Agent skill

Radar Satellite Analysis

by sickn33 in sickn33/agentic-awesome-skills

Interpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty.

MITAuto-check passed

Install Radar Satellite Analysis

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill radar-satellite-analysis -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills radar-satellite-analysis --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/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/radar-satellite-analysis .claude/skills/radar-satellite-analysis && 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/

Facts

Skill name
radar-satellite-analysis
GitHub stars
47k
Used in
1 other repo
Token cost
~3.9k tokens
SKILL.md length
2,001 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

Interpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty.

  • Works in 5 steps: Confirm the radar site or physical… → Verify the time interval, scan start and… → Apply the product's documented scale,… → …
  • SKILL.md covers Overview, When to Use This Skill, Define the Remote-Sensing… and Validate the Products Before…, plus 12 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Radar Satellite Analysis is an agent skill from sickn33/agentic-awesome-skills. Interpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty.

Its SKILL.md is about 3.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is MIT.

Example prompts

  • “/radar-satellite-analysis”

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Confirm the radar site or physical satellite, product identity, sector,
  2. Verify the time interval, scan start and end, coordinate reference system,
  3. Apply the product's documented scale, offset, fill values, calibration, and
  4. Inspect missing scans, partial volumes, data gaps, saturated or invalid
  5. Record the resolution, footprint, and observation uncertainty. A high pixel

What it can do on your machine

Read from SKILL.md and the folder at commit b84d35a. 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

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • registry.opendata.aws
    • roc.noaa.gov
    • ospo.noaa.gov

    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

Radar Satellite Analysis loads about 3.9k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 2,001 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~52
When it runs · the whole SKILL.md, loaded when a task matches
~3.9k

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 sickn33/agentic-awesome-skills at commit b84d35a, republished under its MIT licence (© sickn33). 2,001 words, ~3,887 tokens.

Download SKILL.mdSave it as .claude/skills/radar-satellite-analysis/SKILL.md (or your agent's skills folder).
name
radar-satellite-analysis
description
Interpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty.
category
analysis
risk
safe
source
self
source_type
self
date_added
2026-09-25
author
ShianMike
tags
weather, radar, satellite, nexrad, goes, nowcasting, storm-analysis, remote-sensing, uncertainty
tools
claude, cursor, gemini, codex

Radar and Satellite Analysis

Overview

Analyze radar and satellite weather products as remote-sensing observations, not as ground truth. Validate scan time, geometry, calibration, quality flags, and coverage before interpreting a feature. Then separate what the instrument directly measures from what a meteorological inference suggests.

This skill consumes already retrieved and decoded products. Use it after noaa-radar-satellite-fetching or an equivalent source-specific skill. It does not discover buckets, download files, repair outages, or make a product scientifically suitable by naming it radar or satellite data.

When to Use This Skill

  • Interpret NEXRAD reflectivity, velocity, spectrum width, or multi-moment volumes.
  • Track storm growth, decay, splits, mergers, rotation, anvils, or cloud-top evolution over time.
  • Analyze GOES visible, infrared, water-vapor, or derived cloud and convection products.
  • Combine radar structure with satellite cloud-top evidence while preserving each sensor's limitations.
  • Produce a short-term precipitation or severe-weather nowcast from a sequence of observations.
  • Explain why a radar or satellite feature is—or is not—supported by the data.

Do not infer a surface hail size, wind speed, rainfall rate, or lightning count from a single proxy without stating the retrieval assumptions. Do not use a single still image to claim a storm's future track or intensity.

Define the Remote-Sensing Analysis Contract

Before interpreting pixels, beams, or profiles, record:

  • event, target phenomenon, region, and UTC interval;
  • radar site, product level, scan/volume interval, elevation angles, moments, and resolution—or satellite platform, product, sector, channel, scan mode, and resolution;
  • the measurement height or viewing geometry;
  • the quality masks, calibration or scaling, and missing-data policy;
  • the temporal cadence and the allowed lag between radar, satellite, observations, and model guidance;
  • the feature definition and the expected output: structure, motion, intensity proxy, nowcast, or uncertainty map;
  • the criteria for a confident, tentative, or inconclusive interpretation.

Keep observation time, scan start/end time, file creation time, retrieval time, and analysis time separate. A file created quickly after a scan is not a newer measurement.

Validate the Products Before Interpretation

Common checks
  1. Confirm the radar site or physical satellite, product identity, sector, channel or moment, and expected dimensions from the decoded metadata.
  2. Verify the time interval, scan start and end, coordinate reference system, and geolocation. For GOES fixed grids, use the product's geostationary projection metadata rather than treating scan x and y as latitude and longitude.
  3. Apply the product's documented scale, offset, fill values, calibration, and quality flags before computing statistics or thresholds.
  4. Inspect missing scans, partial volumes, data gaps, saturated or invalid values, and spatial coverage. Do not smooth over a gap without labeling it.
  5. Record the resolution, footprint, and observation uncertainty. A high pixel count is not a high spatial resolution if the beam or channel is coarse.
Radar-specific checks
  • Confirm site, volume start/end time, sweep count, moments, and elevation angles. Level II, Level III, and real-time chunk products are not interchangeable encodings.
  • Account for range-dependent beam width, beam height above the radar, terrain blockage, range folding, attenuation, clutter, anomalous propagation, and velocity dealiasing where relevant.
  • Determine whether a feature is sampled by one low-level sweep, several elevation angles, or a full vertical column. Do not compare a low-level echo top with a satellite cloud top as if they were the same physical surface.
  • Keep base reflectivity, column-integrated liquid, echo top, and derived products distinct. Their thresholds and physical meanings are not interchangeable.
Satellite-specific checks
  • Confirm platform, instrument, product short name, channel or RGB product, sector, scan mode, and scan interval from file attributes.
  • Apply channel-specific calibration and quality flags. Brightness temperature, reflectance, cloud-top temperature, and land-surface temperature are different quantities.
  • Treat cloud-top parallax as a viewing-geometry problem. A high cloud can be displaced from its surface footprint, especially near the edge of a sector.
  • Distinguish visible, infrared, water-vapor, and derived RGB products. Their interpretation depends on reflectance, emission, atmospheric absorption, and daylight or scan mode.

Analyze Radar Structure

Reflectivity and precipitation proxies

Use reflectivity to describe the location and organization of precipitation echoes, then qualify the interpretation with beam height, range, attenuation, and sampling. Look for features such as:

  • high-reflectivity cores and their relationship to weaker surrounding echo;
  • echo overhangs, weak-echo regions, bounded weak-echo regions, and vertical development;
  • bow, hook, line, multicell, or training structures when the geometry supports that description;
  • echo-top or vertically integrated liquid patterns that are relevant to the stated question.

Do not call a reflectivity threshold hail, tornado, or destructive wind. Those are conditional interpretations requiring velocity, cloud-top, lightning, surface, or model context. Convert reflectivity to rain rate only with an explicit relation, sample assumptions, and uncertainty bounds; a reflectivity lookup is not a universal precipitation truth.

Velocity and rotation

Analyze radial velocity as a radar-relative measurement. State whether the field is gate-to-gate shear, environmental shear, storm-relative radial flow, or a derived couplet. Consider dealiasing, range folding, noise, side lobes, velocity folding artifacts, and the fact that a velocity signature can have multiple meteorological explanations.

For rotation or mesocyclone interpretation, use the appropriate temporal, azimuthal, and vertical support and compare the result with neighboring scans. A single couplet is a candidate feature, not a confirmed tornado. Do not use radial velocity as a direct measurement of the environmental wind vector without accounting for viewing geometry.

Three-dimensional organization

When a volume supports it, align elevation angles and analyze vertical structure: low-level inflow, mid-level rotation, updraft organization, echo top, overhang, and bounded weak-echo regions. Account for increasing beam volume and decreasing resolution with height. A feature that appears vertically stacked may be a sampling or attenuation artifact; check adjacent sites or scans before asserting a continuous structure.

Analyze Satellite Cloud and Storm Structure

Use satellite data to describe cloud-top and environmental context, not to replace radar precipitation structure. Depending on the product, examine:

  • cloud-top temperature, brightness-temperature gradients, and overshooting top candidates;
  • anvil extent, spreading direction, and relationship to upper-level outflow;
  • deep-convective cloud shields and mesoscale convective systems;
  • visible-texture changes, cloud-top lowering or warming, and inferred growth or decay only with a time sequence;
  • water-vapor patterns, upper-level moisture, and potential convective environments when the product and resolution support those claims.

An infrared cold cloud top is not, by itself, proof of severe weather, hail, or a particular updraft strength. A very cold top can reflect a high cloud whose emission and viewing geometry differ from a nearby lower cloud. State the proxy, the threshold, and the alternative explanation.

Track Evolution and Motion

For a time sequence, use a consistent feature-identification and tracking method. Track cell or cloud-system centers, echoes or anvils, growth and decay, merges and splits, and changes in shape or intensity proxy. Distinguish motion from the steering flow and from apparent motion caused by parallax, changing scan geometry, or inconsistent feature definitions.

Estimate motion over multiple scans and report the method, time interval, position uncertainty, and whether the feature was occluded or lost. A single displacement between scans is not a reliable nowcast. If a forecast window is short, say so and show the persistence or extrapolation assumption.

Show full SKILL.md (852 more words)Show less

Combine Radar and Satellite Carefully

Align the products in time and space before joining them. Radar can provide precipitation structure, radial motion, and vertical echoes; satellite can provide broad cloud shield, cloud-top context, anvils, and environmental patterns. Their footprints, resolution, viewing geometry, and quality are not identical.

Use a joint interpretation only when the evidence is complementary. For example, a radar core beneath a rapidly developing cold cloud top can support a convective-growth diagnosis, but it still does not establish surface hail or a confirmed tornado. Preserve each source's quality mask and contribution in the final result instead of collapsing them into an opaque score.

For multi-radar mosaics, resolve overlapping beams using a documented rule. Never average dBZ values or velocity fields across sites without a physical and quality-aware rule. A mosaic should show coverage and the selected source for each pixel or cell.

Quantify Uncertainty and Avoid Overclaiming

Remote-sensing interpretation has several uncertainty sources: beam geometry, attenuation, parallax, threshold choice, temporal resolution, feature tracking, instrument calibration, cloud microphysics, and incomplete coverage. When the task is operational or safety-relevant, report the strongest alternative interpretation and the observations that would distinguish it.

Use a bounded nowcast horizon and show how the result changes with alternate motion, persistence, growth, or decay assumptions. Do not turn a qualitative feature label into a precise probability without a calibration method. If the images are too sparse, ambiguous, or poorly geolocated, report that the feature is unconfirmed rather than filling the gap with narrative certainty.

Examples

When asked whether a storm intensified over an hour, align successive radar volumes and satellite scans, verify time and geolocation, and track one consistent feature definition. Report measured reflectivity and cloud-top changes separately from inferred intensification, include data gaps and uncertainty, and do not infer surface hail from a cold cloud top alone.

For a suspected velocity couplet, inspect neighboring scans and available elevation angles, confirm radial-velocity convention and dealiasing quality, and call it a possible rotation signature unless the evidence supports a stronger conclusion.

Output Contract

Return a traceable analysis with:

  1. the radar site or satellite platform, product, channel/moment, scan interval, region, and time range;
  2. the preprocessing, calibration, geolocation, quality masking, and temporal alignment steps;
  3. the observed feature locations, structure, motion, and evolution with the supporting pixels or beams identified;
  4. a clear separation between direct measurements, derived indices, and meteorological interpretation;
  5. uncertainty, alternative explanations, and coverage limitations;
  6. figures or maps that preserve timestamps, color scales, units, and source;
  7. provenance for the raw or materialized products and every derived artifact.

Avoid a report that says only "severe storm," "heavy rain," or "rapidly developing convection" without a location, time, measured proxy, and evidence beneath the label.

Verification Checklist

  • Radar site or satellite platform and product metadata are confirmed.
  • Scan start/end time, retrieval time, and analysis time are distinct.
  • Geolocation, projection, scale/offset, calibration, and quality flags are applied.
  • Beam height, beam width, range, terrain, attenuation, and radar artifacts are considered where relevant.
  • Satellite channel meaning, scan mode, daylight dependence, and parallax are considered.
  • Radar and satellite claims remain separate unless their alignment and complementary evidence are explicit.
  • Feature tracking uses multiple scans and reports its motion assumptions.
  • Derived products include units, thresholds, coverage, and uncertainty.

Security & Safety Notes

  • Use only public or authorized radar and satellite products.
  • Do not expose AWS credentials, signed URLs, private bucket names, tokens, or restricted provider metadata in reports or logs.
  • Treat filenames, metadata, and product attributes as untrusted input; validate them before constructing paths or visualizations.
  • Bound scan counts, image sizes, decoding concurrency, and generated artifact resolution.
  • Preserve NOAA, NEXRAD, GOES, and other provider attribution and license terms.

Common Pitfalls

  • The file creation time was used as measurement time: A delayed or reprocessed product was placed at the wrong point in a storm timeline. Parse scan start and end time.
  • A cold cloud top became a hail claim: An infrared proxy was treated as a direct hail measurement. State the proxy and supporting evidence.
  • Radar and satellite features did not line up: Scan intervals, parallax, geolocation, or beam height were ignored. Align the observations first.
  • A reflectivity pixel became a rain rate: A threshold or lookup relation was used without its assumptions and uncertainty. Keep the measured quantity separate from the estimate.
  • A storm “rapidly intensified” from two frames: A gap, feature mismatch, or inconsistent threshold was mistaken for growth. Use a longer sequence and tracking evidence.
  • Radar mosaics hid artifacts: Values were averaged across sites or a blocked beam was treated as a complete field. Retain quality and source information.

Limitations

  • Radar and satellite products are remote-sensing observations with sampling, calibration, attenuation, parallax, and coverage limitations.
  • A single product cannot uniquely identify every convective or microphysical process.
  • Historical gaps, instrument outages, scan strategy changes, and provider product changes cannot be repaired by interpretation alone.
  • This skill does not retrieve products, perform numerical model comparison, or replace official warning and emergency-management guidance.

Additional Resources

© sickn33, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/radar-satellite-analysis of sickn33/agentic-awesome-skills.

Open the folder on GitHubat commit b84d35a

Used in 1 other repository

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Radar Satellite Analysis 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.

Radar Satellite Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Radar Satellite Analysis this skillsickn33/agentic-awesome-skills47k1 repos~3.9kAutomated safety check: PassMIT
Weatheropenclaw/openclaw392k—~726Automated safety check: PassMIT
Langsmith ObservabilityOrchestra-Research/AI-Research-SKILLs13k2 repos~2.4kAutomated safety check: PassMIT
ObservabilityBuilderIO/agent-native7.1k—~7.3kAutomated safety check: PassNone
Form Validationthedaviddias/Front-End-Checklist74k—~633Automated safety check: PassMIT
Python Observabilitywshobson/agents40k—~1.8kAutomated safety check: PassMIT

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Questions about Radar Satellite Analysis

What does Radar Satellite Analysis do?

Interpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty. Radar Satellite Analysis is an agent skill from sickn33/agentic-awesome-skills. Interpret weather radar and satellite observations by validating product metadata and geometry, deriving storm and cloud structures, tracking evolution, and quantifying uncertainty.

How do I install Radar Satellite Analysis in Claude Code?

Run `npx skills add sickn33/agentic-awesome-skills --skill radar-satellite-analysis -a claude-code`. Or copy the skill folder (skills/radar-satellite-analysis in sickn33/agentic-awesome-skills) into .claude/skills/radar-satellite-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Radar Satellite Analysis in Codex?

Run `npx skills add sickn33/agentic-awesome-skills --skill radar-satellite-analysis -a codex`. Or copy the skill folder (skills/radar-satellite-analysis in sickn33/agentic-awesome-skills) into .agents/skills/radar-satellite-analysis in your project. Codex loads it when a task matches its description.

Can I use Radar Satellite Analysis 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 sickn33/agentic-awesome-skills --skill radar-satellite-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/radar-satellite-analysis, .gemini/skills/radar-satellite-analysis, .github/skills/radar-satellite-analysis and .opencode/skills/radar-satellite-analysis in your project.

What does Radar Satellite Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Radar Satellite Analysis is instructions for the agent only.

Does Radar Satellite Analysis access the network?

SKILL.md names 3 domains. As links in the text: registry.opendata.aws, roc.noaa.gov and ospo.noaa.gov. This is read from the text; nothing was executed.

Is Radar Satellite Analysis 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 Radar Satellite Analysis use?

Radar Satellite Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Radar Satellite Analysis use?

About 3.9k tokens (SKILL.md is roughly 16k 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 Radar Satellite Analysis?

Skills that share tags, products or a category with Radar Satellite Analysis: Weather (openclaw/openclaw, 392k stars), Langsmith Observability (Orchestra-Research/AI-Research-SKILLs, 13k stars), Observability (BuilderIO/agent-native, 7.1k stars) and Form Validation (thedaviddias/Front-End-Checklist, 74k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Radar Satellite Analysis?

sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,405 GitHub stars. The repository holds 1,497 skills in this directory. The repository was last updated on October 9, 2026.

Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.