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 RikyZ90/ShibaClaw --skill weathergit clone --depth 1 https://github.com/RikyZ90/ShibaClawWrote 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/rikyz90/shibaclaw/weather)<a href="https://agentmods.dev/skills/rikyz90/shibaclaw/weather"><img src="https://agentmods.dev/badge/skills/rikyz90/shibaclaw/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/rikyz90/shibaclaw/weather"><img src="https://agentmods.dev/badge/skills/rikyz90/shibaclaw/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 | $0.00013 | $0.00390 |
| Opus 5.5 | $0.00005 | $0.00156 |
| Sonnet 5.5 | $0.00003 | $0.00078 |
| Haiku 4.5 | $0.00001 | $0.00039 |
Grade A, and why
weather scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
metadata: {"shibaclaw":{"emoji":"🌤️","requires":{"bins":["curl"]}}} This is a copy
89% identical to weather — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Weather
Two free services, no API keys needed.
wttr.in (primary)
Quick one-liner:
curl -s "wttr.in/London?format=3"
# Output: London: ⛅️ +8°C
Compact format:
curl -s "wttr.in/London?format=%l:+%c+%t+%h+%w"
# Output: London: ⛅️ +8°C 71% ↙5km/h
Full forecast:
curl -s "wttr.in/London?T"
Format codes: %c condition · %t temp · %h humidity · %w wind · %l location · %m moon
Tips:
- URL-encode spaces:
wttr.in/New+York - Airport codes:
wttr.in/JFK - Units:
?m(metric)?u(USCS) - Today only:
?1· Current only:?0 - PNG:
curl -s "wttr.in/Berlin.png" -o /tmp/weather.png
Open-Meteo (fallback, JSON)
Free, no key, good for programmatic use:
curl -s "https://api.open-meteo.com/v1/forecast?latitude=51.5&longitude=-0.12¤t_weather=true"
Find coordinates for a city, then query. Returns JSON with temp, windspeed, weathercode.
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.
- 1mo ago First seen · 50 lines · 13 tokens per session scan A b8929436ad2f
weather is a skill published in the GitHub repository RikyZ90/ShibaClaw (84 stars, last pushed yesterday), licensed Apache-2.0. It adds 13 tokens to every session and 390 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 89% identical to weather, differing in 3 lines, and is treated as a copy.
Other skills, from other repositories
python-package-management
Guide for managing packages in the Agent Framework Python monorepo, including creating new connector packages, versioning, and the lazy-loading pattern. Use this when adding, modifying, or releasing packages.
python-feature-lifecycle
Guidance for package and feature lifecycle in the Agent Framework Python codebase, including stage meanings, feature-stage decorators, feature enums, and how to move APIs from one stage to the next.
python-code-quality
Code quality checks, linting, formatting, and type checking commands for the Agent Framework Python codebase. Use this when running checks, fixing lint errors, or troubleshooting CI failures.
building-pydantic-ai-agents
Build AI agents with Pydantic AI — tools, capabilities (including on-demand loading), workspaces, structured output, streaming, testing, and multi-agent patterns. Use when the user mentions Pydantic AI, imports pydanticai, or asks to build an AI agent, add tools/capabilities, attach a workspace, defer capability…
python-development
Workflow guidance for developing Python code in the Agent Framework repository. Use this when writing or modifying Python source files under python/.
migrating-langchain-to-pydantic-ai
Migrate Python LangChain, LangGraph, or Deep Agents applications to Pydantic AI and, when the source uses harness features, Pydantic AI Harness. Use for LangChain agents, chains, LCEL, direct LangGraph graphs, persistence, interrupts, streaming, and createdeepagent projects with planning, filesystem or sandbox…