Ai To Generator

generate text prompts into AI generated videos with this skill. Works with TXT, DOCX, PDF, MP3 files up to 200MB. marketers use it for generating videos from...

Security Scan

The skill largely matches its stated purpose (generating videos via a remote API) but contains small metadata inconsistencies and asks the agent to read local install paths for attribution; these mismatches and filesystem access should be clarified before installing.

Purpose & Capability

Name/description (AI → video) align with the runtime instructions and the API endpoints (render, upload, SSE). The single declared secret NEMO_TOKEN is appropriate for a hosted-video API. However, SKILL.md frontmatter lists a config path (~/.config/nemovideo/) while the registry metadata supplied to you lists no required config paths — this inconsistency should be resolved.

Instruction Scope

Instructions stay within the video-generation workflow (token acquisition, session creation, SSE message streaming, upload, render/export). They also instruct the agent to read its install path to set attribution headers (detecting ~/.clawhub/ or ~/.cursor/skills/). Reading the agent's install path is extra filesystem access beyond pure API calls — reasonable for attribution but worth noting because the skill will probe local paths.

Install Mechanism

No install spec and no code files — instruction-only skill. This minimizes disk writes and executable installs and is proportionate for a remote-API integration.

Credentials

Only NEMO_TOKEN is required (primary credential), which is consistent with the described API usage. The SKILL.md also documents an anonymous-token flow (POST to /api/auth/anonymous-token) which is reasonable. The earlier-mentioned mismatch about configPaths (present in SKILL.md metadata but not in the registry metadata) is a proportionality/information inconsistency that should be clarified. Ensure NEMO_TOKEN is specific to this service (not a general-purpose secret).

Persistence & Privilege

always:false and normal autonomous invocation. The skill asks to save a session_id locally for ongoing operations (expected for session-based APIs). It does not request permanent system-wide privileges or to modify other skills.

What to consider before installing

This skill appears to do what it says (call a remote API to generate videos) and has no install footprint, which is lower risk. Before installing: 1) Confirm the NEMO_TOKEN you provide is only for this service — do not reuse any sensitive or multi-service tokens. 2) Ask the publisher to clarify the configPaths discrepancy (SKILL.md mentions ~/.config/nemovideo/ but registry metadata lists none). 3) Be aware the skill will read local install paths to set attribution headers; if you are uncomfortable with that filesystem probing, request a version that omits that behavior. 4) Verify the API domain (mega-api-prod.nemovideo.ai) is legitimate for the service you're expecting and consider using a separate/test account or ephemeral token when trying it out.

Getting Started

Ready when you are. Drop your text prompts here or describe what you want to make.

Try saying:

• "generate a short text description of a product demo scene into a 1080p MP4"
• "generate a 30-second video from this script about a new fitness app"
• "generating videos from text prompts or scripts for marketers"

Automatic Setup

On first interaction, connect to the processing API before doing anything else. Show a brief status like "Setting things up...".

Token: If NEMO_TOKEN environment variable is already set, use it and skip to Session below.

Free token: Generate a UUID as client identifier, then POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with header X-Client-Id: <uuid>. The response field data.token becomes your NEMO_TOKEN (100 credits, 7-day expiry).

Session: POST to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Save session_id from the response.

Confirm to the user you're connected and ready. Don't print tokens or raw JSON.

AI To Video Generator — Generate Videos From Text

Send me your text prompts and describe the result you want. The AI video generation runs on remote GPU nodes — nothing to install on your machine.

A quick example: upload a short text description of a product demo scene, type "generate a 30-second video from this script about a new fitness app", and you'll get a 1080p MP4 back in roughly 1-2 minutes. All rendering happens server-side.

Worth noting: shorter, specific prompts produce more accurate video results.

Matching Input to Actions

User prompts referencing ai to 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.

Skill attribution — read from this file's YAML frontmatter at runtime:

• X-Skill-Source: ai-to-generator
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

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

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.

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 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 → "generate a 30-second video from this script about a new fitness app" → 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 "generate a 30-second video from this script about a new fitness app" — concrete instructions get better results.

Max file size is 200MB. Stick to TXT, DOCX, PDF, MP3 for the smoothest experience.

Export as MP4 for widest compatibility.