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 cinience/alicloud-skills --skill aliyun-emogit clone --depth 1 https://github.com/cinience/alicloud-skillsWrote 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/cinience/alicloud-skills/aliyun-emo)<a href="https://agentmods.dev/skills/cinience/alicloud-skills/aliyun-emo"><img src="https://agentmods.dev/badge/skills/cinience/alicloud-skills/aliyun-emo/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/cinience/alicloud-skills/aliyun-emo"><img src="https://agentmods.dev/badge/skills/cinience/alicloud-skills/aliyun-emo.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Rogue Agent · line 14 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
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 | $0.00709 |
| Opus 5.5 | $0.00020 | $0.00284 |
| Sonnet 5.5 | $0.00010 | $0.00142 |
| Haiku 4.5 | $0.00005 | $0.00071 |
Grade A, and why
aliyun-emo 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 2mo 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.
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Category: provider
Model Studio EMO
Validation
mkdir -p output/aliyun-emo
python -m py_compile skills/ai/video/aliyun-emo/scripts/prepare_emo_request.py && echo "py_compile_ok" > output/aliyun-emo/validate.txt
Pass criteria: command exits 0 and output/aliyun-emo/validate.txt is generated.
Output And Evidence
- Save normalized request payloads, detection boxes, and task polling snapshots under
output/aliyun-emo/. - Record the chosen
style_leveland the exactface_bbox/ext_bbox.
Use EMO when the input is a portrait image and speech audio, and you need a non-Wan expressive talking-head result.
Critical model names
Use these exact model strings:
emo-v1-detectemo-v1
Selection guidance:
- Run image detection first to obtain
face_bboxandext_bbox. - Use
emo-v1only after detection succeeds.
Prerequisites
- China mainland (Beijing) only.
- Set
DASHSCOPE_API_KEYin your environment, or adddashscope_api_keyto~/.alibabacloud/credentials. - Input files must be public HTTP/HTTPS URLs.
Normalized interface (video.emo)
Detect Request
model(string, optional): defaultemo-v1-detectimage_url(string, required)
Generate Request
model(string, optional): defaultemo-v1image_url(string, required)audio_url(string, required)face_bbox(array, required)ext_bbox(array, required)style_level(string, optional):normal,calm, oractive
Response
task_id(string)task_status(string)video_url(string, when finished)
Quick start
python skills/ai/video/aliyun-emo/scripts/prepare_emo_request.py \
--image-url "https://example.com/portrait.png" \
--audio-url "https://example.com/speech.mp3" \
--face-bbox 302,286,610,593 \
--ext-bbox 71,9,840,778 \
--style-level active
Operational guidance
- Do not invent
face_bboxorext_bbox; use the detection API output. ext_bboxratio determines output format:1:1yields512x512,3:4yields512x704.- Keep the input portrait clear and front-facing for better expression quality.
- EMO is portrait-focused; for full-scene human videos use other skills instead.
What ships with it
3 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.
- 2mo ago First seen · 88 lines · 49 tokens per session scan A 5180ec8d8e36
aliyun-emo is a skill published in the GitHub repository cinience/alicloud-skills (396 stars, last pushed 2mo ago), licensed MIT. It adds 49 tokens to every session and 709 once invoked, about $0.0002 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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