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weather-plugin

weather-plugin is a skill for Claude Code from skillmds/skillmd. It costs 23 tokens per session (1,097 once invoked), scanned A, original, MIT.

A usage guide for weather tools that provide current conditions, forecasts, warnings, radar and weather-model maps. It also covers meteorological calculations such as dew point and wind chill.

In plain words
What is it for?
Use it to answer weather questions, show radar or model imagery, check severe-weather threats, retrieve soundings, and calculate weather values.
Why use it?
It helps an agent choose the correct weather tool for a request and avoid calling tools that do not exist.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the data-ml plugin — 17 skills shipped together

not rated 143repo +49 changed 1mo ago A scan 23 tokens original MIT

Good fit Use it to answer weather questions, show radar or model imagery, check severe-weather threats, retrieve soundings, and calculate weather values.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/skillmds/skillmd/weather-plugin
View source ↗ skillmds/skillmd skillmd.com
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add skillmds/skillmd --skill weather-plugin
Clone the repo
git clone --depth 1 https://github.com/skillmds/skillmd

Made for: Claude Code.

Or install data-ml, the plugin that ships this one along with the rest of its 17 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for weather-plugin

README.md
[![agentmods](https://agentmods.dev/badge/skills/skillmds/skillmd/weather-plugin/github.svg)](https://agentmods.dev/skills/skillmds/skillmd/weather-plugin)
Your own site
<a href="https://agentmods.dev/skills/skillmds/skillmd/weather-plugin"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/weather-plugin/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for weather-plugin

Your own site · 80×15
<a href="https://agentmods.dev/skills/skillmds/skillmd/weather-plugin"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/weather-plugin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 23 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,097 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00023 $0.01097
Opus 5.5 $0.00009 $0.00439
Sonnet 5.5 $0.00005 $0.00219
Haiku 4.5 $0.00002 $0.00110

Measured 1mo ago against content hash 5e976ee7bd24, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

weather-plugin scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.

The scan reads SKILL.md. This mod also ships 5 executable files (__init__.py, bootstrap.py, model_support.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/data-ml/skills/weather-plugin/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Weather Plugin Usage Guide

You have 12 weather tools. Prefer lightweight data tools and only use images when asked.

Tool Selection

"How's the weather in [place]?" → wx_conditions "What's the forecast?" → wx_forecast "Any warnings/watches?" → wx_alerts "Show me the radar" → wx_radar_image "Show me CAPE/temperature/wind map" → wx_model_image (use comma-separated vars: "cape,helicity,uh,srh" for multiple in ONE call) "What's the severe weather outlook?" → wx_severe "Weather in Tokyo/London/etc" → wx_global (worldwide) "Calculate dewpoint/wind chill/LCL" → wx_calc "Is there a tornado threat?" → wx_severe first, then wx_model_image --var cape or --var helicity for visual "What does the HRRR show?" → wx_model_image with appropriate var "Show me the 18z HRRR" → wx_model_image with cycle: 18 "Sounding at a point" → wx_sounding

IMPORTANT: Only these 12 tools exist

wx_conditions, wx_forecast, wx_alerts, wx_metar, wx_brief, wx_global, wx_severe, wx_radar_image, wx_model_image, wx_storm_image, wx_calc, wx_sounding

Do NOT try to call any other tool names.

Rules

  1. Start lightweight. Use wx_conditions or wx_brief before heavier tools.
  2. Images only when asked. Don't call image tools unless the user wants to SEE something.
  3. US vs international. Use wx_global for non-US locations.
  4. Calculations are verified. wx_calc uses metrust — 205 Rust-backed functions verified against MetPy.
  5. Don't stack redundant calls. wx_brief already includes conditions + forecast + alerts.
  6. BATCH model images. NEVER call wx_model_image multiple times for different variables. Use comma-separated vars in ONE call: "cape,helicity,uh,srh". This generates all images at once.
  7. Severe weather package. For severe weather analysis, use: var: "cape,helicity,uh,srh" — one call, 4 images.
  8. ZOOM to threat area. When the user asks about a specific region (e.g., "Indiana to New York"), ALWAYS pass lat/lon and radius_km to zoom in. Don't show full CONUS when they asked about a corridor. Examples:
    • "Ohio Valley" → lat: 39.5, lon: -83, radius_km: 400
    • "Indiana to New York" → lat: 40.5, lon: -77, radius_km: 500
    • "Texas" → lat: 31.5, lon: -99, radius_km: 500
    • "Southeast" → lat: 33, lon: -85, radius_km: 600
    • "Great Plains" → lat: 38, lon: -99, radius_km: 600 Use your geographic knowledge to pick appropriate center + radius.

Read the full file on GitHub · 68 lines

Files

What ships with it

11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 1mo ago Changed · +1 lines 5e976ee7bd24
  2. 1mo ago First seen · 67 lines · 23 tokens per session scan A 887d5feedb09

Subscribe to this mod's changes

weather-plugin is a skill published in the GitHub repository skillmds/skillmd (143 stars, last pushed 4d ago), licensed MIT. It adds 23 tokens to every session and 1,097 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-19.

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