Agent skill

Nexrad Mosaic Construction

by sickn33 in sickn33/agentic-awesome-skills

Construct a quality-aware NEXRAD multi-radar mosaic from aligned single-site products with explicit coverage, beam geometry, quality weighting, overlap resolution, and provenance.

MITAuto-check passed

Install Nexrad Mosaic Construction

skills CLI
$ npx skills add sickn33/agentic-awesome-skills --skill nexrad-mosaic-construction -a claude-code

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

GitHub CLI
$ gh skill install sickn33/agentic-awesome-skills nexrad-mosaic-construction --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/nexrad-mosaic-construction .claude/skills/nexrad-mosaic-construction && 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
nexrad-mosaic-construction
GitHub stars
47k
Used in
1 other repo
Token cost
~3.1k tokens
SKILL.md length
1,637 words
Files
1
Skills in repo
1,497
Repo updated
First seen
Licence
MIT

At a glance

Construct a quality-aware NEXRAD multi-radar mosaic from aligned single-site products with explicit coverage, beam geometry, quality weighting, overlap resolution, and provenance.

  • Works in 6 steps: verify each site's decoded metadata,… → convert all source fields to one… → align source times using a documented… → …
  • SKILL.md covers Overview, When to Use This Skill, Define the Mosaic Contract and Validate and Align Source Data, plus 14 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Nexrad Mosaic Construction is an agent skill from sickn33/agentic-awesome-skills. Construct a quality-aware NEXRAD multi-radar mosaic from aligned single-site products with explicit coverage, beam geometry, quality weighting, overlap resolution, and provenance.

Its SKILL.md is about 3.1k 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

  • “/nexrad-mosaic-construction”

Workflow steps

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

  1. verify each site's decoded metadata, time, product, units, and quality;
  2. convert all source fields to one declared projection and common output grid;
  3. align source times using a documented tolerance and interpolation policy;
  4. preserve each source's native spatial support and quality fields;
  5. reject a source that is too stale, out of range, or outside its valid product
  6. label any resampling or interpolation rather than presenting the output as

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
    • mrms.ncep.noaa.gov
    • roc.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

Nexrad Mosaic Construction loads about 3.1k tokens when it runs. Until then it costs about 52 tokens; SKILL.md has 1,637 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
~3.1k

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). 1,637 words, ~3,089 tokens.

Download SKILL.mdSave it as .claude/skills/nexrad-mosaic-construction/SKILL.md (or your agent's skills folder).
name
nexrad-mosaic-construction
description
Construct a quality-aware NEXRAD multi-radar mosaic from aligned single-site products with explicit coverage, beam geometry, quality weighting, overlap resolution, and provenance.
category
analysis
risk
safe
source
self
source_type
self
date_added
2026-09-25
author
ShianMike
tags
weather, nexrad, mosaic, multi-radar, quality-weighting, coverage, overlap-resolution
tools
claude, cursor, gemini, codex

NEXRAD Mosaic Construction

Overview

Build a custom multi-radar composite from aligned, validated single-site data. Every output pixel must have a traceable source, coverage state, and quality decision. Preserve the distinction between a local analysis product and an official provider mosaic.

Use this skill after products from multiple radars have been resolved. It does not discover, download, or decode source files. Use nexrad-product-access for single-site access and nexrad-mosaic-access when the required result is an existing NOAA MRMS composite rather than a newly constructed analysis.

When to Use This Skill

  • Combine two or more radars to cover a region with overlapping or adjacent fields.
  • Construct a consistent base-reflectivity, velocity, spectrum-width, or dual-polarization composite.
  • Build a research mosaic on a common grid or for a cross-radar comparison.
  • Compare a custom mosaic with an official MRMS product.
  • Visualize source contributions and coverage gaps in a composite.

Do not use this skill to fetch an official MRMS product, select a single radar, or claim that a simple pixel average is a physically resolved regional field.

Define the Mosaic Contract

Record:

  • product and exact units from each source;
  • source sites, coordinates, volume start/end times, and sweeps;
  • target region, output grid, projection, and cell size;
  • time-matching and temporal interpolation policy;
  • native range limits, beam width, beam height, and vertical sampling;
  • quality-mask, clutter, attenuation, range-folding, and dealiasing policy;
  • overlap-resolution and quality-weighting method;
  • treatment of coastlines, terrain, blocked sectors, and missing gates;
  • output format, metadata, and provenance requirements.

Do not combine a Level II moment and a Level III display product merely because their names resemble one another. Their processing, resolution, and quality semantics must be verified equivalent or the difference must be represented explicitly.

Validate and Align Source Data

Before compositing:

  1. verify each site's decoded metadata, time, product, units, and quality;
  2. convert all source fields to one declared projection and common output grid;
  3. align source times using a documented tolerance and interpolation policy;
  4. preserve each source's native spatial support and quality fields;
  5. reject a source that is too stale, out of range, or outside its valid product contract;
  6. label any resampling or interpolation rather than presenting the output as a native-resolution observation.

When a common time cannot be formed without excessive interpolation, use a nearest time within the stated tolerance, retain the actual time difference, or mark the output unavailable. Do not average distant scans into one pseudo-time without showing the temporal support.

Define the Output Quantity and Quality

For reflectivity, retain dBZ or the product's native calibrated unit and use a quality-aware combination rule. Do not average dBZ as a linear physical quantity without a stated rationale. For velocity, preserve positive and negative radial-velocity conventions and handle dealiasing and folding before compositing. For dual-polarization moments, preserve their physical units and quality masks.

Keep these fields separate:

  • measured or decoded value;
  • quality or suitability score;
  • source site and source time;
  • beam-height or range metadata;
  • interpolation or resampling state;
  • coverage and no-data state.

The output should make it possible to identify which radar contributed each pixel, especially near overlap boundaries.

Select Candidate Data by Quality and Geometry

For each output cell, consider only sources that provide valid coverage at a compatible time and product level. A useful selection score can include:

  • lower beam height at the target location;
  • shorter range and smaller beam width;
  • fewer terrain or clutter artifacts;
  • better data quality or signal-to-noise status;
  • newer scan or smaller time difference;
  • lower attenuation or range-folding penalty;
  • product-specific suitability such as dual-pol quality.

The score is a modeling choice, not a universal truth. State its terms and weights, normalize only comparable components, and test sensitivity to the selection rule. Never hide the score behind an unexplained “best radar” rule.

Resolve Overlaps Deterministically

Use a deterministic rule for every target cell:

  1. select the source with the highest declared quality/suitability score;
  2. apply a documented tie-breaker such as lower beam height, smaller range, newer valid time, or stable site order;
  3. preserve the selected source ID and runner-up source; or
  4. use a physics-aware fusion method only after validating it against single-site fields and reference observations.

Do not average dBZ, radial velocity, or quality fields across radars by default. Blending is acceptable only with a declared product-specific rationale and sensitivity analysis. Never let a blocked, stale, or invalid source win merely because its numeric value is larger, larger in magnitude, or easier to interpolate.

Preserve Coverage and Quality Metadata

The mosaic should emit at least:

  • value field;
  • source-site field;
  • source-time or time-difference field;
  • beam-height or range-quality field when relevant;
  • quality or suitability field;
  • coverage and no-data mask;
  • product, units, grid, projection, and timestamp metadata.

No-data and masked gates must remain distinguishable from a physical zero or a valid low value. A blank-looking region is acceptable only if the coverage mask makes its reason visible. Do not fill blocked sectors with neighboring radar data without labeling the substitution.

Handle 3D and Vertical Products

A two-dimensional mosaic must not silently mix elevation sweeps with incompatible heights. For reflectivity, state the selected elevation policy and range-dependent beam height. For VIL, echo top, or another 3D-derived product, use the provider's product definition or a separately validated 3D algorithm.

For a vertical composite, retain per-source sweep and beam-height metadata, define the vertical target or column, and document the common vertical coordinate. Do not call a lowest-sweep composite a column-integrated quantity or compare it with a true VIL product without explaining the difference.

Validate the Mosaic Scientifically

Inspect:

  • coverage, holes, overlap seams, and domain edges;
  • discontinuities at site boundaries;
  • implausible values or source-selection flips;
  • range, terrain, and beam-height artifacts;
  • agreement with each source in non-overlap regions;
  • comparison with an independent analysis or observation where available;
  • sensitivity to quality weights, time tolerance, and product choice.

An aesthetically smooth mosaic can hide source disagreements. Preserve the unresolved or selected-source map for scientific review. A local composite is an analysis with assumptions, not ground truth.

Show full SKILL.md (656 more words)Show less

Coordinate with Analysis and Visualization

Use radar-satellite-analysis to interpret storm structure. A separate feature-tracking skill may be used after the mosaic when available, but it is not required for mosaic construction. Use nexrad-radar-visualization for site products or decoded gridded products. For a constructed mosaic, plot the value alongside source, quality, and coverage layers and label it as a local analysis.

For an official NOAA composite, retrieve it with nexrad-mosaic-access and preserve its product semantics instead of rebuilding it. A custom mosaic can be compared with that official product, but the two must not be labeled as identical.

Examples

To combine two overlapping Level II reflectivity volumes, verify both sites, sweeps, times, units, and quality; reproject them to a declared grid; then select a source per cell using a documented rule based on beam height, range, time difference, and quality. Preserve source and coverage arrays beside the reflectivity field, and inspect seams and each radar's non-overlap area before interpreting the result.

For an MRMS comparison, retrieve the exact official product through nexrad-mosaic-access, align its valid-time and grid semantics explicitly, and report differences as product/algorithm differences rather than treating either grid as ground truth.

Output Contract

Return a custom mosaic with a clear audit trail:

  1. source radar sites, product or moment, units, volume times, and quality;
  2. output domain, grid, projection, cell size, and time policy;
  3. source-selection or fusion rule and its parameters;
  4. value, source, quality, beam-height/range, and coverage layers;
  5. rejected sources and cells with reasons;
  6. validation results and sensitivity to major construction choices;
  7. scientific limitations and distinction from official MRMS products; and
  8. provenance and checksums for inputs, code, configuration, and outputs.

Do not publish only a colored mosaic. The source, quality, and coverage layers are part of the result whenever overlap or heterogeneous radar support matters.

Verification Checklist

  • Every source product, site, unit, and time is verified before compositing.
  • The common time, grid, projection, and range policy are explicit.
  • Level II and Level III products are not treated as interchangeable.
  • Quality, terrain, beam height, range folding, and de-aliasing checks are applied.
  • Overlap selection is deterministic, auditable, and sensitivity-tested.
  • No-data, blocked sectors, and source substitutions remain visible.
  • Source, quality, and coverage metadata accompany the value field.
  • Custom output is labeled as a local analysis, not an official MRMS product.

Security & Safety Notes

  • Use public or authorized radar data only.
  • Do not expose cloud credentials, signed URLs, private bucket names, or sensitive station metadata in outputs or logs.
  • Treat metadata, source IDs, and product names as untrusted input; validate paths and labels before rendering.
  • Bound site count, volume size, output extent, interpolation, and worker count.
  • Preserve NOAA, NEXRAD, Unidata, and other provider attribution and license terms.

Common Pitfalls

  • Seams appeared at radar boundaries: Source selection or quality weights were not declared. Preserve source and quality layers and test tie-breaks.
  • dBZ was averaged across sites: A logarithmic reflectivity quantity was treated as a linear scalar. Use a product-specific rule and sensitivity.
  • A blocked sector looked like no rain: No-data or quality-masked gates were converted to zero. Preserve the coverage mask.
  • A lower sweep was compared with a 3D product: The vertical meaning was collapsed. State the elevation and product definition.
  • The composite changed between runs: Site order, time tolerance, or random sampling determined the winner. Make selection deterministic and record its parameters.
  • A custom mosaic was called official: The local product was confused with NOAA MRMS. Keep source and algorithm identity in the output metadata.

Limitations

  • Heterogeneous range, beam width, terrain, calibration, and product processing limit cross-site comparability.
  • A selection-based mosaic may create discontinuities or discard useful information from a second radar.
  • No pixel is an independent observation; quality and source fields are correlated in space and time.
  • Custom mosaics require local validation and are not an official warning or truth product.

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/nexrad-mosaic-construction 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

Nexrad Mosaic Construction 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.

Nexrad Mosaic Construction compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Nexrad Mosaic Construction this skillsickn33/agentic-awesome-skills47k1 repos~3.1kAutomated safety check: PassMIT
Radar AutomationComposioHQ/awesome-claude-skills77k3 repos~723Automated safety check: PassNone
Instance Awarenessn8n-io/n8n207k—~1.4kAutomated safety check: PassCustom licence
Sanity Radarsanity-io/sanity6.4k—~2.9kAutomated safety check: PassMIT
Agent Harness Constructionaffaan-m/ECC276k2 repos~222Automated safety check: PassMIT
Agent Harness Constructionaffaan-m/ECC276k—~297Automated safety check: PassMIT

Similar skills

  • Radar Automation

    ComposioHQ/awesome-claude-skills

    Automate Radar tasks via Rube MCP (Composio). An agent skill from ComposioHQ/awesome-claude-skills.

    77k GitHub starsUsed in 3 repos~723 tokens
    Productivity & AutomationAuto-check passed
  • Official

    Load when the request depends on what is already on this instance rather than on what the user just typed: a short or ambiguous opener ("fix it", "carry on", "what should I look at"), a reference to…

    207k GitHub stars~1.4k tokensUpdated today
    Productivity & AutomationAuto-check passed
  • Sanity Radar

    sanity-io/sanity

    Official

    Studio Radar, the repo-health dashboard at radar.sanity.dev (source in dev/radar) — what each tool shows, where the data lives, how to query benchRun / gitCommit / gitTag / bisectSession documents…

    6.4k GitHub stars~2.9k tokensUpdated yesterday
    Auto-check passed
  • 设计和优化AI代理的动作空间、工具定义和观察格式,以提高完成率。

    276k GitHub starsUsed in 2 repos~222 tokens
    Auto-check passed
  • AI エージェントのアクション空間、ツール定義、観測フォーマットを設計・最適化して完了率を向上させます. An agent skill from affaan-m/ECC.

    276k GitHub stars~297 tokensUpdated yesterday
    Auto-check passed
  • LLM API 使用成本优化模式 —— 基于任务复杂度的模型路由、预算跟踪、重试逻辑和提示缓存. An agent skill from affaan-m/ECC.

    277k GitHub starsUsed in 3 repos~1.1k tokens
    AI & LLM EngineeringAuto-check passed

More from sickn33/agentic-awesome-skills

All 1,497 skills in this repo
  • Liuguang Banlan UI

    sickn33/agentic-awesome-skills

    Implements an interface in one of two named color modes, iridescent white or colorful black, from a parameterized starter that reports measured color intensity.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed
  • User Thoughts Memory

    sickn33/agentic-awesome-skills

    Saves a user's project decisions, rules and preferences into a project-local mdbase so later sessions and other agents can recover the intent.

    47k GitHub starsUsed in 1 repo~2.5k tokens
    Auto-check passed
  • Using LWC Memory and Graphs

    sickn33/agentic-awesome-skills

    Keeps project decisions, research and verified results available across coding-agent sessions through LWC memory, a document Wiki graph and a CodeGraph code index.

    47k GitHub starsUsed in 1 repo~2k tokens
    Auto-check passed
  • Find Complementary Founders

    sickn33/agentic-awesome-skills

    Guides an agent through assessing its own owner for cofounder fit, publishing an approved profile, and ranking complementary profiles other agents published for their owners.

    47k GitHub starsUsed in 1 repo~4.8k tokens
    Auto-check passed
  • Whatsapp Cloud API

    sickn33/agentic-awesome-skills

    Integracao com WhatsApp Business Cloud API (Meta). An agent skill from sickn33/agentic-awesome-skills.

    47k GitHub starsUsed in 2 repos~4.5k tokens
    Auto-check passed
  • Cline Pilot

    sickn33/agentic-awesome-skills

    Acts as a proxy for the Cline CLI, dispatching coding tasks one at a time, monitoring runs by hard evidence, relaying decisions to you and learning per-project preferences.

    47k GitHub starsUsed in 1 repo~4.6k tokens
    Auto-check passed

Questions about Nexrad Mosaic Construction

What does Nexrad Mosaic Construction do?

Construct a quality-aware NEXRAD multi-radar mosaic from aligned single-site products with explicit coverage, beam geometry, quality weighting, overlap resolution, and provenance. Nexrad Mosaic Construction is an agent skill from sickn33/agentic-awesome-skills. Construct a quality-aware NEXRAD multi-radar mosaic from aligned single-site products with explicit coverage, beam geometry, quality weighting, overlap resolution, and provenance.

How do I install Nexrad Mosaic Construction in Claude Code?

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

How do I install Nexrad Mosaic Construction in Codex?

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

Can I use Nexrad Mosaic Construction 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 nexrad-mosaic-construction -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/nexrad-mosaic-construction, .gemini/skills/nexrad-mosaic-construction, .github/skills/nexrad-mosaic-construction and .opencode/skills/nexrad-mosaic-construction in your project.

What does Nexrad Mosaic Construction need to run?

SKILL.md names no scripts, command-line tools or credentials: Nexrad Mosaic Construction is instructions for the agent only.

Does Nexrad Mosaic Construction access the network?

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

Is Nexrad Mosaic Construction 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 Nexrad Mosaic Construction use?

Nexrad Mosaic Construction 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 Nexrad Mosaic Construction use?

About 3.1k tokens (SKILL.md is roughly 12k 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 Nexrad Mosaic Construction?

Skills that share tags, products or a category with Nexrad Mosaic Construction: Radar Automation (ComposioHQ/awesome-claude-skills, 77k stars), Instance Awareness (n8n-io/n8n, 207k stars), Sanity Radar (sanity-io/sanity, 6.4k stars) and Agent Harness Construction (affaan-m/ECC, 276k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Nexrad Mosaic Construction?

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.