Anima is an open-source local-network runtime that gives household hardware an AI layer for sensing environments, remembering preferences, planning actions, and controlling devices through adapters. It is intended for intelligent devices such as lights, air conditioners, humidifiers, air purifiers, and speakers, with a dashboard, REST API, and CLI for control and extension. The catalogue entries are skills, rules, and instructions for working with Anima.
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 Fullive-AI/Anima --skill air_conditionergit clone --depth 1 https://github.com/Fullive-AI/AnimaWrote 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/fullive-ai/anima/air_conditioner)<a href="https://agentmods.dev/skills/fullive-ai/anima/air_conditioner"><img src="https://agentmods.dev/badge/skills/fullive-ai/anima/air_conditioner.svg" alt="Measured on agentmods" 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 | $0.00045 | $0.00204 |
| Opus 5 | $0.00023 | $0.00102 |
| Sonnet 5 | $0.00009 | $0.00041 |
| Haiku 4.5 | $0.00005 | $0.00020 |
Grade A, and why
air_conditioner 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 8d 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
Air Conditioner
Use this skill for device-level cooling and heating decisions plus preference learning.
Load These Resources
references/knowledge.mdfor comfort temperatures, energy heuristics, and device interaction rules.references/decide.mdwhen generating a single-device action.references/learn.mdwhen updating the learned profile from usage history.scripts/actions.pyfor the structured action helpers exposed to the runtime.
Working Rules
- Optimize for comfort without ignoring energy cost.
- Treat humidity side effects as part of the decision, not a separate concern.
- Prefer no-op over unnecessary cycling near the target range.
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.
- 8d ago First seen · 26 lines · 45 tokens per session scan A e1920eeec0f8
air_conditioner is a skill published in the GitHub repository Fullive-AI/Anima (1,049 stars, last pushed 18d ago), licensed Apache-2.0. It adds 45 tokens to every session and 204 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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