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.
npx skills add skillmds/skillmd --skill weather-plugingit clone --depth 1 https://github.com/skillmds/skillmdWrote 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.
[](https://agentmods.dev/skills/skillmds/skillmd/weather-plugin)<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.
<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>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00023 | $0.01096 |
| Opus 5.5 | $0.00009 | $0.00438 |
| Sonnet 5 | $0.00005 | $0.00219 |
| Haiku 4.5 | $0.00002 | $0.00110 |
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 4d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 67 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
- Start lightweight. Use
wx_conditionsorwx_briefbefore heavier tools. - Images only when asked. Don't call image tools unless the user wants to SEE something.
- US vs international. Use
wx_globalfor non-US locations. - Calculations are verified.
wx_calcuses metrust — 205 Rust-backed functions verified against MetPy. - Don't stack redundant calls.
wx_briefalready includes conditions + forecast + alerts. - 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. - Severe weather package. For severe weather analysis, use:
var: "cape,helicity,uh,srh"— one call, 4 images. - 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: 600Use your geographic knowledge to pick appropriate center + radius.
- "Ohio Valley" →
What ships with it
10 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.
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.
- 4d ago First seen · 67 lines · 23 tokens per session scan A 887d5feedb09
weather-plugin is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 23 tokens to every session and 1,096 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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