Tiktok Editor Ai

Cloud-based tiktok-editor-ai tool that handles creating short edited TikTok videos from raw footage. Upload MP4, MOV, AVI, WebM files (up to 500MB), describe...

Security Scan

The skill's stated purpose (cloud TikTok video editing) matches most of its instructions, but there are small inconsistencies and some unnecessary local probing that make the bundle look sloppy and warrant caution before installing.

Purpose & Capability

Name and description match the runtime instructions (upload video files, create sessions, render on cloud GPUs). Declared primary credential (NEMO_TOKEN) is sensible for a cloud API. However, the registry metadata reported no required config paths while the SKILL.md frontmatter declares a configPath (~/.config/nemovideo/), an internal inconsistency that should be resolved.

Instruction Scope

Runtime instructions are focused on the external nemovideo.ai API (session creation, upload, SSE, render polling) which fits the purpose. The skill also instructs the agent to read the skill's YAML frontmatter and detect an install path (~/.clawhub, ~/.cursor/skills/) by checking home-directory paths — this local probing is not strictly necessary for editing functionality and expands filesystem access beyond obvious needs. Otherwise there are no instructions to read unrelated env vars or secrets.

Install Mechanism

Instruction-only skill with no install spec or code files — minimal install risk (nothing is downloaded or written by an installer).

Credentials

The skill declares NEMO_TOKEN as a required/primary env var, which is appropriate for a cloud API. But the SKILL.md also describes obtaining an anonymous token automatically if NEMO_TOKEN is absent — so marking the token as strictly required is inconsistent. No other credentials are requested. The mismatch between declared configPaths and registry metadata adds to the proportionality concern.

Persistence & Privilege

Does not request always=true or other elevated persistent privileges. Session IDs are ephemeral and there are no instructions to modify other skills or system-wide settings.

What to consider before installing

This skill mostly does what it says — it uploads your videos to a third-party API (mega-api-prod.nemovideo.ai) for cloud editing and requires/uses a NEMO_TOKEN. Before installing, confirm you trust that domain and understand that your raw videos will be uploaded off-device. Ask the publisher for a homepage or source repository (none is listed). Also note the SKILL.md inconsistently lists a local config path and claims NEMO_TOKEN is required even though it can fetch an anonymous token; that inconsistency could be benign (sloppy metadata) but could also mask unexpected behavior. If you proceed: avoid supplying any unrelated credentials, and prefer letting the skill create an anonymous token rather than entering long-lived secrets until you verify the provider. If you need higher assurance, request the skill's source code or a verified homepage and an explanation for the configPath/install-path checks before enabling it.

Getting Started

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

Try saying:

• "edit my raw video clips"
• "export 1080p MP4"
• "cut to the best 30 seconds,"

Quick Start Setup

This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

• Generate a UUID as client identifier
• POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
• Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

TikTok Editor AI — Edit and Export TikTok Videos

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

Here's a typical use: you send a a 2-minute vertical phone recording, ask for cut to the best 30 seconds, add trending captions and background music, and about 30-60 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — vertical 9:16 footage processes best and skips the crop step entirely.

Matching Input to Actions

User prompts referencing tiktok editor ai, 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 <NEMO_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: tiktok-editor-ai
• 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":"<lang>"} — returns task_id, session_id.

Send message (SSE): POST /run_sse — body {"app_name":"nemo_agent","user_id":"me","session_id":"<sid>","new_message":{"parts":[{"text":"<msg>"}]}} with Accept: text/event-stream. Max timeout: 15 minutes.

Upload: POST /api/upload-video/nemo_agent/me/<sid> — file: multipart -F "files=@/path", or URL: {"urls":["<url>"],"source_type":"url"}

Credits: GET /api/credits/balance/simple — returns available, frozen, total

Session state: GET /api/state/nemo_agent/me/<sid>/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_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll GET /api/render/proxy/lambda/<id> 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 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

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 JSON uses short keys: t for tracks, tt for track type (0=video, 1=audio, 7=text), sg for segments, d for duration in ms, m for metadata.

Example timeline summary:

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 → "cut to the best 30 seconds, add trending captions 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 "cut to the best 30 seconds, add trending captions 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 for widest compatibility across TikTok, Reels, and Shorts.