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 runxhq/runx --skill nws-weathergit clone --depth 1 https://github.com/runxhq/runxWrote 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/runxhq/runx/nws-weather)<a href="https://agentmods.dev/skills/runxhq/runx/nws-weather"><img src="https://agentmods.dev/badge/skills/runxhq/runx/nws-weather/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/runxhq/runx/nws-weather"><img src="https://agentmods.dev/badge/skills/runxhq/runx/nws-weather.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 | $0.00031 | $0.00744 |
| Opus 5.5 | $0.00012 | $0.00298 |
| Sonnet 5.5 | $0.00006 | $0.00149 |
| Haiku 4.5 | $0.00003 | $0.00074 |
Grade A, and why
nws-weather 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 yesterday.
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.
NWS Weather
Fetch the National Weather Service forecast for an intended US location through
the selected NWS connector. The default at-point runner accepts numeric
latitude and longitude, reads the provider's point-to-grid mapping and then
fetches that grid's forecast. No API key, office-code lookup or hand-built
grid request is needed for this route.
Use this for read-only forecast evidence. Global place-name discovery and
Open-Meteo data belong to open-meteo-weather; resolve the intended place first
when coordinates are unknown. Do not guess a city or assume that a US place
shares a grid with a nearby point. Notification, scheduling and operational
actions need their own skill and authority.
How it works
- Supply the intended latitude and longitude. The NWS request uses four decimal places, matching the existing points contract.
points.readreturns the provider'sgridId,gridXandgridY. A bounded domain function admits those exact fields and keeps the point, timezone, forecast URL and native readback binding inlocation. It never derives a grid from geography or follows an arbitrary returned URL.- Only a usable mapping proceeds through the existing
forecast.readoperation. Its original provider packet retains forecast periods, units, generated/update times and native evidence. - Return the observed data. The calling agent interprets the forecast and its coverage; this reader does not invent planning advice or publication.
The NWS API documentation explains point-to-grid discovery. Mappings can change, so reuse a saved grid only while its freshness is appropriate to the caller's job. The default reads the current mapping; it does not maintain a cache or background loop.
Reuse in graphs
forecast still accepts office, grid_x and grid_y as strings and reads
that exact grid once. It never calls points or geocodes again. locate still
accepts string lat and lon for a points-only read. Preserve the native
provider packet and readback when handing either result downstream.
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.
- fixtures/composes-in-graph.yaml 612 B
- fixtures/nws-forecast-washington-monument.yaml 1.9 KB
- fixtures/nws-locate-washington-monument.yaml 714 B
- fixtures/point-forecast.yaml 2.1 KB
- fixtures/point-invalid-grid.yaml 1013 B
- fixtures/point-missing-grid.yaml 992 B
- fixtures/point-provider-large-grid.yaml 1.9 KB
- fixtures/support/composed.graph 241 B
- point.mjs 1.0 KB runs code
- references/catalog.md 143 B
- run.yaml 4.2 KB
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.
- yesterday Changed · -36 lines · +5 tokens per session 3ca1ec58da3f
- 20d ago First seen · 103 lines · 26 tokens per session scan A 55212e7a0091
nws-weather is a skill published in the GitHub repository runxhq/runx (104 stars, last pushed yesterday), licensed Apache-2.0. It adds 31 tokens to every session and 744 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 2026-10-18.
nws-weather compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| nws-weatherrunxhq/runx | Skill | 104 | yesterday | Apache-2.0 | A | 31 | Claude Code, Codex |
| dd-synthesisAbilityai/trinity | Skill | 741 | yesterday | Apache-2.0 | A | 33 | Claude Code, Codex |
| dd-report-formatAbilityai/trinity | Skill | 741 | yesterday | Apache-2.0 | A | 27 | Claude Code, Codex |
| dd-risk-scoringAbilityai/trinity | Skill | 741 | yesterday | Apache-2.0 | A | 29 | Claude Code, Codex |
| dd-sourcingAbilityai/trinity | Skill | 741 | yesterday | Apache-2.0 | A | 29 | Claude Code, Codex |
| dd-verificationAbilityai/trinity | Skill | 741 | yesterday | Apache-2.0 | A | 35 | Claude Code, Codex |
| sagemath02loveslollipop/OpenCROW | Skill | 10 | yesterday | Apache-2.0 | A | 68 | Claude Code, Codex |