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Intro Free

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 intro-free
功能描述
Skip the learning curve of professional editing software. Describe what you want — remove the intro from the beginning of my video — and get intro-free clips...
使用说明 (SKILL.md)

Getting Started

Share your raw video footage and I'll get started on AI intro removal. Or just tell me what you're thinking.

Try saying:

  • "remove my raw video footage"
  • "export 1080p MP4"
  • "remove the intro from the beginning"

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.

Intro Free — Remove Intros from Videos

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

Here's a typical use: you send a a 2-minute YouTube video with a 15-second branded intro, ask for remove the intro from the beginning of my video, and about 20-40 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter clips process faster — trim unneeded footage before uploading.

Matching Input to Actions

User prompts referencing intro free, 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 calls go to https://mega-api-prod.nemovideo.ai. The main endpoints:

  1. SessionPOST /api/tasks/me/with-session/nemo_agent with {"task_name":"project","language":"\x3Clang>"}. Gives you a session_id.
  2. Chat (SSE)POST /run_sse with session_id and your message in new_message.parts[0].text. Set Accept: text/event-stream. Up to 15 min.
  3. UploadPOST /api/upload-video/nemo_agent/me/\x3Csid> — multipart file or JSON with URLs.
  4. CreditsGET /api/credits/balance/simple — returns available, frozen, total.
  5. StateGET /api/state/nemo_agent/me/\x3Csid>/latest — current draft and media info.
  6. ExportPOST /api/render/proxy/lambda with render ID and draft JSON. Poll GET /api/render/proxy/lambda/\x3Cid> every 30s for completed status and download URL.

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

Headers are derived from this file's YAML frontmatter. X-Skill-Source is intro-free, X-Skill-Version comes from the version field, and X-Skill-Platform is detected from the install path (~/.clawhub/ = clawhub, ~/.cursor/skills/ = cursor, otherwise unknown).

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.

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)

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.

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

Common Workflows

Quick edit: Upload → "remove the intro from the beginning of my video" → Download MP4. Takes 20-40 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 "remove the intro from the beginning of my video" — concrete instructions get better results.

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

Export as MP4 for widest compatibility.

安全使用建议
This skill appears coherent for cloud-based intro removal and only needs a single API token (NEMO_TOKEN). Before installing: confirm the API domain (mega-api-prod.nemovideo.ai) is a trusted endpoint for you; ask the publisher how uploaded videos are stored, how long they are retained, and whether exports or thumbnails persist on the backend; verify what the NEMO_TOKEN scope allows and where tokens/config files (e.g., ~/.config/nemovideo/) are stored or written; be aware the skill directs the agent to hide technical details from chat (less transparency). If you need stronger assurance, ask the publisher to provide a privacy/retention policy and clarify the configPaths discrepancy in the metadata.
能力评估
Purpose & Capability
Name/description (remove intros from videos) match the runtime instructions: create a session, upload video, request export and download URL. Requiring a NEMO_TOKEN for the remote API is proportionate. Minor inconsistency: the skill frontmatter declares a config path (~/.config/nemovideo/) while the registry metadata showed no required config paths.
Instruction Scope
Instructions stay within the video-editing domain (session creation, SSE chat, upload, export polling). One UX instruction stands out: 'Keep the technical details out of the chat' — this asks the agent to hide technical actions from users, which is a non-security threat by itself but reduces transparency. The skill does not instruct reading unrelated files or environment variables beyond NEMO_TOKEN (aside from the optional config path in frontmatter).
Install Mechanism
No install spec or code files are present (instruction-only), so nothing is written to disk by the skill itself. This is a low-risk install posture.
Credentials
Only a single credential (NEMO_TOKEN) is requested, which is appropriate for an API-backed editor. The frontmatter also lists a config path (~/.config/nemovideo/) not reflected in the registry metadata — clarify whether the skill will read or write that config directory. The skill describes a fallback anonymous-token flow (POST to /api/auth/anonymous-token) which is reasonable but means the agent will perform network calls to acquire/use tokens.
Persistence & Privilege
The skill is not always-enabled and does not request elevated platform privileges. It does not attempt to modify other skills or system-wide settings. Autonomous invocation is allowed (platform default) but not combined with other concerning privileges.
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 intro-free
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /intro-free 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 Intro Free, an AI-powered tool to automatically remove intros from uploaded videos. - Supports MP4, MOV, AVI, and WebM files up to 500MB. - Fast cloud processing: get edited, intro-free clips back within 20–40 seconds. - Simple workflow: upload your footage and describe your edit—no editing experience needed. - Includes free starter credits for new users; handles backend token and session management automatically. - Ideal for YouTubers and content creators who need to cut opening sequences quickly and easily.
元数据
Slug intro-free
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

Intro Free 是什么?

Skip the learning curve of professional editing software. Describe what you want — remove the intro from the beginning of my video — and get intro-free clips... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 68 次。

如何安装 Intro Free?

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

Intro Free 是免费的吗?

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

Intro Free 支持哪些平台?

Intro Free 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 Intro Free?

由 mory128(@mory128)开发并维护,当前版本 v1.0.0。

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