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 _templategit 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/_template)<a href="https://agentmods.dev/skills/fullive-ai/anima/_template"><img src="https://agentmods.dev/badge/skills/fullive-ai/anima/_template/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/fullive-ai/anima/_template"><img src="https://agentmods.dev/badge/skills/fullive-ai/anima/_template.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 | $0.00033 | $0.00490 |
| Opus 5.5 | $0.00013 | $0.00196 |
| Sonnet 5.5 | $0.00007 | $0.00098 |
| Haiku 4.5 | $0.00003 | $0.00049 |
Grade A, and why
your-skill-name 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 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.
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
Your Skill
This folder is a starter template for a user-authored Anima skill.
Before you use it:
- Copy
_template/toskills/custom/<your-skill-name>/ - Rename
name:to a real skill name - Replace
your_device_typewith the device type your adapter emits - Update the references and scripts below
What This Skill Must Define
- When the skill should be used
- Which device types it supports
- What actions it is allowed to emit
- What counts as a safe no-op
- How preferences should be learned over time
Load These Resources
references/knowledge.mdfor domain rules, comfort ranges, and device interactions.references/decide.mdwhen generating a single-device action.references/learn.mdwhen updating the learned profile from usage history.scripts/actions.pyfor the runtime action helpers exposed to Anima.
Working Rules
- Keep the description in frontmatter concrete so the skill is easy to trigger.
- Only expose actions in
scripts/actions.pythat the target adapter can execute. - Prefer narrow, conservative decisions over broad generic prompts.
Fill-In Checklist
- Frontmatter:
Replace
your-skill-namewith a lowercase stable id such asfanorplant_watering. metadata.device_types: Must match thedevice.typevalues your adapter produces.references/knowledge.md: Add real target ranges, risk conditions, and interactions.references/decide.md: Keep the output schema strict. Do not let the model invent actions.references/learn.md: Keep the output structured. Avoid free-form essays.scripts/actions.py: Only keep helpers that are valid for your device.
Common Mistakes
- Using action names that no adapter actually supports
- Forgetting to define when the correct decision is
none - Writing vague descriptions like "smart control for devices"
- Mixing multiple unrelated device types into one skill
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.
- 1mo ago First seen · 63 lines · 33 tokens per session scan A 83c11f942e90
your-skill-name is a skill published in the GitHub repository Fullive-AI/Anima (636 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 33 tokens to every session and 490 once invoked, about $0.0001 per session on Opus 5.5. 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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