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Boomerang Video Maker Free

作者 peandrover adam · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install boomerang-video-maker-free
功能描述
Skip the learning curve of professional editing software. Describe what you want — turn this clip into a looping boomerang that plays forward and backward —...
使用说明 (SKILL.md)

Getting Started

Send me your short video clips and I'll handle the boomerang loop creation. Or just describe what you're after.

Try saying:

  • "convert a 2-second clip of a coffee cup being set down into a 1080p MP4"
  • "turn this clip into a looping boomerang that plays forward and backward"
  • "creating boomerang loop videos for Instagram and TikTok for Instagram and TikTok creators"

First-Time Connection

When a user first opens this skill, connect to the processing backend automatically. Briefly let them know (e.g. "Setting up...").

Authentication: Check if NEMO_TOKEN is set in the environment. If it is, skip to step 2.

  1. Obtain a free token: Generate a random UUID as client identifier. POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id set to that UUID. The response data.token is your NEMO_TOKEN — 100 free credits, valid 7 days.
  2. Create a session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Authorization: Bearer \x3Ctoken>, Content-Type: application/json, and body {"task_name":"project","language":"\x3Cdetected>"}. Store the returned session_id for all subsequent requests.

Keep setup communication brief. Don't display raw API responses or token values to the user.

Boomerang Video Maker Free — Create Looping Boomerang Video Clips

This tool takes your short video clips and runs boomerang loop creation through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 2-second clip of a coffee cup being set down and want to turn this clip into a looping boomerang that plays forward and backward — the backend processes it in about 20-40 seconds and hands you a 1080p MP4.

Tip: shorter clips of 1-3 seconds produce the smoothest boomerang loops.

Matching Input to Actions

User prompts referencing boomerang video maker 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.

Every API call needs Authorization: Bearer \x3CNEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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

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

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 Handling

Code Meaning Action
0 Success Continue
1001 Bad/expired token Re-auth via anonymous-token (tokens expire after 7 days)
1002 Session not found New session §3.0
2001 No credits Anonymous: show registration URL with ?bind=\x3Cid> (get \x3Cid> from create-session or state response when needed). Registered: "Top up credits in your account"
4001 Unsupported file Show supported formats
4002 File too large Suggest compress/trim
400 Missing X-Client-Id Generate Client-Id and retry (see §1)
402 Free plan export blocked Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429 Rate limit (1 token/client/7 days) Retry in 30s 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

SSE Event Handling

Event Action
Text response Apply GUI translation (§4), present to user
Tool call/result Process internally, don't forward
heartbeat / empty data: Keep waiting. Every 2 min: "⏳ Still working..."
Stream closes Process final response

~30% of editing operations return no text in the SSE stream. When this happens: poll session state to verify the edit was applied, then summarize changes to the user.

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 → "turn this clip into a looping boomerang that plays forward and backward" → 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 "turn this clip into a looping boomerang that plays forward and backward" — concrete instructions get better results.

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

Export as MP4 for widest compatibility across Instagram, TikTok, and WhatsApp.

安全使用建议
This skill will upload your videos and session metadata to mega-api-prod.nemovideo.ai for cloud rendering — if you care about privacy or sensitive content, do not upload it. Note the registry metadata and the SKILL.md disagree (config paths and whether NEMO_TOKEN is strictly required), and the skill can auto-provision an anonymous token for you: ask where and how tokens/session IDs are stored and for how long. Confirm you trust the nemo video service (domain, privacy policy, retention) before proceeding. If you need stricter guarantees, request the skill author to (1) make required env/config paths consistent in manifest and docs, (2) document where tokens/session IDs are stored (in-memory vs written to disk), and (3) state explicit privacy/retention rules for uploaded media.
功能分析
Type: OpenClaw Skill Name: boomerang-video-maker-free Version: 1.0.0 The skill automates video processing by communicating with a third-party API (mega-api-prod.nemovideo.ai), requiring the agent to upload user files and manage authentication tokens. It includes instructions to fingerprint the local environment by checking installation paths (e.g., ~/.clawhub/ or ~/.cursor/skills/) for attribution headers and explicitly directs the agent to hide raw API responses and tokens from the user. While these behaviors support the stated functionality, the automated handling of credentials and file uploads to an external domain represents a significant attack surface and data privacy risk.
能力评估
Purpose & Capability
The skill's declared primary credential (NEMO_TOKEN) matches the described cloud backend, which is coherent. However the registry metadata and the SKILL.md disagree: the top-level manifest lists no config paths while SKILL.md frontmatter advertises ~/.config/nemovideo/ as a config path. Also the manifest declares NEMO_TOKEN as required while the runtime instructions describe auto-provisioning an anonymous token if NEMO_TOKEN is missing. These mismatches reduce trust in the declared requirements.
Instruction Scope
Runtime instructions tell the agent to obtain anonymous tokens, create sessions, upload user videos (up to 200MB), poll render jobs, and include attribution headers. That broadly matches a cloud render service, but instructions also instruct reading the skill's YAML frontmatter and probing install paths (e.g., ~/.clawhub/, ~/.cursor/skills/) to set X-Skill-Platform — reading these install directories is outside the core editing task and is not declared in the top-level manifest. The skill will transmit user media and metadata to a third-party domain (mega-api-prod.nemovideo.ai), so user data will leave the device.
Install Mechanism
No install spec and no code files (instruction-only). This minimizes on-disk footprint and reduces supply-chain risk.
Credentials
Only one credential (NEMO_TOKEN) is requested, which is proportional to a cloud API. However the manifest claims the env var is required while the instructions will automatically obtain an anonymous token if it's absent — this inconsistency is notable. The skill also references a config path in its own frontmatter (~/.config/nemovideo/), though the registry metadata did not list that path.
Persistence & Privilege
The skill does not request always:true, does not claim system-wide changes, and is user-invocable. It does instruct storing session IDs for requests, but does not explicitly request persistent system-wide privileges or modify other skills.
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 boomerang-video-maker-free
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /boomerang-video-maker-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 Boomerang Video Maker Free. - Instantly turns user-uploaded video clips into looping boomerang videos (plays forward and backward) in 20–40 seconds. - Supports uploads in MP4, MOV, AVI, and WebM formats up to 200MB; optimized for Instagram and TikTok creators. - Simple authentication: auto-generates free token for first-time users, with 100 free credits (valid 7 days). - Exports 1080p MP4 video; batch and iterative edit workflows supported. - Automatic error handling and user-friendly prompts for common issues (token expiry, no credits, file size, etc.).
元数据
Slug boomerang-video-maker-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

Boomerang Video Maker Free 是什么?

Skip the learning curve of professional editing software. Describe what you want — turn this clip into a looping boomerang that plays forward and backward —... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 76 次。

如何安装 Boomerang Video Maker Free?

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

Boomerang Video Maker Free 是免费的吗?

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

Boomerang Video Maker Free 支持哪些平台?

Boomerang Video Maker 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 Boomerang Video Maker Free?

由 peandrover adam(@peand-rover)开发并维护,当前版本 v1.0.0。

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