Individual skill
climate-data-analysis
Working with observational climate data — reanalyses, station records, anomalies, trends, and honest uncertainty.
skills/climate-data-analysis/SKILL.md
Source commit faf37c0859ebf627bfcb5225e8057d088691e0ae
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- No
- Observed behavior
- Not checked yet
- Last checked
- Nov 8, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 54
- Repository last updated
- Oct 10, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- ai-agents Repository topic
- ai-agents-automation Repository topic
- ai-skills Repository topic
- awesome Repository topic
- awesome-list Repository topic
- chatgpt Repository topic
- claude-skills Repository topic
- meta-ai Repository topic
- muse-ai Repository topic
- data Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Read excerpt
## Overview
Climate data analysis turns raw observations (stations, satellites, ocean
buoys) into statements about how the climate is changing. This skill covers
the standard datasets (ERA5, HadCRUT, GISTEMP, Berkeley Earth), computing
anomalies and trends correctly, handling spatial/temporal inhomogeneities,
and presenting uncertainty the way the field expects.
## When to use
- Computing temperature or precipitation trends for a region or the globe
- Comparing observations with model output (after regridding to a common grid)
- Building a climate indicator: heatwave days, frost days, growing-season length
- Checking a claim about "record" warmth/rainfall against the data