Token导航 LogoToken导航

Agent Skill · 开发

wan-2-7(万 2 7)

wan-2-7 用于整理文档、README、Markdown 和说明材料,适合在 OpenClaw 中需要把零散信息整理成结构清晰的文档时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

wan-2-7 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 19,191。

总安装 288周安装 12GitHub Stars 公开资料未说明下载 96核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
0.1.1

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:wan-2-7(万 2 7)
来源仓库:https://github.com/kalvinrv/wan-2-7
安装命令:
openclaw skills install wan-2-7
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install wan-2-7

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

wan-2-7 在 RunComfy 上使用 Wan 2.7 模型生成文本到视频。适合在 OpenClaw 中制作动画或视频剪辑时使用。支持音频驱动和多参考调节功能。wan-2-7 属于开发类 Skill,可作为该场景下的辅助能力补充。安装前需确认权限范围、维护状态及是否会触发 GPU 资源调用。

适用于视频创作者探索 AI 生成内容技术。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install wan-2-7;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

wan-2-7 怎么安装?

在终端执行 openclaw skills install wan-2-7。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

wan-2-7 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

wan-2-7 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

wan-2-7 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

wan-2-7 用的人多吗?

公开数据是累计安装 288 次,近一周安装 12 次,下载 96 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

wan-2-7 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
wan-2-7

万 2 7 · 本页

288—接触敏感数据MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
wan-2-7
displayName
🫧 Wan 2.7 — Pro Pack on RunComfy
description
>
emoji
🫧
homepage
https://www.runcomfy.com
license
MIT
clawdis
requires
bins
env
config

🫧 Wan 2.7 — Pro Pack on RunComfy

runcomfy.com · docs · Text-to-video

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

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):

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):

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):

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.

平台分布

OpenClaw

75.71%
按下载量换算73

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills