Text Editor Generator

Get transcript-edited videos ready to post, without touching a single slider. Upload your raw video footage (MP4, MOV, AVI, WebM, up to 500MB), say something...

Security Scan

The skill's requests and runtime instructions are consistent with a cloud-based, text-driven video editing service: it needs a single service token (NEMO_TOKEN) and uploads user video to the remote API — no installs or unrelated credentials are requested.

Purpose & Capability

The name/description describe cloud transcript-based video editing and the SKILL.md only asks for a service token and API calls to a video-rendering backend. Requiring a token and upload endpoints is proportionate for this purpose.

Instruction Scope

Instructions contain detailed API usage (session creation, SSE, upload, render polling) and explicitly upload user media to https://mega-api-prod.nemovideo.ai. That is expected, but the frontmatter also lists a config path (~/.config/nemovideo/) which the registry summary says is not required — this inconsistency could indicate the skill may look for local config files if present. The skill also requires specific attribution headers for each request (X-Skill-*) which is unusual but coherent with a hosted API.

Install Mechanism

There is no install spec and no code files — instruction-only — so nothing will be downloaded or written by an installer. This is the lowest install risk.

Credentials

Only one credential is required (NEMO_TOKEN), which fits the stated backend API usage. However, the frontmatter's configPaths entry (~/.config/nemovideo/) is present in SKILL.md metadata but the registry summary shows no required config paths; if the skill actually reads that path it could access local saved tokens or configs, so users should be cautious about what is stored there.

Persistence & Privilege

always is false and the skill does not request system-wide changes or elevated privileges. Autonomous invocation is enabled by default (normal for skills) but not combined with other high-risk factors.

Assessment

This skill is internally coherent for a hosted transcript-based video editor: it will send your uploaded video and editing instructions to the nemovideo.ai backend and requires a NEMO_TOKEN (or will obtain a short-lived anonymous token). Before installing or using it, confirm you trust the external service (mega-api-prod.nemovideo.ai) and are comfortable with uploading potentially sensitive video/audio. Be aware of the small metadata inconsistency: SKILL.md lists ~/.config/nemovideo/ as a config path — avoid storing secrets or unrelated tokens there unless you trust the skill. No code is installed locally and no other credentials are requested.

Getting Started

Send me your raw video footage and I'll handle the AI text-based editing. Or just describe what you're after.

Try saying:

• "edit a 3-minute interview recording into a 1080p MP4"
• "edit the video by deleting sentences from the transcript and trimming silences"
• "editing videos by modifying text transcripts instead of timelines for content creators and podcasters"

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.

Text Editor Generator — Edit Videos via Text Transcript

This tool takes your raw video footage and runs AI text-based editing through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 3-minute interview recording and want to edit the video by deleting sentences from the transcript and trimming silences — the backend processes it in about 30-60 seconds and hands you a 1080p MP4.

Tip: deleting a word in the transcript automatically removes that segment from the video.

Matching Input to Actions

User prompts referencing text editor generator, 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 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.

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

Header	Value
X-Skill-Source	text-editor-generator
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":"<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

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 "edit the video by deleting sentences from the transcript and trimming silences" — 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.

Common Workflows

Quick edit: Upload → "edit the video by deleting sentences from the transcript and trimming silences" → 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.