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Login Video

作者 mory128 · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install login-video
功能描述
Turn a 30-second screen recording of an app login flow into 1080p polished login videos just by typing what you need. Whether it's creating onboarding or log...
使用说明 (SKILL.md)

Getting Started

Ready when you are. Drop your raw video footage here or describe what you want to make.

Try saying:

  • "create a 30-second screen recording of an app login flow into a 1080p MP4"
  • "create a clean login walkthrough video with annotations and background music"
  • "creating onboarding or login tutorial videos for apps and websites for product teams, UX designers, marketers"

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.

Login Video — Create Login Walkthrough Videos Fast

This tool takes your raw video footage and runs AI login video creation through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 30-second screen recording of an app login flow and want to create a clean login walkthrough video with annotations and background music — the backend processes it in about 30-60 seconds and hands you a 1080p MP4.

Tip: keep your screen recording under 60 seconds for fastest processing and clearest output.

Matching Input to Actions

User prompts referencing login 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.

Base URL: https://mega-api-prod.nemovideo.ai

Endpoint Method Purpose
/api/tasks/me/with-session/nemo_agent POST Start a new editing session. Body: {"task_name":"project","language":"\x3Clang>"}. Returns session_id.
/run_sse POST Send a user message. Body includes app_name, session_id, new_message. Stream response with Accept: text/event-stream. Timeout: 15 min.
/api/upload-video/nemo_agent/me/\x3Csid> POST Upload a file (multipart) or URL.
/api/credits/balance/simple GET Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/\x3Csid>/latest GET Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda POST Start export. Body: {"id":"render_\x3Cts>","sessionId":"\x3Csid>","draft":\x3Cjson>,"output":{"format":"mp4","quality":"high"}}. Poll status every 30s.

Accepted file types: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

Skill attribution — read from this file's YAML frontmatter at runtime:

  • X-Skill-Source: login-video
  • X-Skill-Version: from frontmatter version
  • X-Skill-Platform: detect from install path (~/.clawhub/clawhub, ~/.cursor/skills/cursor, else unknown)

Include Authorization: Bearer \x3CNEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

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

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.

Translating GUI Instructions

The backend responds as if there's a visual interface. Map its instructions to API calls:

  • "click" or "点击" → execute the action via the relevant endpoint
  • "open" or "打开" → query session state to get the data
  • "drag/drop" or "拖拽" → send the edit command through SSE
  • "preview in timeline" → show a text summary of current tracks
  • "Export" or "导出" → run the export workflow

Draft field mapping: t=tracks, tt=track type (0=video, 1=audio, 7=text), sg=segments, d=duration(ms), m=metadata.

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

Common Workflows

Quick edit: Upload → "create a clean login walkthrough video with annotations and background music" → 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.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "create a clean login walkthrough video with annotations and background music" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, AVI, WebM for the smoothest experience.

Export as MP4 with H.264 codec for widest compatibility across web and mobile platforms.

安全使用建议
Install only if you are comfortable sending the selected media to NemoVideo's cloud API. Use test login flows or redact secrets, avoid recording real passwords or tokens, and monitor any NemoVideo token or credit usage.
功能分析
Type: OpenClaw Skill Name: login-video Version: 1.0.0 The skill bundle is a legitimate integration for a video processing service (nemovideo.ai). It provides instructions for an AI agent to manage sessions, upload media, and trigger cloud-based video rendering. The code and instructions (SKILL.md) are focused entirely on the stated purpose of creating login walkthrough videos and do not exhibit signs of data exfiltration, malicious execution, or harmful prompt injection.
能力评估
Purpose & Capability
Purpose and capabilities are aligned: SKILL.md describes taking "raw video footage" and running it through a "cloud rendering pipeline" to produce a login walkthrough video. Because the intended input is a login-flow recording, the media may contain sensitive account or credential details.
Instruction Scope
SKILL.md routes most edit requests to SSE and says backend GUI-style instructions should be mapped to API calls, with "Tool calls stay internal." This is scoped to NemoVideo endpoints, but users should know the backend can drive internal workflow steps.
Install Mechanism
No install spec or code files are present, and the static scanner reported no findings.
Credentials
External API use is proportionate to the cloud-rendering purpose, but SKILL.md explicitly sends uploads and render jobs to `https://mega-api-prod.nemovideo.ai`, so user-provided media leaves the local environment.
Persistence & Privilege
SKILL.md uses `NEMO_TOKEN`, creates a `session_id`, and notes that render job IDs are carried by the session token. No local persistence or privileged system access is shown, but cloud sessions and credits are involved.
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 login-video
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /login-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 login-video: Create polished login walkthrough videos from screen recordings. - Instantly turn raw app login recordings into 1080p login, onboarding, or tutorial videos by describing your needs — no timeline editing required. - Handles uploads of multiple video and audio formats, applies AI-powered annotation, background music, and rendering in the cloud within 30–90 seconds. - Provides a straightforward flow: Upload → Describe desired edit → Download final MP4. - Simple credential and session management, with support for both token and anonymous sessions. - Includes clear error responses, session state polling, and helpful chat UI status messages.
元数据
Slug login-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

Login Video 是什么?

Turn a 30-second screen recording of an app login flow into 1080p polished login videos just by typing what you need. Whether it's creating onboarding or log... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 35 次。

如何安装 Login Video?

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

Login Video 是免费的吗?

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

Login Video 支持哪些平台?

Login 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 Login Video?

由 mory128(@mory128)开发并维护,当前版本 v1.0.0。

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