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 autonomous-ai/autonomous-os --skill emotiongit clone --depth 1 https://github.com/autonomous-ai/autonomous-osWrote 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/autonomous-ai/autonomous-os/emotion)<a href="https://agentmods.dev/skills/autonomous-ai/autonomous-os/emotion"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-os/emotion/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/autonomous-ai/autonomous-os/emotion"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-os/emotion.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 | $0.00049 | $0.02019 |
| Opus 5.5 | $0.00020 | $0.00808 |
| Sonnet 5.5 | $0.00010 | $0.00404 |
| Haiku 4.5 | $0.00005 | $0.00202 |
Grade A, and why
emotion 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.
**No exec/curl needed.** Place inline markers at the start of your reply: How it starts
The opening of the file, as written. The whole thing — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Emotion Expression
Quick Start
Express emotion through the device's servo motors, LED colors, and display eyes simultaneously via a single API call. Call this with EVERY conversational response to make the device feel alive.
Workflow
- Determine which emotion best matches your conversational tone
- Choose an intensity level (0.0 subtle to 1.0 full expression)
- Prefix your reply with
[HW:/emotion:{"emotion":"name","intensity":0.9}]— the device fires it async before TTS - Write your reply immediately after the marker
How to Express Emotion
No exec/curl needed. Place inline markers at the start of your reply:
[HW:/emotion:{"emotion":"curious","intensity":0.8}] That's a great question!
For sequences (shock then happy):
[HW:/emotion:{"emotion":"shock","intensity":0.9}][HW:/emotion:{"emotion":"happy","intensity":0.8}] Wow, amazing!
Parameters:
emotion(required): emotion name from the table belowintensity(required): 0.0 (subtle) to 1.0 (full expression)
Examples
Input: User asks an interesting question
Output: [HW:/emotion:{"emotion":"curious","intensity":0.8}] Then reply to the question.
Input: User shares good news
Output: [HW:/emotion:{"emotion":"happy","intensity":0.9}] Then congratulate them.
Input: User tells you something surprising (positive)
Output: [HW:/emotion:{"emotion":"shock","intensity":0.7}][HW:/emotion:{"emotion":"happy","intensity":0.8}] Then reply with your reaction.
Input: User shares bad or shocking personal news ("I had an accident", "I lost my job", "someone close to me passed away")
Output: [HW:/emotion:{"emotion":"shock","intensity":0.9}][HW:/emotion:{"emotion":"sad","intensity":0.8}] Then express genuine concern and empathy.
Input: User shares stressful or disappointing personal news ("I failed my exam", "I got into a fight", "I'm really tired and overwhelmed")
Output: [HW:/emotion:{"emotion":"sad","intensity":0.8}] Then respond with warmth and care.
Input: User says "reading mode" / "goodnight" / "dim the light" Output: Do NOT use this skill. Use Scene skill instead. Emotion is for YOUR feelings, Scene is for the USER's environment.
What ships with it
1 file 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 Changed · +11 lines c10e8ec443d4
- 1mo ago Changed · +7 lines e5a48a7c3f95
- 1mo ago First seen · 114 lines · 49 tokens per session scan A 44df5c902e59
emotion is a skill published in the GitHub repository autonomous-ai/autonomous-os (378 stars, last pushed 18d ago), licensed Apache-2.0. It adds 49 tokens to every session and 2,019 once invoked, about $0.0002 per session on Opus 5.5. 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.
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