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Fanqie Ai Video

作者 linmillsd7 · 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 fanqie-ai-video
功能描述
Skip the learning curve of professional editing software. Describe what you want — auto-cut silences, add subtitles, and export as a short-form video — and g...
使用说明 (SKILL.md)

Getting Started

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

Try saying:

  • "edit my raw video footage"
  • "export 1080p MP4"
  • "auto-cut silences, add subtitles, and export"

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.

Fanqie AI Video — Edit and Export AI Videos

Send me your raw video footage and describe the result you want. The AI video editing runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a 2-minute phone-recorded vlog clip, type "auto-cut silences, add subtitles, and export as a short-form video", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter clips under 60 seconds process significantly faster.

Matching Input to Actions

User prompts referencing fanqie ai 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.

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

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

Header Value
X-Skill-Source fanqie-ai-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 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

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

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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "auto-cut silences, add subtitles, and export as a short-form 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 across all social platforms.

Common Workflows

Quick edit: Upload → "auto-cut silences, add subtitles, and export as a short-form video" → Download MP4. Takes 1-2 minutes 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 appears to be a frontend for a remote AI video-editing API and will upload videos to mega-api-prod.nemovideo.ai and hold short-lived tokens/sessions. Before installing: (1) confirm you trust the remote domain and its privacy/terms — any uploaded videos go to their servers; (2) ask the author to fix metadata mismatches (registry claims no config path and required NEMO_TOKEN, while SKILL.md lists ~/.config/nemovideo/ and provides an anonymous-token flow); (3) decide whether you’re comfortable the skill will create and store anonymous tokens (7‑day) and may read install-path info to set an attribution header; (4) avoid uploading sensitive content until you verify the service; (5) if you want to limit exposure, set a dedicated NEMO_TOKEN tied to a disposable account or require manual token entry rather than allowing automatic anonymous-token creation. If you want higher assurance, request the skill author to clarify where tokens/session_ids are stored and to align registry metadata with SKILL.md.
功能分析
Type: OpenClaw Skill Name: fanqie-ai-video Version: 1.0.0 The fanqie-ai-video skill is a functional integration for an AI-assisted video editing service hosted at nemovideo.ai. It provides detailed instructions for the agent to manage sessions, upload media, and poll for render status. The behavior, including automated token acquisition and environment variable usage (NEMO_TOKEN), is consistent with the stated purpose of providing a cloud-based video editing workflow and lacks indicators of malicious intent, unauthorized data exfiltration, or harmful prompt injection.
能力评估
Purpose & Capability
The described purpose (remote AI video editing, uploads, rendering, and downloads) aligns with the API endpoints and flows in SKILL.md. However, registry metadata and the skill frontmatter disagree: the registry lists no config paths while the SKILL.md frontmatter requires ~/.config/nemovideo/. Also the registry marks NEMO_TOKEN as required but the instructions include an anonymous-token acquisition flow when NEMO_TOKEN is absent. These mismatches are incoherent and worth clarifying.
Instruction Scope
Instructions explicitly tell the agent to upload user video files and URLs, create sessions, stream SSE messages, poll render endpoints, and store a session_id. Those actions are expected for a video-editing integration. Points to watch: (1) the skill auto-generates an anonymous token by POSTing to an external API if NEMO_TOKEN is not set, (2) it instructs the agent to 'auto-detect' platform from the install path for an attribution header (this implies reading agent/install path metadata), and (3) it says keep tokens hidden but still store them for reuse — storage location is not fully specified. None of these are inherently malicious but they extend the agent’s filesystem/network access in ways the registry metadata doesn't fully describe.
Install Mechanism
This is an instruction-only skill with no install spec and no code files, so it does not write or install binaries on disk. That reduces install-time risk.
Credentials
The only declared credential is NEMO_TOKEN (primaryEnv). That is proportionate for a remote service. However, the registry marks it as required while SKILL.md provides an anonymous-token flow when it's absent — a mismatch. The frontmatter also references a config path (~/.config/nemovideo/) which the registry omitted; if the agent will write session tokens or IDs into that path, the registry should have declared it. No unrelated credentials are requested.
Persistence & Privilege
always:false (no forced global presence). The skill instructs storing session_id and possibly tokens (7-day anonymous tokens) for reuse, and frontmatter suggests a config directory. Storing its own session state is normal, but the registry should have declared the config path if persistent storage is used. Autonomous invocation is allowed (default), which increases impact if the skill is later given broad permissions — this is expected but worth noting.
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 fanqie-ai-video
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /fanqie-ai-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 Fanqie AI Video — Edit and Export AI Videos. - Upload raw video files (MP4, MOV, AVI, WebM up to 500MB) for instant AI-driven editing. - Describe edits in plain language: auto-cut silences, add subtitles, export as social-ready video. - Quick cloud rendering: edited clips ready in 1–2 minutes, optimized for TikTok and similar platforms. - Simple authentication with automatic token generation and session management. - Flexible commands: export, check credits, upload, review timeline, and customize actions by prompt.
元数据
Slug fanqie-ai-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

Fanqie Ai Video 是什么?

Skip the learning curve of professional editing software. Describe what you want — auto-cut silences, add subtitles, and export as a short-form video — and g... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 61 次。

如何安装 Fanqie Ai Video?

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

Fanqie Ai Video 是免费的吗?

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

Fanqie Ai Video 支持哪些平台?

Fanqie Ai 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 Fanqie Ai Video?

由 linmillsd7(@linmillsd7)开发并维护,当前版本 v1.0.0。

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