: - earth2studio - earth2 - python - data-fetch - weather-data - xarray description: > Fetch weather/climate data via Earth2Studio data sources for specific variables and times. Do NOT use for inference pipelines, model discovery, or installation.
Earth2Studio Data Fetch Skill
Purpose
Guide a user through downloading weather/climate data via Earth2Studio data source APIs. Identifies compatible sources by checking the lexicon, verifies variable support, and produces a working fetch script outputting an xarray DataArray.
Prerequisites
- Earth2Studio installed (
uv pip install earth2studioor equivalent) - Network access to remote data stores (GCS, S3, CDS API, etc.)
- For CDS-based sources: valid CDS API key configured (
~/.cdsapirc) - Python 3.10+
Instructions
You are helping a user download specific weather/climate data using Earth2Studio's data source APIs. Your job is to identify which data source(s) can provide the requested variables, verify compatibility via the lexicon system, and produce a working fetch script.
Core principle: live docs and lexicon are the source of truth
Data source APIs, available variables, and the lexicon evolve between releases. Before recommending a data source or writing a fetch script:
- Fetch the relevant data source doc page to confirm the API signature and constructor arguments.
- Check the lexicon to verify the requested variable is supported by that data source.
Live doc references (fetch only what the user's request requires):
- Analysis data sources: https://nvidia.github.io/earth2studio/modules/datasources_analysis.html
- Forecast data sources: https://nvidia.github.io/earth2studio/modules/datasources_forecast.html
- DataFrame data sources: https://nvidia.github.io/earth2studio/modules/datasources_dataframe.html
- Lexicon base: https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/lexicon/base.py
- Lexicon per-source: https://github.com/NVIDIA/earth2studio/tree/main/earth2studio/lexicon
Interaction protocol
Step 1. Understand the user's request
Extract from what the user has said (ask follow-ups if needed, cap at 3 questions):
- Variables — what do they want? Use Earth2Studio variable names
(e.g.
t2m,u500,z850,tp,msl). If the user uses plain language ("500 hPa geopotential height"), map it to the E2Studio name by checking the livebase.pyE2STUDIO_VOCAB. - Time — what date/time range? A single timestamp, a range, or multiple discrete times?
- Data type — analysis/reanalysis (historical state) or forecast (lead-time based)?
- Lead time (forecast only) — how far ahead? Which initialization time?
- Region — global or regional (e.g. North America for HRRR)?
- Output format — xarray DataArray (default), save to file (NetCDF/Zarr)?
Step 2. Identify candidate data sources
Based on the request type, narrow candidates:
Analysis/reanalysis (historical state at a specific time):
- Use analysis data source page to identify options
- Common choices: GFS (operational, recent), HRRR (NA, hourly), IFS/IFS_ENS (ECMWF), ARCO/CDS/WB2ERA5/NCAR_ERA5 (ERA5 reanalysis), GOES/MRMS/JPSS (observational)
Forecast (predictions from an initialization time with lead times):
- Use forecast data source page to identify options
- Common choices: GFS_FX, GEFS_FX, HRRR_FX, IFS_FX, IFS_ENS_FX, AIFS_FX, CFS_FX
Key differentiators to surface:
- Temporal coverage — operational sources (GFS, HRRR) have limited history; reanalysis (ERA5 via ARCO/CDS/WB2) goes back decades
- Spatial resolution — HRRR is 3km NA-only; GFS is 0.25° global; WB2ERA5_32x64 is 5.625° global
- Update frequency — some are real-time, some have multi-day lag
Step 3. Verify variable support via lexicon
This is critical. Each data source has a lexicon file that defines which E2Studio variables it can provide.
To verify:
- Fetch the source's lexicon file from
https://github.com/NVIDIA/earth2studio/blob/main/earth2studio/lexicon/<source>.py(e.g.gfs.py,hrrr.py,cds.py,arco.py,wb2.py) - Check that the user's requested variable(s) appear as keys in the
source's
VOCABdict - If a variable is NOT in a source's lexicon, that source cannot provide it — try another
The lexicon VOCAB maps Earth2Studio variable names → source-specific identifiers. If a variable key exists in the VOCAB, the source supports it.
Present the results clearly: "GFS supports t2m, u500, z850. HRRR also
supports these but is limited to North America. ARCO (ERA5) supports all
three and has data back to 1959."
Step 4. Confirm data source selection with user
Present the viable options with tradeoffs:
| Source | Variables | Coverage | Resolution | Time Range | |--------|-----------|----------|------------|------------| | ... | ... | ... | ... | ... |
Let the user pick. If there's one obvious choice, recommend it and ask for confirmation.
Step 5. Generate fetch script
Write a Python script that uses the selected data source to fetch the requested data. The script structure depends on whether it's an analysis or forecast source.
Analysis source pattern:
import datetime
from earth2studio.data import <SourceClass>
# Initialize data source
ds = <SourceClass>()
# Fetch data
# Analysis sources use: ds(time, variable) -> xr.DataArray
time = [datetime.datetime(YYYY, M, D, H)] # or array of times
variable = ["var1", "var2"] # E2Studio variable names
data = ds(time, variable)
Forecast source pattern:
import datetime
from earth2studio.data import <SourceClass>
# Initialize data source
ds = <SourceClass>()
# Forecast sources use: ds(time, lead_time, variable) -> xr.DataArray
time = [datetime.datetime(YYYY, M, D, H)] # initialization time
lead_time = [datetime.timedelta(hours=H)] # or array of lead times
variable = ["var1", "var2"]
data = ds(time, lead_time, variable)
Always fetch the specific data source's API doc page to confirm the exact constructor arguments and call signature before writing the script — they can vary (some need auth tokens, cache paths, specific parameters).
Include in the script:
- Appropriate imports
- Clear comments explaining each step
- How to inspect the result (
print(data),data.shape,data.coords) - Optional: saving to file if the user requested it
Step 6. Offer next steps
After delivering the script, mention:
- How to change variables/times without rewriting the whole thing
- If they might want to feed this into a model, point them to the discover skill
- Cache behavior (data is cached locally after first fetch via
EARTH2STUDIO_CACHE)
Ownership and out-of-scope
Owns: identifying data sources for a user's variable/time request, verifying variable support via lexicon, generating data fetch scripts, explaining analysis vs. forecast source differences.
Does not own: installation (earth2studio-install), model selection (earth2studio-discover), inference pipelines, custom data source creation (point to extend examples), data source authentication setup beyond what the docs describe.
Examples
Typical invocation:
"I need 500 hPa geopotential height and 2m temperature from ERA5 for January 1, 2020 at 00Z."
The skill would:
- Map plain language →
z500,t2m - Check ARCO/CDS/WB2ERA5 lexicons for support
- Recommend ARCO (free, no API key) or CDS (official, needs key)
- Generate a fetch script using the selected source
Limitations
- Network required — all data sources fetch from remote stores (GCS, S3, CDS API)
- No local file loading — for local NetCDF/Zarr, use
DataArrayFile/DataSetFiledirectly - One source type per script — cannot mix analysis and forecast sources in a single call
- Variable availability varies — not all sources provide all variables; always verify via lexicon
- Rate limits — CDS API has queue-based throttling; GCS/S3 sources are generally faster
Troubleshooting
| Error | Cause | Solution |
|-------|-------|----------|
| KeyError: '<var>' | Not in lexicon | Check lexicon; try another source |
| FileNotFoundError / 404 | Time not available | Verify temporal coverage |
| CDS API timeout | Queue congestion | Retry or use ARCO for ERA5 |
| ModuleNotFoundError | Not installed | uv pip install earth2studio |
| Empty DataArray | Time/var mismatch | Check datetime and variable name |
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
