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 artokun/comfyui-mcp --skill wan-multitalkgit clone --depth 1 https://github.com/artokun/comfyui-mcpWrote 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/artokun/comfyui-mcp/wan-multitalk)<a href="https://agentmods.dev/skills/artokun/comfyui-mcp/wan-multitalk"><img src="https://agentmods.dev/badge/skills/artokun/comfyui-mcp/wan-multitalk/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/artokun/comfyui-mcp/wan-multitalk"><img src="https://agentmods.dev/badge/skills/artokun/comfyui-mcp/wan-multitalk.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 | $0.00055 | $0.01451 |
| Opus 5 | $0.00028 | $0.00726 |
| Sonnet 5 | $0.00011 | $0.00290 |
| Haiku 4.5 | $0.00006 | $0.00145 |
Grade A, and why
wan-multitalk 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 5d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- wan-multitalk — 91% identical, 59 lines differ
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WAN MultiTalk — Audio-Driven Talking Avatar
Overview
MultiTalk (MeiGen-AI) drives a still portrait's lip-sync and head motion from an audio track. It runs on WAN 2.1 14B Image-to-Video via kijai's ComfyUI-WanVideoWrapper. Wav2Vec speech embeddings condition the WAN sampler so the mouth and expression follow the speech, while the lightx2v step-distill LoRA keeps it to a few sampling steps.
Use it for talking heads, dubbing, and single-speaker avatar clips (~10s at 480p).
It is distinct from wan-animate (pose/motion-driven character animation). This
is audio → lip-sync, not reference-video motion transfer.
Pack: wan-multitalk (480p, ~10s). Higher-res/longer variants exist in the source
bundle (720p, long-context) as VRAM/duration knobs on the same graph.
Pipeline (node graph)
LoadImage (portrait) ─┐
LoadAudio ─ AudioSeparation ─ AudioCrop ─ DownloadAndLoadWav2VecModel ─ MultiTalkWav2VecEmbeds ─┐
▼
WanVideoModelLoader (WAN 2.1 14B I2V GGUF) ─ MultiTalkModelLoader ─ WanVideoLoraSelect (lightx2v)
+ LoadWanVideoT5TextEncoder (umt5) + WanVideoTextEncode + WanVideoClipVisionEncode (clip_vision_h)
+ WanVideoVAELoader ──────────────────────────────────────────────────────────────────────────┘
▼
WanVideoImageToVideoMultiTalk ─ WanVideoSampler ─ WanVideoDecode ─ VHS_VideoCombine
Key nodes (all kijai WanVideoWrapper unless noted):
- DownloadAndLoadWav2VecModel. Auto-downloads the Wav2Vec speech model on first run (no manifest entry needed).
- MultiTalkWav2VecEmbeds. Turns the (separated, cropped) speech into the embeddings that steer the mouth and expression.
- MultiTalkModelLoader + WanVideoImageToVideoMultiTalk. The MultiTalk head on top of the WAN I2V model.
- AudioSeparation and AudioCrop (audio-separation-nodes-comfyui). Isolate the voice from music/noise before embedding and trim the segment you want to animate.
- ImageResizeKJv2 (KJNodes), VHS_VideoCombine (VideoHelperSuite). Resize and mux to mp4.
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.
- 5d ago Changed · +2 lines f3414ecdb693
- 9d ago First seen · 103 lines · 55 tokens per session scan A c60821102fa6
wan-multitalk is a skill published in the GitHub repository artokun/comfyui-mcp (730 stars, last pushed yesterday), licensed MIT. It adds 55 tokens to every session and 1,451 once invoked, about $0.0003 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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