Film Maker

Get polished film clips ready to post, without touching a single slider. Upload your raw footage (MP4, MOV, AVI, MKV, up to 500MB), say something like "cut t...

Security Scan

The skill's declared purpose (cloud video editing) matches most of its runtime instructions and required token, but there are inconsistencies about config/paths and a small scope creep (detecting install path) and external network calls to an unknown backend that warrant caution.

Purpose & Capability

The skill claims to perform remote video editing and only requires a single API token (NEMO_TOKEN), which is proportionate. However, the SKILL.md frontmatter lists a config path (~/.config/nemovideo/) and later instructions require detecting the agent install path (~/.clawhub/ or ~/.cursor/skills/) for X-Skill-Platform attribution. The registry summary showed no required config paths — this mismatch is unexplained.

Instruction Scope

Runtime instructions include network calls to an external API for anonymous-token acquisition and session management, uploading user media (up to 500MB), streaming SSE, polling state, and reading local paths to determine X-Skill-Platform. Reading install paths or config directories is not fully declared in the registry and is broader than strictly necessary for basic upload/edit operations.

Install Mechanism

No install spec or code files are present; the skill is instruction-only. This minimizes disk-write/installation risk.

Credentials

The only declared environment variable is NEMO_TOKEN (primary credential), which is appropriate for a third‑party API. The skill will fall back to obtaining an anonymous token via a network call if NEMO_TOKEN is absent. There are no other required secrets, which is proportionate — but the anonymous-token flow means the skill can communicate with the external domain even without a user-provided token.

Persistence & Privilege

The skill does not request always:true, has no install steps, and does not claim to modify other skills or system-wide settings. It does, however, instruct the agent to inspect installation paths to set an attribution header — that read access is a limited privilege but should have been declared.

What to consider before installing

This skill mostly does what it says (uploads your video to a remote service for editing) and only needs one token (NEMO_TOKEN). Before installing: 1) Verify the backend domain (mega-api-prod.nemovideo.ai) and the skill author — there is no homepage or known source listed. 2) Confirm privacy/retention and billing for uploaded media (uploads go to a third party and may be stored/charged). 3) Accept that if you don't set NEMO_TOKEN the skill will call an anonymous-token endpoint (it will generate a UUID and obtain a short-lived token). 4) Ask the author why the registry metadata omitted config paths while SKILL.md references ~/.config/nemovideo/ and requires detecting install paths (~/.clawhub/, ~/.cursor/skills/) — filesystem reads should be documented and you should be comfortable with them. 5) If you proceed, test with non-sensitive footage and avoid supplying long-lived secrets in NEMO_TOKEN unless you trust the service.

Getting Started

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

Try saying:

• "edit my raw footage"
• "export 1080p MP4"
• "cut the best takes, add transitions,"

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.

Film Maker — Edit and Export Film Videos

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

A quick example: upload a 3-minute raw scene recording, type "cut the best takes, add transitions, and sync background music", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: breaking long footage into scenes speeds up processing.

Matching Input to Actions

User prompts referencing film maker, 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":"<lang>"}. 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/<sid>	POST	Upload a file (multipart) or URL.
/api/credits/balance/simple	GET	Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/<sid>/latest	GET	Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda	POST	Start export. Body: {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"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: film-maker
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

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

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=<id>, 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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "cut the best takes, add transitions, and sync background music" — concrete instructions get better results.

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

Export as MP4 with H.264 codec for widest platform compatibility.

Common Workflows

Quick edit: Upload → "cut the best takes, add transitions, and sync background music" → 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.