Wan 2 7 logo

Wan 2 7

Organization
genmedia-labs
wan-2-7

Generate text-to-video with Wan 2.7 (Wan-AI's flagship motion model) on RunComfy. Documents Wan 2.7's strengths (multi-reference conditioning, audio-driven lip-sync via `audio_url`, smoother transitions, prompt expansion), the duration / resolution / aspect-ratio schema, and when to route to HappyHorse 1.0 / Seedance 2.0 / Kling / LTX 2 instead. Calls `runcomfy run wan-ai/wan-2-7/text-to-video` through the local RunComfy CLI. Triggers on "wan", "wan 2.7", "wan-2-7", "wan video", or any explicit ask to generate video with this model.

Overview

Publishergenmedia-labs
Repositoryskills
Skill namewan-2-7
Stars
26
Forks
12
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by genmedia-labs on GitHub. Read the source before you install it.

Installation

Install the Wan 2 7 AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/genmedia-labs/skills/tree/main/wan-2-7
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/genmedia-labs/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/wan-2-7 .claude/skills/wan-2-7
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wan 2 7 in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wan 2 7 on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wan 2 7 is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Wan 2.7 — Pro Pack on RunComfy

runcomfy.com · Text-to-video · GitHub

Wan-AI's Wan 2.7 — flagship video model with multi-reference conditioning and audio-driven lip-sync — hosted on the RunComfy Model API.

bash
npx skills add agentspace-so/runcomfy-skills --skill wan-2-7 -g

When to pick this model (vs siblings)

You wantUse
Lip-sync video to an audio track you supplyWan 2.7 (audio_url)
Multi-reference fine motion controlWan 2.7
Smooth transitions, accurate motion physicsWan 2.7
Currently-#1 blind-vote video modelHappyHorse 1.0
Multi-modal cinematic with image+video+audio refs + in-pass voice generationSeedance 2.0 Pro
Cinematic motion editing on existing footageKling Video O1
Ultra-fast iterationLTX 2

If the user said "Wan" / "Wan 2.7" / "wan-ai" / "alibaba video" explicitly, route here regardless.

Prerequisites

  1. RunComfy CLI — npm i -g @runcomfy/cli
  2. RunComfy account — runcomfy login opens a browser device-code flow.
  3. CI / containers — set RUNCOMFY_TOKEN=<token> instead of runcomfy login.

Endpoints + input schema

wan-ai/wan-2-7/text-to-video

FieldTypeRequiredDefaultNotes
promptstringyes—Up to ~5000 chars / ~1500 tokens.
audio_urlstringno—WAV/MP3, 3–30s, ≤15MB. Drives lip-sync. Omit → background music auto-generated.
aspect_ratioenumno16:916:9, 9:16, 1:1, 4:3, 3:4.
resolutionenumno1080p720p or 1080p.
durationenumno52–15 (whole seconds).
negative_promptstringno—Up to 500 chars. Concrete issues to avoid.
enable_prompt_expansionboolnotrueAuto-rewrites short prompts. Disable for literal control.
seedintno—0..2^31-1. Reuse for variants.

How to invoke

Default (5s 1080p 16:9, prompt-expanded):

bash
runcomfy run wan-ai/wan-2-7/text-to-video \
  --input '{"prompt": "<user prompt>"}' \
  --output-dir <absolute/path>

Audio-driven lip-sync (your own track):

bash
runcomfy run wan-ai/wan-2-7/text-to-video \
  --input '{
    "prompt": "Medium close-up of the spokesperson, warm key light, locked tripod, slight breathing motion.",
    "audio_url": "https://.../voiceover.mp3",
    "duration": 12,
    "aspect_ratio": "9:16"
  }' \
  --output-dir <absolute/path>

Literal control (no auto-expansion):

bash
runcomfy run wan-ai/wan-2-7/text-to-video \
  --input '{
    "prompt": "<exactly what you want, verbatim>",
    "enable_prompt_expansion": false,
    "negative_prompt": "no subtitles, no flicker, no distorted hands"
  }' \
  --output-dir <absolute/path>

Prompting — what actually works

Camera + motion in plain English. "Slow dolly in", "locked tripod, low angle", "handheld follow", "crane move from above". Front-load the shot.

One primary action per clip. Don't pile up multiple competing actions. Pick the beat: "she turns, then smiles" not "she turns AND smiles AND a bus passes AND...".

Use negative_prompt for concrete issues. Good: "no subtitles, no watermark, no flicker". Bad (vague): "no bad lighting".

Prompt expansion is on by default. Short prompts get auto-rewritten by the model. For terse / literal prompts (e.g. brand-strict ad copy), disable with enable_prompt_expansion: false.

Audio specs matter. audio_url must be 3–30s, ≤15MB, WAV/MP3. Out-of-range files reject. Match audio length to clip duration.

Iterate seeds. Reuse the same seed when you want consistent output across variants of the same prompt. Change seed for genuine variety.

Anti-patterns:

  • Static-frame descriptions → motion will be vague.
  • Vague negatives ("no bad colors") → ignored.
  • Audio outside the 3–30s / 15MB / WAV-MP3 spec → rejected.
  • Prompts > 5000 chars / 1500 tokens → degraded output.

Where it shines

Use caseWhy Wan 2.7
Lip-synced ads with custom voiceoveraudio_url accepts your track
Multi-language dub variantsSame prompt, different audio_url per language
Multi-reference motion controlUp to 5 reference media (image / video / voice)
Smooth transitions + motion physicsStrong physics-aware motion priors
Negative-prompted clean outputTargeted issue exclusion

Sample prompts (verified to produce strong results)

Page example (product showcase):

Cinematic medium shot of a product on a marble surface, soft studio
lighting, slow subtle camera push-in, shallow depth of field, premium
commercial look, crisp 1080p detail

Lip-synced spokesperson (with audio_url):

Medium close-up of a confident spokesperson in a softly-lit recording
booth, leaning slightly toward the camera, locked tripod, shallow depth
of field, warm key light from camera-left.

Vertical platform-native:

9:16 vertical short. A barista pulls a single espresso shot, steam
rising into morning sun, rich crema slowly forming. Close-up handheld,
shallow DOF, warm cafe ambience.

Limitations

  • Duration cap 15s. For longer narratives, stitch multiple calls.
  • No native 4K — 1080p ceiling.
  • Aspect ratios — only the 5 documented values.
  • Audio specs — 3–30s, ≤15MB, WAV/MP3 only.
  • Reference media cap 5 (image + video + voice combined).
  • For in-pass voice generation (no separate audio track), use Seedance 2.0 Pro — Wan accepts audio rather than generating it.

Exit codes

codemeaning
0success
64bad CLI args
65bad input JSON / schema mismatch
69upstream 5xx
75retryable: timeout / 429
77not signed in or token rejected

Full reference: docs.runcomfy.com/cli/troubleshooting.

How it works

The skill invokes runcomfy run wan-ai/wan-2-7/text-to-video with a JSON body matching the schema. The CLI POSTs to https://model-api.runcomfy.net/v1/models/wan-ai/wan-2-7/text-to-video, polls the request, fetches the result, and downloads any .runcomfy.net/.runcomfy.com URL into --output-dir. Ctrl-C cancels the remote request before exit.

Security & Privacy

  • Token storage: runcomfy login writes the API token to ~/.config/runcomfy/token.json with mode 0600 (owner-only read/write). Set RUNCOMFY_TOKEN env var to bypass the file entirely in CI / containers.
  • Input boundary: the user prompt is passed as a JSON string to the CLI via --input. The CLI does NOT shell-expand the prompt; it transmits the JSON body directly to the Model API over HTTPS. No shell injection surface from prompt content.
  • Third-party content: image / mask / video URLs you pass are fetched by the RunComfy model server, not by the CLI on your machine. Treat external URLs as untrusted; image-based prompt injection is a known risk for any image-edit / video-edit model.
  • Outbound endpoints: only model-api.runcomfy.net (request submission) and *.runcomfy.net / *.runcomfy.com (download whitelist for generated outputs). No telemetry, no callbacks.
  • Generated-file size cap: the CLI aborts any single download > 2 GiB to prevent disk-fill from a malicious or runaway model output.

Frequently asked questions

What does the Wan 2 7 AI skill do?

Generate text-to-video with Wan 2.7 (Wan-AI's flagship motion model) on RunComfy. Documents Wan 2.7's strengths (multi-reference conditioning, audio-driven lip-sync via `audio_url`, smoother transitions, prompt expansion), the duration / resolution / aspect-ratio schema, and when to route to HappyHorse 1.0 / Seedance 2.0 / Kling / LTX 2 instead. Calls `runcomfy run wan-ai/wan-2-7/text-to-video` through the local RunComfy CLI. Triggers on "wan", "wan 2.7", "wan-2-7", "wan video", or any explicit ask to generate video with this model.

Why use Wan 2 7 on TypingMind?

Because you install it once and use it with any model. Wan 2 7 is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wan 2 7 in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/genmedia-labs/skills/tree/main/wan-2-7. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wan 2 7?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wan 2 7?

As many as you like. As long as a model supports skills, you can use Wan 2 7 with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wan 2 7 AI skill free?

Yes. It is published on GitHub by genmedia-labs under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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