Atomic Agent is a local-first AI agent that runs its control loop and state on a user's machine while using local or cloud models. It drives browsers, edits files, runs approved commands, remembers context, schedules follow-ups, and connects to external tools, with the catalogue providing skills for its use.
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 AtomicBot-ai/atomic-agent --skill wttr-weathergit clone --depth 1 https://github.com/AtomicBot-ai/atomic-agentWrote 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/atomicbot-ai/atomic-agent/wttr-weather)<a href="https://agentmods.dev/skills/atomicbot-ai/atomic-agent/wttr-weather"><img src="https://agentmods.dev/badge/skills/atomicbot-ai/atomic-agent/wttr-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/atomicbot-ai/atomic-agent/wttr-weather"><img src="https://agentmods.dev/badge/skills/atomicbot-ai/atomic-agent/wttr-weather.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 | $0.00025 | $0.00191 |
| Opus 5 | $0.00013 | $0.00096 |
| Sonnet 5 | $0.00005 | $0.00038 |
| Haiku 4.5 | $0.00003 | $0.00019 |
Grade A, and why
wttr-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 9d 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.
No `wttr` binary. No `skill.run_script`. Fallback: `curl -fsS --max-time 25 '<url>'` via `os.shell.run`. What it actually says
wttr-weather
os.http.request GET (allowlist must include wttr.in if not null):
https://wttr.in/<Place>?format=3— one-line texthttps://wttr.in/<Place>?format=j1— JSON- No place:
https://wttr.in/?format=3 - Metric/imperial: append
&mor&u
No wttr binary. No skill.run_script. Fallback: curl -fsS --max-time 25 '<url>' via os.shell.run.
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.
- 9d ago First seen · 21 lines · 25 tokens per session scan A 0bd3accc3ca2
wttr-weather is a skill published in the GitHub repository AtomicBot-ai/atomic-agent (2,513 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 191 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
harness-init-runner
Initialize a lightweight repo-local Node.js harness (harness/ + .harness/) WITHOUT AIOS dependency. Use ONLY when you need a standalone, portable harness. If AIOS is installed, use aios-long-running-harness instead — it has rex Command hosting, ContextDB integration, and checkpoint recovery.
manage-llm-server
Inspect and control the local LLM server(s) via llama-launcher — list profiles, see what's running, switch models, tail logs. Use when the user wants to know what model is loaded, change models, start/stop or restart the server, or troubleshoot a stuck load.
opencli-usage
Use at the start of any OpenCLI session — this is the top-level map of what opencli can do, how to discover adapters, what flags and output formats are universal, and which specialized skill to load next. Point here when an agent asks "what can opencli do?" or "how do I find the right command?".
opencli-sitemap-author
Use when creating or maintaining OpenCLI site sitemaps: agent-facing navigation, page-state, action, workflow, API-reference, pitfall, and fallback knowledge for a website. Use after browser exploration discovers durable site context, when a sitemap is stale, or when promoting local site knowledge into the repo.
Agent Browser Automation
Fast Rust-based headless browser automation CLI with Node.js fallback for AI agents, featuring navigation, clicking, typing, snapshots, and structured commands optimized for agent workflows.
agent-desktop
Use the built-in Computer sub-agent with agent-desktop for macOS desktop automation. Apply when a task needs application launching, accessibility snapshots, stable element refs, window focusing, semantic clicks/typing, or visual confirmation outside the browser sandbox.