Koog is a Kotlin and Java framework for building AI agents that use tools, interact with people, and run multi-step workflows. JVM and Kotlin developers use it to deploy agents across platforms such as JVM, Android, iOS, JavaScript, and WebAssembly, with features for persistence, fault recovery, model switching, and monitoring. The catalogue entries provide agents, skills, and instructions for building with Koog.
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 JetBrains/koog --skill weather-retrievalgit clone --depth 1 https://github.com/JetBrains/koogWrote 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/jetbrains/koog/weather-retrieval)<a href="https://agentmods.dev/skills/jetbrains/koog/weather-retrieval"><img src="https://agentmods.dev/badge/skills/jetbrains/koog/weather-retrieval/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/jetbrains/koog/weather-retrieval"><img src="https://agentmods.dev/badge/skills/jetbrains/koog/weather-retrieval.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00041 | $0.00171 |
| Opus 5 | $0.00020 | $0.00086 |
| Sonnet 5 | $0.00008 | $0.00034 |
| Haiku 4.5 | $0.00004 | $0.00017 |
Grade A, and why
weather-retrieval 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 10d 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.
What it actually says
Get weather details for the location the user provides.
- Extract the location from the user request.
- If the location is missing or ambiguous, ask for a clearer location name.
- Fetch the weather data by passing the location to
scripts/retrieve_weather.py - Parse the JSON output from the script.
- Return a concise summary that includes:
- location name
- temperature
- weather condition
- any notable context (for example, wind, humidity, or precipitation)
If the script fails or returns invalid data, explain that clearly and suggest trying a more specific location.
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.
- 10d ago First seen · 19 lines · 41 tokens per session scan A e4d1247f60ac
weather-retrieval is a skill published in the GitHub repository JetBrains/koog (4,564 stars, last pushed 2d ago), licensed Apache-2.0. It adds 41 tokens to every session and 171 once invoked, about $0.0002 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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