Text Online Generator

Skip the learning curve of professional editing software. Describe what you want — turn this text into a 30-second explainer video with visuals and voiceover...

Security Scan

The skill's requested access and runtime instructions are largely consistent with a cloud-based text→video service, but there are small metadata inconsistencies and privacy considerations you should review before installing.

Purpose & Capability

The skill is described as a text-to-video generator and it requests a single service credential (NEMO_TOKEN) and uses an API at mega-api-prod.nemovideo.ai — this is coherent. However, the SKILL.md frontmatter lists a config path (~/.config/nemovideo/) while the registry metadata at the top reported no required config paths; that mismatch should be clarified (does the skill need to read that directory?).

Instruction Scope

The SKILL.md contains detailed, narrow instructions for authenticating, creating sessions, uploading files, streaming SSE, and exporting renders — all within the described service scope. It does require adding attribution headers (X-Skill-Source / X-Skill-Version / X-Skill-Platform) and asks the agent to auto-detect an install path for X-Skill-Platform, which may implicitly require access to runtime or install-path information. The instructions do not ask the agent to read unrelated user files or secrets beyond the service token.

Install Mechanism

Instruction-only skill with no install spec or code files, so nothing is downloaded or written to disk by an installer — lowest-risk install mechanism.

Credentials

Only a single credential (NEMO_TOKEN) is declared as required, which is proportional for a cloud API client. The frontmatter's configPaths entry (~/.config/nemovideo/) is unexpected compared to registry metadata — if the agent will read that path it could expose local files/configs; clarify whether that path is actually needed. The skill also describes an anonymous-token flow that will obtain a token from the remote API if NEMO_TOKEN is absent.

Persistence & Privilege

always:false and no instructions to modify other skills or system-wide settings. The skill can be invoked autonomously (platform default), which increases risk if the skill were malicious, but there is no sign here that it requests elevated persistence.

Assessment

This skill appears to do what it says: talk to a nemovideo API, upload content, and return rendered videos. Before installing: 1) Confirm you trust the service (owner/source unknown) because you will provide a token (NEMO_TOKEN) or the skill will obtain an anonymous token on your behalf. 2) Avoid using sensitive or private documents when uploading — large media uploads can contain PHI or secrets. 3) Ask the publisher to clarify the config path (~/.config/nemovideo/) listed in the skill frontmatter — if used, it could expose local config or credentials. 4) If you’re concerned about autonomous calls, restrict or review agent invocation permissions or supply a throwaway token with limited scope/credits. 5) Verify the API host and privacy/retention policy of mega-api-prod.nemovideo.ai before sending data.

Getting Started

Share your text prompts and I'll get started on AI video generation. Or just tell me what you're thinking.

Try saying:

• "generate my text prompts"
• "export 1080p MP4"
• "turn this text into a 30-second"

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 Online Generator — Generate Videos From Text

This tool takes your text prompts and runs AI video generation through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 150-word product description paragraph and want to turn this text into a 30-second explainer video with visuals and voiceover — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter, clearer text produces more accurate visuals — aim for simple sentences.

Matching Input to Actions

User prompts referencing text online 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.

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.

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

Header	Value
X-Skill-Source	text-online-generator
X-Skill-Version	frontmatter version
X-Skill-Platform	auto-detect: clawhub / cursor / unknown from install path

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

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=<id> (get <id> 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

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.

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

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 → "turn this text into a 30-second explainer video with visuals and voiceover" → 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.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "turn this text into a 30-second explainer video with visuals and voiceover" — concrete instructions get better results.

Max file size is 500MB. Stick to TXT, DOCX, PDF, copied text for the smoothest experience.

Export as MP4 for widest compatibility across platforms and devices.