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Ai Wechat Video

作者 mhogan2013-9 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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当前安装
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在 OpenClaw 中安装
/install ai-wechat-video
功能描述
Turn a 60-second product demo video into 1080p WeChat-ready videos just by typing what you need. Whether it's converting and formatting videos for sharing on...
使用说明 (SKILL.md)

Getting Started

Got video clips to work with? Send it over and tell me what you need — I'll take care of the AI video optimization.

Try saying:

  • "convert a 60-second product demo video into a 1080p MP4"
  • "format this video for WeChat Moments with subtitles and a square crop"
  • "converting and formatting videos for sharing on WeChat for WeChat content creators"

Getting Connected

Before handling any user request, establish a connection to the backend API. Show a brief status like "Connecting...".

If NEMO_TOKEN is in the environment, use it directly and create a session. Otherwise, acquire a free starter token:

  • Generate a UUID as client identifier
  • POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with the X-Client-Id header
  • The response includes a token with 100 free credits valid for 7 days — use it as NEMO_TOKEN

Then create a session by POSTing to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer authorization and body {"task_name":"project","language":"en"}. The session_id in the response is needed for all following requests.

Tell the user you're ready. Keep the technical details out of the chat.

AI WeChat Video — Format and Export WeChat Videos

Drop your video clips in the chat and tell me what you need. I'll handle the AI video optimization on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a 60-second product demo video, ask for format this video for WeChat Moments with subtitles and a square crop, and about 30-60 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — keep videos under 1 minute to match WeChat Moments playback limits.

Matching Input to Actions

User prompts referencing ai wechat video, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says... Action Skip SSE?
"export" / "导出" / "download" / "send me the video" → §3.5 Export
"credits" / "积分" / "balance" / "余额" → §3.3 Credits
"status" / "状态" / "show tracks" → §3.4 State
"upload" / "上传" / user sends file → §3.2 Upload
Everything else (generate, edit, add BGM…) → §3.1 SSE

Cloud Render Pipeline Details

Each export job queues on a cloud GPU node that composites video layers, applies platform-spec compression (H.264, up to 1080x1920), and returns a download URL within 30-90 seconds. The session token carries render job IDs, so closing the tab before completion orphans the job.

All requests must include: Authorization: Bearer \x3CNEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

Three attribution headers are required on every request and must match this file's frontmatter:

Header Value
X-Skill-Source ai-wechat-video
X-Skill-Version frontmatter version
X-Skill-Platform auto-detect: clawhub / cursor / unknown from install path

API base: https://mega-api-prod.nemovideo.ai

Create session: POST /api/tasks/me/with-session/nemo_agent — body {"task_name":"project","language":"\x3Clang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"\x3Csid>","new_message":{"parts":[{"text":"\x3Cmsg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/\x3Csid> — file: multipart -F "files=@/path", or URL: {"urls":["\x3Curl>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/\x3Csid>/latest — key fields: data.state.draft, data.state.video_infos, data.state.generated_media

Export (free, no credits): POST /api/render/proxy/lambda — body {"id":"render_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/\x3Cid> every 30s until status = completed. Download URL at output.url.

Supported formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Error Codes

  • 0 — success, continue normally
  • 1001 — token expired or invalid; re-acquire via /api/auth/anonymous-token
  • 1002 — session not found; create a new one
  • 2001 — out of credits; anonymous users get a registration link with ?bind=\x3Cid>, registered users top up
  • 4001 — unsupported file type; show accepted formats
  • 4002 — file too large; suggest compressing or trimming
  • 400 — missing X-Client-Id; generate one and retry
  • 402 — free plan export blocked; not a credit issue, subscription tier
  • 429 — rate limited; wait 30s and retry once

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend says You do
"click [button]" / "点击" Execute via API
"open [panel]" / "打开" Query session state
"drag/drop" / "拖拽" Send edit via SSE
"preview in timeline" Show track summary
"Export button" / "导出" Execute export workflow

Reading the SSE Stream

Text events go straight to the user (after GUI translation). Tool calls stay internal. Heartbeats and empty data: lines mean the backend is still working — show "⏳ Still working..." every 2 minutes.

About 30% of edit operations close the stream without any text. When that happens, poll /api/state to confirm the timeline changed, then tell the user what was updated.

Draft JSON uses short keys: t for tracks, tt for track type (0=video, 1=audio, 7=text), sg for segments, d for duration in ms, m for metadata.

Example timeline summary:

Timeline (3 tracks): 1. Video: city timelapse (0-10s) 2. BGM: Lo-fi (0-10s, 35%) 3. Title: "Urban Dreams" (0-3s)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "format this video for WeChat Moments with subtitles and a square crop" — concrete instructions get better results.

Max file size is 200MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience.

Export as MP4 with H.264 codec for best WeChat compatibility.

Common Workflows

Quick edit: Upload → "format this video for WeChat Moments with subtitles and a square crop" → Download MP4. Takes 30-60 seconds for a 30-second clip.

Batch style: Upload multiple files in one session. Process them one by one with different instructions. Each gets its own render.

Iterative: Start with a rough cut, preview the result, then refine. The session keeps your timeline state so you can keep tweaking.

安全使用建议
This skill mostly does what it says — it talks to a Nemovideo-like backend to upload and render short videos — but a few things are inconsistent and worth checking before you install or hand over credentials: - Only provide a NEMO_TOKEN if you trust the backend/service. That token can be used to upload files, start renders, and consume credits on your account. If possible, test with an anonymous token (the SKILL.md shows how to obtain one) rather than your primary account token. - Ask the publisher to clarify the config path discrepancy: SKILL.md frontmatter references ~/.config/nemovideo/ while registry metadata did not declare any required config paths. If the skill will read local config files, you should know exactly what it will access. - Confirm how uploads are handled: the doc shows multipart uploads using local file paths (files=@/path). Ensure the agent will only upload files you explicitly attach in-chat, and will not attempt to read arbitrary filesystem paths. - The requirement to auto-detect an install path for X-Skill-Platform is unrealistic for an instruction-only skill; ask the author to switch to a fixed platform header or a safe default. - Because the source is unknown, prefer using anonymous/free tokens for initial testing, revoke tokens you provide if you stop using the skill, and monitor your account activity/credits on the backend service. If the publisher can resolve the config-path/metadata mismatch and confirm uploads are limited to user-provided files, the incoherences look like sloppy packaging rather than malicious behavior.
功能分析
Type: OpenClaw Skill Name: ai-wechat-video Version: 1.0.0 The ai-wechat-video skill is a legitimate integration for a cloud-based video processing service. It facilitates video formatting and exporting for WeChat by interacting with the 'mega-api-prod.nemovideo.ai' backend. The skill's instructions (SKILL.md) clearly outline the necessary API calls for authentication, session management, file uploads, and rendering. While it requires network access and handles user-provided video files, these actions are directly aligned with its stated purpose. There is no evidence of malicious intent, such as unauthorized access to sensitive local files (e.g., SSH keys) or the execution of harmful commands.
能力评估
Purpose & Capability
The declared purpose (cloud-based video formatting for WeChat) aligns with the API endpoints and operations documented (upload, render, export). Requesting a single service token (NEMO_TOKEN) is proportional for a cloud backend. However, the SKILL.md frontmatter lists a required config path (~/.config/nemovideo/) while the registry metadata earlier listed no required config paths — this mismatch is inconsistent.
Instruction Scope
The runtime instructions direct the agent to obtain or reuse a bearer token, create sessions, upload videos (multipart or URL), stream SSE responses, and poll render status — all expected for this service. Concerns: (1) the file-upload examples explicitly show local file paths (files=@/path) which could imply reading arbitrary local paths if the agent has file access; the instructions don't clearly constrain uploads to user-supplied attachments. (2) The header 'X-Skill-Platform' is supposed to be auto-detected from install path — instruction-only skills often cannot reliably detect an install path, so this requirement is unrealistic. (3) The SKILL.md frontmatter requiring a config directory (contradicting registry metadata) suggests either sloppy packaging or an unexplained need to read local config.
Install Mechanism
No install spec and no code files (instruction-only) — lowest-risk delivery mechanism. Nothing is downloaded or written to disk by an installer.
Credentials
The skill only requests one credential: NEMO_TOKEN (primaryEnv). That is proportionate for a cloud render service. Important caveat: that single token can grant access to the user's account on the backend (jobs, uploads, credits), so it's sensitive — the SKILL.md also documents steps to obtain an anonymous token if not provided, which is safer for trial use. The apparent frontmatter request for a local config path is inconsistent with the declared requirements and should be clarified.
Persistence & Privilege
always is false and the skill is instruction-only; it does not request permanent inclusion or system-wide configuration changes. Autonomous invocation is allowed by default (normal) but does increase runtime scope; this is not combined with elevated privileges in this package.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install ai-wechat-video
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ai-wechat-video 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
- Initial release of AI WeChat Video v1.0.0. - Instantly format and export 60-second product demo videos to 1080p MP4, WeChat-ready, via simple chat instructions. - Seamless backend connection: automatic session setup with environment token or anonymous access and free credits. - Cloud GPU video processing supports cropping, subtitles, aspect ratios, and multiple output formats (e.g., mp4, mov, jpg). - Clear error handling and session management; connection, credits, and export status shown to the user. - Designed for fast, easy WeChat video optimization—no timeline editing or export settings required.
元数据
Slug ai-wechat-video
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Ai Wechat Video 是什么?

Turn a 60-second product demo video into 1080p WeChat-ready videos just by typing what you need. Whether it's converting and formatting videos for sharing on... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 105 次。

如何安装 Ai Wechat Video?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install ai-wechat-video」即可一键安装,无需额外配置。

Ai Wechat Video 是免费的吗?

是的,Ai Wechat Video 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Ai Wechat Video 支持哪些平台?

Ai Wechat Video 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Ai Wechat Video?

由 mhogan2013-9(@mhogan2013-9)开发并维护,当前版本 v1.0.0。

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