How do I install the Weather skill?

Run npx skillmds@latest add seokhoonj/weather in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Weather safe to use?

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Which AI agents work with Weather?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Weather free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Weather?

seokhoonj (@seokhoonj) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Weather by seokhoonj — Agent Skill

Name: Weather
Author: seokhoonj

Weather

Fetch 기상청 동네예보 (KMA village weather forecast) from data.go.kr (service 1360000) -- the 단기예보 (`forecast`, ~3 days), 초단기예보 (`ultra_forecast`, 6 hours), and 초단기실황 (`nowcast`, latest observation) for one 5km grid cell (nx, ny). Holds no logic of its own -- it calls the pydatagokr package's CLI (`datagokr weather`) and shows the result to the user. Trigger phrases: 날씨, 동네예보, 단기예보, 초단기예보, 기온, 강수확률, 하늘상태, weather forecast, temperature, rain probability.

seokhoonj 4bce9ec 3.6 KB Updated Sep 17, 2026

File contents

datagokr — 기상청 동네예보

Fetch a grid cell's forecast or observation. The result is long -- one row per weather item (category) per time. Clean columns: base_date, base_time, category, forecast_date/forecast_time + forecast_value (forecasts) or observed_value (nowcast), nx, ny. The value's meaning depends on category:

category	항목	category	항목
TMP / T1H	기온 (℃)	POP	강수확률 (%)
SKY	하늘상태 (1 맑음·3 구름많음·4 흐림)	PTY	강수형태 (0 없음·1 비·2 비/눈·3 눈)
REH	습도 (%)	WSD	풍속 (m/s)
PCP / RN1	강수량 (mm)	SNO	적설 (cm)

operation	예보
`forecast`	단기예보 (~3일)
`ultra_forecast`	초단기예보 (6시간)
`nowcast`	초단기실황 (현재 관측)

Prerequisite

pipx install pydatagokr      # or: pip install pydatagokr

Never print the key value (the DATAGOKR_API_KEY env var or credentials.json) to output, logs, or a summary -- if you need to check which form it is (encoding vs decoding), ask the user.

A data.go.kr decoding key must be configured (env DATAGOKR_API_KEY or ~/.config/pydatagokr/credentials.json), and the 단기예보 dataset (service 1360000, VilageFcstInfoService_2.0) applied for (활용신청) on that account.

Running

datagokr weather <operation> --nx NX --ny NY [--base-date YYYYMMDD --base-time HHMM] [--json]

--base-date/--base-time: the announcement time. Omit both to use the latest published announcement for the operation. 단기예보 is issued at 0200/0500/0800/1100/1400/1700/2000/ 2300; 초단기예보 (ultra_forecast) hourly at HH30; 초단기실황 (nowcast) hourly at HH00. Pass both or neither.
--nx/--ny: the 기상청 5km grid coordinates (서울 시청 ≈ 60/127).

Procedure

Resolve the grid cell. Convert the location to nx/ny with the offline resolver -- datagokr grid 37.5714 126.9658 -> 60 127. (It takes decimal-degree lat/lon; if you only have a place name, get its lat/lon first.) Omit base_date/base_time for the latest announcement, or pick a valid pair for the operation.

Run.

datagokr weather forecast --nx 60 --ny 127          # latest announcement

Add --json for machine-readable data.

Relay the result. The rows are long; group by forecast_date/forecast_time and read forecast_value per category (translate the codes above for the user).
Error handling. A one-line datagokr: <message> on stderr:
- a [30]/[20] auth error -> the key is wrong, is the encoding form by mistake, or service 1360000 is not applied for yet.
- an empty result (no rows) usually means a base_time with no issued forecast yet.

What this skill does not do

It does not re-implement fetching or parsing (the package does); it always calls the CLI.
It converts a lat/lon to the KMA grid via datagokr grid; it does not geocode a place name to lat/lon -- supply the coordinates.

---name: weatherdescription: Fetch 기상청 동네예보 (KMA village weather forecast) from data.go.kr (service 1360000) -- the 단기예보 (`forecast`, ~3 days), 초단기예보 (`ultra_forecast`, 6 hours), and 초단기실황 (`nowcast`, latest observation) for one 5km grid cell (nx, ny). Holds no logic of its own -- it calls the pydatagokr package's CLI (`datagokr weather`) and shows the result to the user. Trigger phrases: 날씨, 동네예보, 단기예보, 초단기예보, 기온, 강수확률, 하늘상태, weather forecast, temperature, rain probability.---# datagokr — 기상청 동네예보Fetch a grid cell's forecast or observation. The result is **long** -- one row per weatheritem (`category`) per time. Clean columns: `base_date`, `base_time`, `category`,`forecast_date`/`forecast_time` + `forecast_value` (forecasts) or `observed_value` (nowcast),`nx`, `ny`. The value's meaning depends on `category`:| category | 항목 | category | 항목 ||---|---|---|---|| TMP / T1H | 기온 (℃) | POP | 강수확률 (%) || SKY | 하늘상태 (1 맑음·3 구름많음·4 흐림) | PTY | 강수형태 (0 없음·1 비·2 비/눈·3 눈) || REH | 습도 (%) | WSD | 풍속 (m/s) || PCP / RN1 | 강수량 (mm) | SNO | 적설 (cm) || operation | 예보 ||---|---|| `forecast` | 단기예보 (~3일) || `ultra_forecast` | 초단기예보 (6시간) || `nowcast` | 초단기실황 (현재 관측) |## Prerequisite```pipx install pydatagokr # or: pip install pydatagokr```**Never print the key value** (the `DATAGOKR_API_KEY` env var or `credentials.json`) to output, logs, or a summary -- if you need to check which form it is (encoding vs decoding), ask the user.A data.go.kr **decoding** key must be configured (env `DATAGOKR_API_KEY` or`~/.config/pydatagokr/credentials.json`), and the 단기예보 dataset (service 1360000,VilageFcstInfoService_2.0) applied for (활용신청) on that account.## Running```datagokr weather <operation> --nx NX --ny NY [--base-date YYYYMMDD --base-time HHMM] [--json]```- `--base-date`/`--base-time`: the announcement time. **Omit both to use the latest published announcement** for the operation. 단기예보 is issued at 0200/0500/0800/1100/1400/1700/2000/ 2300; 초단기예보 (`ultra_forecast`) hourly at HH30; 초단기실황 (`nowcast`) hourly at HH00. Pass both or neither.- `--nx`/`--ny`: the 기상청 5km grid coordinates (서울 시청 ≈ 60/127).## Procedure1. **Resolve the grid cell.** Convert the location to `nx`/`ny` with the offline resolver -- `datagokr grid 37.5714 126.9658` -> `60 127`. (It takes decimal-degree lat/lon; if you only have a place name, get its lat/lon first.) Omit `base_date`/`base_time` for the latest announcement, or pick a valid pair for the operation.2. **Run.** ```bash datagokr weather forecast --nx 60 --ny 127 # latest announcement ``` Add `--json` for machine-readable data.3. **Relay the result.** The rows are long; group by `forecast_date`/`forecast_time` and read `forecast_value` per `category` (translate the codes above for the user).4. **Error handling.** A one-line `datagokr: <message>` on stderr: - a `[30]`/`[20]` auth error -> the key is wrong, is the *encoding* form by mistake, or service 1360000 is not applied for yet. - an empty result (no rows) usually means a `base_time` with no issued forecast yet.## What this skill does not do- It does not re-implement fetching or parsing (the package does); it always calls the CLI.- It converts a lat/lon to the KMA grid via `datagokr grid`; it does not geocode a place name to lat/lon -- supply the coordinates.

Frequently asked questions