Borrowing it
Nothing to install: this file belongs to amkessler/nicar2026_skills_in_codex_claude. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/amkessler/nicar2026_skills_in_codex_claude/main/.claude/skills/weather-forecast/SKILL.mdgit clone --depth 1 https://github.com/amkessler/nicar2026_skills_in_codex_claudeWrote 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/amkessler/nicar2026_skills_in_codex_claude/weather-forecast)<a href="https://agentmods.dev/skills/amkessler/nicar2026_skills_in_codex_claude/weather-forecast"><img src="https://agentmods.dev/badge/skills/amkessler/nicar2026_skills_in_codex_claude/weather-forecast/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/amkessler/nicar2026_skills_in_codex_claude/weather-forecast"><img src="https://agentmods.dev/badge/skills/amkessler/nicar2026_skills_in_codex_claude/weather-forecast.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 | $0.00054 | $0.01392 |
| Opus 5 | $0.00027 | $0.00696 |
| Sonnet 5 | $0.00011 | $0.00278 |
| Haiku 4.5 | $0.00005 | $0.00139 |
Grade A, and why
weather-forecast 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 12d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weather Forecast Skill
This skill fetches 7-day weather forecasts from the Open-Meteo API and presents the data in both table and chart formats.
When to Use This Skill
Use this skill when:
- User requests a weather forecast for any location worldwide
- User wants to see temperature trends or weather data
- User asks for a visual representation of weather conditions
- User specifies a city name or coordinates
Standard Workflow - IMPORTANT
When given a city name, ALWAYS follow this two-step process:
-
Use get_coordinates.py to geocode the location (DO NOT use built-in knowledge)
uv run python skills/weather-forecast/scripts/get_coordinates.py "City, State" -
Use those coordinates with get_forecast.py
uv run python skills/weather-forecast/scripts/get_forecast.py <lat> <lon>
DO NOT: Hardcode coordinates from training data or external knowledge. Always use the get_coordinates.py script to ensure the skill is self-contained and reproducible.
Prerequisites
The script requires the requests library. Install if needed:
pip install requests --break-system-packages
Workflow
Option A: Using City Names (US Cities Only)
For the 1000 largest US cities, use the get_coordinates.py helper script:
Step 1: Get coordinates from city name
uv run python skills/weather-forecast/scripts/get_coordinates.py "City, State"
Accepts formats:
- "Philadelphia, PA" (city, state abbreviation)
- "Trenton, New Jersey" (city, full state name)
- "Denver" "CO" (separate arguments)
Step 2: Get forecast using the coordinates
# Combined workflow
uv run python skills/weather-forecast/scripts/get_forecast.py $(uv run python skills/weather-forecast/scripts/get_coordinates.py "Philadelphia, PA")
Option B: Using Coordinates Directly
For international locations or US cities not in the database:
Step 1: Get Coordinates
- Search the web for "{city name} coordinates" to find the lat/lon
- Or ask the user to provide coordinates directly
What ships with it
4 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.
- 12d ago First seen · 169 lines · 54 tokens per session scan A 0506f42957e5
weather-forecast is a skill published in the GitHub repository amkessler/nicar2026_skills_in_codex_claude (20 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 54 tokens to every session and 1,392 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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