Text To Video Deevid

Turn a 150-word product description script into 1080p AI generated videos just by typing what you need. Whether it's generating videos from written scripts o...

Security Scan

The skill largely matches a text→video cloud service, but its runtime instructions ask the agent to read local skill/frontmatter and detect install paths (filesystem access) and there's an inconsistency about required config paths — these behaviors broaden its scope and merit caution.

Purpose & Capability

The declared purpose (convert text to 1080p AI videos) aligns with the API calls and required NEMO_TOKEN. However, the SKILL.md requests reading the skill file's YAML frontmatter at runtime and detecting install paths to set X-Skill-Platform, which is not strictly necessary for video generation and expands filesystem access. Also the SKILL.md's metadata lists a config path (~/.config/nemovideo/) while the registry metadata above lists no required config paths — an internal inconsistency.

Instruction Scope

Instructions require creating sessions, uploading user files, and posting to a third-party API (expected). Concerningly, they also instruct the agent to read the SKILL.md frontmatter and probe common install directories (~/.clawhub/, ~/.cursor/) to set attribution headers. That requires reading local paths and possibly other files; it could surface more local context than necessary. The instructions also tell the agent to automatically obtain an anonymous token if NEMO_TOKEN is absent (network request to an external endpoint).

Install Mechanism

This is instruction-only with no install spec or downloaded code, so nothing is written to disk by an installer. That reduces install-time risk.

Credentials

The skill requests a single credential (NEMO_TOKEN) which is proportional for a cloud video service. However, the SKILL.md metadata lists a config path (~/.config/nemovideo/) not declared in the registry summary, creating ambiguity about whether the skill expects to access local configuration files beyond the token.

Persistence & Privilege

always:false (no forced persistent inclusion). The skill instructs creation of server-side render jobs that may persist if the session closes (orphaned jobs), which affects user data retention and costs but is not an authorization escalation. The agent is allowed autonomous invocation by default (not flagged alone).

What to consider before installing

Before installing or using this skill: (1) Understand uploads and all files you send will be transmitted to https://mega-api-prod.nemovideo.ai — do not upload sensitive data unless you trust that service. (2) The skill asks the agent to read the SKILL.md frontmatter and probe common install paths to set X-Skill-Platform — ask the maintainer why filesystem access is needed and whether that can be avoided. (3) Confirm the legitimacy of the nemo API domain and its privacy/retention policy; anonymous-token creation will generate a client UUID that could link activity. (4) Prefer testing with a throwaway NEMO_TOKEN or anonymous flow and non-sensitive sample media first. (5) Resolve the metadata inconsistency about required config paths (registry vs SKILL.md) before granting broader access.

Getting Started

Send me your text prompts and I'll handle the AI video creation. Or just describe what you're after.

Try saying:

• "convert a 150-word product description script into a 1080p MP4"
• "turn this script into a 30-second video with visuals and background music"
• "generating videos from written scripts or text prompts for marketers, content creators, social media managers"

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 to Video Deevid — Convert Text Into AI Videos

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

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

Tip: shorter and more specific prompts produce more accurate video results.

Matching Input to Actions

User prompts referencing text to video deevid, 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: text-to-video-deevid
• 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

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 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)

Common Workflows

Quick edit: Upload → "turn this script into a 30-second video with visuals and 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.

Tips and Tricks

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

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

Export as MP4 for widest compatibility across platforms.