Official agent skill

Earth2studio Data Fetch

Fetch weather/climate data via Earth2Studio data sources for specific variables and times.

OfficialApache-2.0Auto-check passedResearch & Science

Install Earth2studio Data Fetch

skills CLI

$ npx skills add NVIDIA/skills --skill earth2studio-data-fetch -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills earth2studio-data-fetch --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/earth2studio-data-fetch .claude/skills/earth2studio-data-fetch && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "earth2studio-data-fetch" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-data-fetch into .claude/skills/earth2studio-data-fetch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-data-fetch", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-data-fetch

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add NVIDIA/skills --skill earth2studio-data-fetch -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills earth2studio-data-fetch --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/earth2studio-data-fetch .agents/skills/earth2studio-data-fetch && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "earth2studio-data-fetch" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-data-fetch into .agents/skills/earth2studio-data-fetch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-data-fetch", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill earth2studio-data-fetch -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills earth2studio-data-fetch --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/earth2studio-data-fetch .cursor/skills/earth2studio-data-fetch && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "earth2studio-data-fetch" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-data-fetch into .cursor/skills/earth2studio-data-fetch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-data-fetch", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/NVIDIA/skills.git --path skills/earth2studio-data-fetch

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add NVIDIA/skills --skill earth2studio-data-fetch -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills earth2studio-data-fetch --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/earth2studio-data-fetch .gemini/skills/earth2studio-data-fetch && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "earth2studio-data-fetch" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-data-fetch into .gemini/skills/earth2studio-data-fetch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-data-fetch", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install NVIDIA/skills earth2studio-data-fetch

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add NVIDIA/skills --skill earth2studio-data-fetch -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/earth2studio-data-fetch .github/skills/earth2studio-data-fetch && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "earth2studio-data-fetch" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-data-fetch into .github/skills/earth2studio-data-fetch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-data-fetch", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add NVIDIA/skills --skill earth2studio-data-fetch -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install NVIDIA/skills earth2studio-data-fetch --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/NVIDIA/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/earth2studio-data-fetch .opencode/skills/earth2studio-data-fetch && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "earth2studio-data-fetch" agent skill from https://github.com/NVIDIA/skills/tree/main/skills/earth2studio-data-fetch into .opencode/skills/earth2studio-data-fetch/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "earth2studio-data-fetch", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: earth2studio-data-fetch
GitHub stars: 3.5k
Token cost: ~2.2k tokens
SKILL.md length: 948 words
Files: 8
Skills in repo: 380
Repo updated: Oct 7, 2026
First seen: Oct 7, 2026
Licence: Apache-2.0

At a glance

Fetch weather/climate data via Earth2Studio data sources for specific variables and times.

Works in 6 steps: Understand the user's request → Identify candidate data sources → Verify variable support via lexicon → …
Inference pipelines
SKILL.md covers Purpose, Prerequisites, Instructions and Examples, plus 2 more sections
Runs Python scripts from its folder; calls uv; reaches github.com

What it does

Earth2studio Data Fetch is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Fetch weather/climate data via Earth2Studio data sources for specific variables and times. Do NOT use for inference pipelines, model discovery, or installation.

Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 9 other files (for example `BENCHMARK.md`, `evals/evals.json` and `evals/targets/eval_1_target.py`).

It sits in Research & Science, covering Physical and earth sciences. The repository describes itself as: Agent Skills for NVIDIA products — install into Claude Code, Codex, and other coding agents to run Physical AI, robotics, simulation, CUDA, and RAG workflows end to end. The licence is Apache-2.0.

When your agent uses it

Inference pipelines
Model discovery

Example prompts

“/earth2studio-data-fetch”

Requirements

Python 3

Workflow steps

6 steps, taken from the step headings in SKILL.md.

Understand the user's request
Identify candidate data sources
Verify variable support via lexicon
Confirm data source selection with user
Generate fetch script
Offer next steps

What it can do on your machine

Read from SKILL.md and the folder at commit 0e0d506. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships script files (Python), which the agent can run.
Shell commands in SKILL.md call:
- uv
From the folder's file list and the shell code blocks in SKILL.md.
Network
Hosts in commands or code, which the agent is likely to contact:
- github.com
Also links to:
- nvidia.github.io
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Earth2studio Data Fetch loads about 2.2k tokens when it runs. Until then it costs about 46 tokens; SKILL.md has 948 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~46
When it runs · the whole SKILL.md, loaded when a task matches: ~2.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 0e0d506, republished under its Apache-2.0 licence (© NVIDIA). 948 words, ~2,165 tokens.

Download SKILL.mdSave it as .claude/skills/earth2studio-data-fetch/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.

name: earth2studio-data-fetch
description: Fetch weather/climate data via Earth2Studio data sources for specific variables and times. Do NOT use for inference pipelines, model discovery, or installation.
version: 0.16.0
license: Apache-2.0
metadata.author: NVIDIA Earth-2 Team
metadata.tags: earth2studio, earth2, python, data-fetch, weather-data, xarray

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 earth2studio or 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 live base.py E2STUDIO_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 VOCAB dict
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.

Show full SKILL.md (368 more words)Show less

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:

python

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:

python

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/DataSetFile directly
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

Files

SKILL.md and 7 other files in skills/earth2studio-data-fetch of NVIDIA/skills.

SKILL.md
BENCHMARK.md
evals/evals.json
evals/targets/eval_1_target.py
evals/targets/eval_4_target.py
evals/targets/eval_7_target.py
skill-card.md
skill.oms.sig

Open the folder on GitHubat commit 0e0d506

Compare with similar skills

Earth2studio Data Fetch next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Earth2studio Data Fetch compared with similar skills
Skill	Stars	Used in	Tokens	Auto-check	Licence	Repo updated
Earth2studio Data Fetch this skillNVIDIA/skills	3.5k	—	~2.2k	Automated safety check: Pass	Apache-2.0	today
AstropyzLanqing/codex-claude-academic-skills	4.6k	14 repos	~2.9k	Automated safety check: Pass	BSD-3-Clause	4 mo ago
PymatgenzLanqing/codex-claude-academic-skills	4.6k	12 repos	~5k	Automated safety check: Pass	MIT	4 mo ago
Cantera Ignition DelayK-Dense-AI/scientific-agent-skills	48k	1 repos	~2.2k	Automated safety check: Pass	MIT	2 days ago
Weathertrpc-group/trpc-agent-go	1.8k	8 repos	~591	Automated safety check: Pass	Apache-2.0	today
Pymol VisualizationChatMol/ChatMol	372	—	~1.2k	Automated safety check: Pass	MIT	6 mo ago

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Questions about Earth2studio Data Fetch

What does Earth2studio Data Fetch do?

Fetch weather/climate data via Earth2Studio data sources for specific variables and times. Earth2studio Data Fetch is an agent skill from NVIDIA/skills, published by the product's own GitHub organization. Fetch weather/climate data via Earth2Studio data sources for specific variables and times.

When should I use Earth2studio Data Fetch?

Earth2studio Data Fetch fits situations like: inference pipelines; model discovery.

How do I install Earth2studio Data Fetch in Claude Code?

Run `npx skills add NVIDIA/skills --skill earth2studio-data-fetch -a claude-code`. Or copy the skill folder (skills/earth2studio-data-fetch in NVIDIA/skills) into .claude/skills/earth2studio-data-fetch in your project. Claude Code loads it when a task matches its description.

How do I install Earth2studio Data Fetch in Codex?

Run `npx skills add NVIDIA/skills --skill earth2studio-data-fetch -a codex`. Or copy the skill folder (skills/earth2studio-data-fetch in NVIDIA/skills) into .agents/skills/earth2studio-data-fetch in your project. Codex loads it when a task matches its description.

Can I use Earth2studio Data Fetch in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add NVIDIA/skills --skill earth2studio-data-fetch -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/earth2studio-data-fetch, .gemini/skills/earth2studio-data-fetch, .github/skills/earth2studio-data-fetch and .opencode/skills/earth2studio-data-fetch in your project.

What does Earth2studio Data Fetch need to run?

Going by SKILL.md and its folder, Earth2studio Data Fetch needs Python for the scripts in its folder and the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Earth2studio Data Fetch access the network?

SKILL.md names 2 domains. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. As links in the text: nvidia.github.io. This is read from the text; nothing was executed.

Is Earth2studio Data Fetch safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Earth2studio Data Fetch use?

Earth2studio Data Fetch is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Earth2studio Data Fetch use?

About 2.2k tokens (SKILL.md is roughly 8.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Earth2studio Data Fetch?

Skills that share tags, products or a category with Earth2studio Data Fetch: Astropy (zLanqing/codex-claude-academic-skills, 4.6k stars), Pymatgen (zLanqing/codex-claude-academic-skills, 4.6k stars), Cantera Ignition Delay (K-Dense-AI/scientific-agent-skills, 48k stars) and Weather (trpc-group/trpc-agent-go, 1.8k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Earth2studio Data Fetch?

NVIDIA (a GitHub organization, an official publisher) maintains it in NVIDIA/skills, which has 3,534 GitHub stars. The repository holds 380 skills in this directory. The repository was last updated on October 7, 2026.

Source: NVIDIA/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Earth2studio Data Fetch

Facts

At a glance

What it does

When your agent uses it

Example prompts

Requirements

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Earth2Studio Data Fetch Skill

Purpose

Prerequisites

Instructions

Core principle: live docs and lexicon are the source of truth

Interaction protocol

Step 1. Understand the user's request

Step 2. Identify candidate data sources

Step 3. Verify variable support via lexicon

Step 4. Confirm data source selection with user

Step 5. Generate fetch script

Step 6. Offer next steps

Ownership and out-of-scope

Examples

Limitations

Troubleshooting

Files

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