Gemini Live API
google/skills
Generates a Gemini LiveAPI client service class in the user's chosen programming language.
Build WAN 2.2 Text-to-Video workflows. An agent skill from artokun/comfyui-mcp.
$ npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install artokun/comfyui-mcp wan-t2v-video --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .claude/skills && cp -r skills-src/plugin/skills/wan-t2v-video .claude/skills/wan-t2v-video && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "wan-t2v-video" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-t2v-video into .claude/skills/wan-t2v-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wan-t2v-video", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-t2v-videoType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install artokun/comfyui-mcp wan-t2v-video --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .agents/skills && cp -r skills-src/plugin/skills/wan-t2v-video .agents/skills/wan-t2v-video && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "wan-t2v-video" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-t2v-video into .agents/skills/wan-t2v-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wan-t2v-video", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install artokun/comfyui-mcp wan-t2v-video --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/plugin/skills/wan-t2v-video .cursor/skills/wan-t2v-video && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "wan-t2v-video" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-t2v-video into .cursor/skills/wan-t2v-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wan-t2v-video", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/artokun/comfyui-mcp.git --path plugin/skills/wan-t2v-video--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install artokun/comfyui-mcp wan-t2v-video --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/plugin/skills/wan-t2v-video .gemini/skills/wan-t2v-video && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "wan-t2v-video" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-t2v-video into .gemini/skills/wan-t2v-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wan-t2v-video", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install artokun/comfyui-mcp wan-t2v-videoInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .github/skills && cp -r skills-src/plugin/skills/wan-t2v-video .github/skills/wan-t2v-video && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "wan-t2v-video" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-t2v-video into .github/skills/wan-t2v-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wan-t2v-video", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install artokun/comfyui-mcp wan-t2v-video --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/artokun/comfyui-mcp.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/plugin/skills/wan-t2v-video .opencode/skills/wan-t2v-video && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "wan-t2v-video" agent skill from https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-t2v-video into .opencode/skills/wan-t2v-video/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "wan-t2v-video", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
wan-t2v-videoBuild WAN 2.2 Text-to-Video workflows. An agent skill from artokun/comfyui-mcp.
Wan T2v Video is an agent skill from artokun/comfyui-mcp. Build WAN 2.2 Text-to-Video workflows. Dual hi-lo models, lightning LoRAs, VACE modules, and KSamplerAdvanced two-pass
Its SKILL.md is about 3.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in AI & LLM Engineering, covering Fine-tuning and AI video generation. The repository describes itself as: Local-first, agent-native control plane for ComfyUI — MCP server + sidebar agent that generates images, video & audio, authors and runs workflows, and edits your live graph in… The licence is MIT.
Read from SKILL.md and the folder at commit 6ad6fc0. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are json).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Wan T2v Video loads about 3.3k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 864 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from artokun/comfyui-mcp at commit 6ad6fc0, republished under its MIT licence (© artokun). 864 words, ~3,296 tokens.
.claude/skills/wan-t2v-video/SKILL.md (or your agent's skills folder).WAN 2.2 T2V generates videos from text prompts using a 14B parameter MoE (Mixture of Experts) architecture split across two specialized models:
This dual-model technique is the same as FLF/I2V (see wan-flf-video skill) but without image conditioning nodes.
Key difference from I2V/FLF: T2V does NOT use CLIPVisionEncode, WanFirstLastFrameToVideo, or any image input. It uses EmptyHunyuanLatentVideo for latent initialization and text-only conditioning.
| Model | Loader | Notes |
|---|---|---|
Wan2_2-T2V-A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensors | UNETLoader | HighNoise expert, 14.3GB FP8 |
Wan2_2-T2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors | UNETLoader | LowNoise expert, 14.3GB FP8 |
| Component | Node | Model | Notes |
|---|---|---|---|
| CLIP (T5) | CLIPLoader (type=wan) | umt5_xxl_fp8_e4m3fn_scaled.safetensors | UMT5-XXL fp8, in clip/ |
| Component | Node | Model |
|---|---|---|
| VAE | VAELoader | wan_2.1_vae.safetensors |
| Model | Size | Notes |
|---|---|---|
Wan2_2_Fun_VACE_module_A14B_HIGH_bf16.safetensors | 5.8GB | HighNoise VACE module |
Wan2_2_Fun_VACE_module_A14B_LOW_bf16.safetensors | 5.8GB | LowNoise VACE module |
VACE modules add reference image / pose / depth conditioning to T2V. See WanVideoWrapper section below.
| LoRA | Applies To | Path |
|---|---|---|
wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise | HighNoise UNET | Unknown/no tags/ |
wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise | LowNoise UNET | Unknown/no tags/ |
| LoRA | Path |
|---|---|
Wan2.2_HN_T2V_Lightning_4steps-lora-rank64-Seko_V2.0_HIGH | Root loras/ |
Wan2.2_HN_T2V_Lightning_4steps-lora-rank64-Seko_V2.0_LOW | Root loras/ |
| LoRA | Path | Notes |
|---|---|---|
lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank128_bf16 | Root loras/ | CFG+step distilled, use with more steps |
| Parameter | Pass 1 (Hi) | Pass 2 (Lo) |
|---|---|---|
| model | Hi + Hi Lightning LoRA | Lo + Lo Lightning LoRA |
| add_noise | enable | disable |
| steps | 4 | 4 |
| cfg | 1.0 | 1.0 |
| sampler_name | euler | euler |
| scheduler | simple | simple |
| start_at_step | 0 | 2 |
| end_at_step | 2 | 4 |
| return_with_leftover_noise | enable | disable |
| Parameter | Pass 1 (Hi) | Pass 2 (Lo) |
|---|---|---|
| model | Hi + ModelSamplingSD3 (shift=8) | Lo + ModelSamplingSD3 (shift=8) |
| add_noise | enable | disable |
| steps | 20 | 20 |
| cfg | 3.5 | 3.5 |
| sampler_name | euler | euler |
| scheduler | simple | simple |
| start_at_step | 0 | 10 |
| end_at_step | 10 | 20 |
| return_with_leftover_noise | enable | disable |
Required for WAN 2.2 flow matching. Apply to BOTH models:
{
"class_type": "ModelSamplingSD3",
"inputs": { "model": ["<unet>", 0], "shift": 8 }
}T2V shift values:
Creates the initial video latent for T2V (no image input):
{
"class_type": "EmptyHunyuanLatentVideo",
"inputs": {
"width": 832,
"height": 480,
"length": 81,
"batch_size": 1
}
}This replaces WanFirstLastFrameToVideo (which is for FLF/I2V only). The latent goes directly to KSamplerAdvanced Pass 1.
The tones are vibrant, overexposed, static, details are unclear, subtitles, style, work, painting, image, still, overall grayish, worst quality, low quality, JPEG compression artifacts, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, distorted limbs, merged fingers, motionless image, cluttered background, three legs, many people in the background, walking backwardsUNETLoader (HIGH T2V) → ModelSamplingSD3 (shift) → LoraLoaderModelOnly (Hi Lightning) → MODEL_HI
UNETLoader (LOW T2V) → ModelSamplingSD3 (shift) → LoraLoaderModelOnly (Lo Lightning) → MODEL_LO
CLIPLoader (wan) → CLIP
├─ CLIPTextEncode (positive) → CONDITIONING
└─ CLIPTextEncode (negative) → CONDITIONING
VAELoader → VAE
EmptyHunyuanLatentVideo (832x480, 81 frames) → LATENT
KSamplerAdvanced (Hi: MODEL_HI, steps 0-2, add_noise=enable, return_leftover=enable)
→ noisy LATENT
KSamplerAdvanced (Lo: MODEL_LO, steps 2-4, add_noise=disable, return_leftover=disable)
→ final LATENT
VAEDecode → IMAGE → VHS_VideoCombine → MP4{
"1": { "class_type": "UNETLoader", "inputs": { "unet_name": "Wan2_2-T2V-A14B_HIGH_fp8_e4m3fn_scaled_KJ.safetensors", "weight_dtype": "default" }, "_meta": { "title": "UNET HighNoise T2V" }},
"2": { "class_type": "UNETLoader", "inputs": { "unet_name": "Wan2_2-T2V-A14B-LOW_fp8_e4m3fn_scaled_KJ.safetensors", "weight_dtype": "default" }, "_meta": { "title": "UNET LowNoise T2V" }},
"3": { "class_type": "ModelSamplingSD3", "inputs": { "model": ["1", 0], "shift": 5 }, "_meta": { "title": "Hi Shift" }},
"4": { "class_type": "ModelSamplingSD3", "inputs": { "model": ["2", 0], "shift": 5 }, "_meta": { "title": "Lo Shift" }},
"5": { "class_type": "LoraLoaderModelOnly", "inputs": {
"model": ["3", 0],
"lora_name": "Unknown\\no tags\\wan2.2_t2v_lightx2v_4steps_lora_v1.1_high_noise.safetensors",
"strength_model": 1.0
}, "_meta": { "title": "Hi Lightning" }},
"6": { "class_type": "LoraLoaderModelOnly", "inputs": {
"model": ["4", 0],
"lora_name": "Unknown\\no tags\\wan2.2_t2v_lightx2v_4steps_lora_v1.1_low_noise.safetensors",
"strength_model": 1.0
}, "_meta": { "title": "Lo Lightning" }},
"7": { "class_type": "CLIPLoader", "inputs": { "clip_name": "umt5_xxl_fp8_e4m3fn_scaled.safetensors", "type": "wan" }},
"8": { "class_type": "VAELoader", "inputs": { "vae_name": "wan_2.1_vae.safetensors" }},
"9": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["7", 0], "text": "<positive prompt describing the video scene and motion>" }, "_meta": { "title": "Positive" }},
"10": { "class_type": "CLIPTextEncode", "inputs": { "clip": ["7", 0], "text": "The tones are vibrant, overexposed, static, details are unclear, subtitles, worst quality, low quality, motionless image" }, "_meta": { "title": "Negative" }},
"11": { "class_type": "EmptyHunyuanLatentVideo", "inputs": {
"width": 832, "height": 480, "length": 81, "batch_size": 1
}},
"12": { "class_type": "KSamplerAdvanced", "inputs": {
"model": ["5", 0],
"positive": ["9", 0],
"negative": ["10", 0],
"latent_image": ["11", 0],
"add_noise": "enable", "noise_seed": 0, "steps": 4, "cfg": 1,
"sampler_name": "euler", "scheduler": "simple",
"start_at_step": 0, "end_at_step": 2, "return_with_leftover_noise": "enable"
}, "_meta": { "title": "Hi Pass" }},
"13": { "class_type": "KSamplerAdvanced", "inputs": {
"model": ["6", 0],
"positive": ["9", 0],
"negative": ["10", 0],
"latent_image": ["12", 0],
"add_noise": "disable", "noise_seed": 0, "steps": 4, "cfg": 1,
"sampler_name": "euler", "scheduler": "simple",
"start_at_step": 2, "end_at_step": 4, "return_with_leftover_noise": "disable"
}, "_meta": { "title": "Lo Pass" }},
"14": { "class_type": "VAEDecode", "inputs": { "samples": ["13", 0], "vae": ["8", 0] }},
"15": { "class_type": "VHS_VideoCombine", "inputs": {
"images": ["14", 0], "frame_rate": 16, "loop_count": 0,
"filename_prefix": "wan_t2v", "format": "video/h264-mp4",
"pingpong": false, "save_output": true,
"pix_fmt": "yuv420p", "crf": 19, "save_metadata": true, "trim_to_audio": false
}}
}Same structure as above but replace the LoRA and sampler settings:
shift to 8 in ModelSamplingSD3 nodessteps: 20, cfg: 3.5start_at_step: 0, end_at_step: 10start_at_step: 10, end_at_step: 20For more control, use the WanVideoWrapper custom node pack. Key differences from native:
WanVideoModelLoader → WANVIDEOMODEL typeWanVideoSampler with built-in shift parameterWanVideoLoraSelect → WanVideoModelLoader lora inputmerge_loras=false with fp8-scaled modelsWhen a LoRA (e.g. the lightx2v 4-step lightning hi/lo LoRAs) is loaded onto an
fp8-quantized model (quantization=fp8_e4m3fn_scaled) via WanVideoLoraSelect,
set the node's merge_loras widget to false. The default merge_loras=true
tries to bake the LoRA into the already-quantized fp8 weights and hard-crashes
ComfyUI during LoRA loading with no Python traceback (the process dies, which looks
like an unexplained restart/OOM). merge_loras=false applies the LoRA as a runtime
patch instead, which is fp8-safe. Use merge_loras=true only on non-quantized
bf16/fp16 models. Pairs cleanly with WanVideoBlockSwap for fp8 14B on 24GB cards.
WanVideoModelLoader (T2V model) → WANVIDEOMODEL
WanVideoVAELoader → WANVAE
WanVideoTextEncode (positive + negative prompts) → WANVIDEOTEXTEMBEDS
WanVideoImageToVideoEncode (no images — creates empty embeds for T2V)
→ WANVIDIMAGE_EMBEDS
WanVideoSampler (model, image_embeds, text_embeds, steps, cfg, shift, scheduler)
→ LATENT
WanVideoDecode → IMAGE → VHS_VideoCombine → MP4| Parameter | Standard | Lightning | Notes |
|---|---|---|---|
| steps | 30 | 4 | |
| cfg | 6.0 | 1.0 | |
| shift | 5.0 | 5.0 | Flow matching shift |
| scheduler | unipc | euler | |
| force_offload | true | true |
Located in loras/Wan Video 2.2 T2V-A14B/:
concept/PussyLoRA_HighNoise_Wan2.2_HearmemanAI.safetensors + LowNoise pairApply concept LoRAs the same way as lightning LoRAs. Match hi/lo to the correct model pass. Use LoraLoaderModelOnly with strength 0.5 to 1.0.
| Aspect | Resolution | Notes |
|---|---|---|
| Landscape 16:9 | 832x480 | Default, recommended |
| Portrait 9:16 | 480x832 | |
| 720p landscape | 1280x720 | Higher quality, more VRAM |
| 720p portrait | 720x1280 |
Width and height must be divisible by 16.
4n + 1)Standard: 16 fps for WAN 2.2 output.
| Config | VRAM | Notes |
|---|---|---|
| Dual FP8 models + UMT5 fp8 | ~22-24GB | Tight on RTX 4090 |
| Single FP8 model (no dual) | ~14-16GB | Lower quality but safer |
| With VACE modules | +5.8GB per module | Very tight, may need block swap |
clear_vram before switching to WAN T2V from another model familyDescribe motion and temporal progression in addition to the scene:
Good: "A beautiful young woman slowly walks through a blooming cherry blossom garden, petals drifting in the breeze, soft sunlight filtering through branches, cinematic slow motion, 4K quality"
Bad: "woman in garden"Include motion cues: "slowly walks", "camera pans", "wind blowing", "gradually reveals"
| Feature | T2V | I2V/FLF |
|---|---|---|
| Input | Text only | Text + start/end images |
| Latent init | EmptyHunyuanLatentVideo | WanFirstLastFrameToVideo |
| CLIPVision | Not used | Required |
| Models | T2V-specific (HIGH/LOW) | I2V-specific (HIGH/LOW) |
| Lightning LoRAs | T2V-specific | I2V-specific |
| Creativity | Full creative freedom | Constrained by input frames |
| Use case | Original content | Transitions, animations |
packs/ and observed renders; not a vendor prompting guide.© artokun, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in plugin/skills/wan-t2v-video of artokun/comfyui-mcp.
Open the folder on GitHubat commit 6ad6fc0
Wan T2v Video next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Wan T2v Video this skillartokun/comfyui-mcp | 803 | — | ~3.3k | Automated safety check: Pass | MIT | |
| Gemini Live APIgoogle/skills | 21k | — | ~2.5k | Automated safety check: Notes | Apache-2.0 | |
| Tao Finetune Cosmos EmbedNVIDIA/skills | 3.6k | — | ~3.5k | Automated safety check: Notes | Apache-2.0 | |
| Nsfw VideoLeoYeAI/openclaw-master-skills | 2.2k | — | ~4.6k | Automated safety check: Pass | MIT | |
| Flux Imagedanielmeppiel/agentic-sdlc-handbook | 158 | 1 repos | ~778 | Automated safety check: Pass | Custom licence | |
| Add Diffusion Modelvllm-project/vllm-omni | 7.1k | — | ~7k | Automated safety check: Pass | Apache-2.0 |
google/skills
Generates a Gemini LiveAPI client service class in the user's chosen programming language.
NVIDIA/skills
Cosmos-Embed1 video-text embedding for text-to-video retrieval, video-to-video search, semantic deduplication, and fine-tuning.
LeoYeAI/openclaw-master-skills
Generate AI videos for mature creative projects using Wan 2.2 Spicy (LoRA-tuned for NSFW, top recommended), Wan 2.6, Seedance 1.5, Vidu Q3-Pro, and other models with relaxed content policies via…
danielmeppiel/agentic-sdlc-handbook
Generate images with FLUX models (Black Forest Labs) via inference.sh CLI.
vllm-project/vllm-omni
Add a new diffusion model (text-to-image, text-to-video, image-to-video, text-to-audio, image editing) to vLLM-Omni, including native non-Diffusers ports, reference-parity validation, Cache-DiT…
NVIDIA/skills
Orchestrates defect image generation for PCBA, metal surface and glass inspection with NVIDIA Cosmos AnomalyGen on OSMO, from cold-start Day 0 to real-photo Day 1 labeling.
artokun/comfyui-mcp
Train custom LoRAs with ostris AI-Toolkit. An agent skill from artokun/comfyui-mcp.
artokun/comfyui-mcp
Anime/illustration text-to-image (ANIMA 1.0, ~2B Cosmos DiT).
artokun/comfyui-mcp
Discover Civitai models with the BUILT-IN downloadmodel action:"searchcivitai" and install/generate them locally.
artokun/comfyui-mcp
Diagnose and fix video/image color OBJECTIVELY with the getimage (action:"analyzecolor") tool (scopes/stats such as black/white points, contrast, saturation, clipping, cast) instead of eyeballing a…
artokun/comfyui-mcp
Authoring ComfyUI v2 frontend extensions with @comfyorg/extension-api, covering defineNode/defineExtension/defineWidget, shell UI (sidebar tabs, commands, hotkeys), typed events, and handles.
artokun/comfyui-mcp
Pick the right ComfyUI startup flags for VRAM, attention, caching, and speed.
Categories
Build WAN 2.2 Text-to-Video workflows. An agent skill from artokun/comfyui-mcp. Wan T2v Video is an agent skill from artokun/comfyui-mcp.2 Text-to-Video workflows.
Wan T2v Video fits situations like: tasks that involve Fine-tuning; tasks that involve AI video generation.
Run `npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a claude-code`. Or copy the skill folder (plugin/skills/wan-t2v-video in artokun/comfyui-mcp) into .claude/skills/wan-t2v-video in your project. Claude Code loads it when a task matches its description.
Run `npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a codex`. Or copy the skill folder (plugin/skills/wan-t2v-video in artokun/comfyui-mcp) into .agents/skills/wan-t2v-video in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add artokun/comfyui-mcp --skill wan-t2v-video -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/wan-t2v-video, .gemini/skills/wan-t2v-video, .github/skills/wan-t2v-video and .opencode/skills/wan-t2v-video in your project.
SKILL.md names no scripts, command-line tools or credentials: Wan T2v Video is instructions for the agent only. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Wan T2v Video is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.3k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Wan T2v Video: Gemini Live API (google/skills, 21k stars), Tao Finetune Cosmos Embed (NVIDIA/skills, 3.6k stars), Nsfw Video (LeoYeAI/openclaw-master-skills, 2.2k stars) and Flux Image (danielmeppiel/agentic-sdlc-handbook, 158 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
artokun (a GitHub user) maintains it in artokun/comfyui-mcp, which has 803 GitHub stars. The repository holds 42 skills in this directory. The repository was last updated on October 5, 2026.
Source: artokun/comfyui-mcp on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.