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Ai Free Generator

v1.0.0

Turn a short text description of a product demo scene into 1080p ready-to-share videos just by typing what you need. Whether it's generating videos from text...

0· 65·0 current·0 all-time

Install

OpenClaw Prompt Flow

Install with OpenClaw

Best for remote or guided setup. Copy the exact prompt, then paste it into OpenClaw for dsewell-583h0/ai-free-generator.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Ai Free Generator" (dsewell-583h0/ai-free-generator) from ClawHub.
Skill page: https://clawhub.ai/dsewell-583h0/ai-free-generator
Keep the work scoped to this skill only.
After install, inspect the skill metadata and help me finish setup.
Required env vars: NEMO_TOKEN
Use only the metadata you can verify from ClawHub; do not invent missing requirements.
Ask before making any broader environment changes.

Command Line

CLI Commands

Use the direct CLI path if you want to install manually and keep every step visible.

OpenClaw CLI

Bare skill slug

openclaw skills install ai-free-generator

ClawHub CLI

Package manager switcher

npx clawhub@latest install ai-free-generator
Security Scan
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OpenClawOpenClaw
Suspicious
medium confidence
Purpose & Capability
The name/description (AI video generator) align with the actions in SKILL.md (session creation, SSE, uploads, exports). The skill legitimately needs a token (NEMO_TOKEN) to call the backend. However, the SKILL.md metadata references a config path (~/.config/nemovideo/) and install-path detection for an X-Skill-Platform header; the registry-level summary reported no required config paths. This mismatch is an incoherence to be aware of.
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Instruction Scope
Runtime instructions tell the agent to: read NEMO_TOKEN from env (expected); if absent, create an anonymous token by POSTing to the service; create sessions and perform SSE calls; upload files via multipart (files=@/path) or by URL; and poll render endpoints until completion. Uploading local files and reading an install path are within the scope of a video tool but are also sensitive operations — the skill will transmit user files and session state to mega-api-prod.nemovideo.ai. The SKILL.md also instructs hiding technical details from the chat, which could obscure these network actions from users.
Install Mechanism
This is instruction-only (no install spec, no code files). That minimizes supply-chain risk because nothing is downloaded or written by the skill itself.
Credentials
The only declared credential is NEMO_TOKEN (primaryEnv), which is proportional for a cloud API. The skill can also obtain an anonymous token if NEMO_TOKEN is absent. The SKILL.md metadata mentioning ~/.config/nemovideo/ (a config path) conflicts with the registry report that no config paths are required; this should be clarified before trusting any stored tokens or config files.
Persistence & Privilege
The skill is not always-enabled and does not request elevated persistence or modify other skills. It can be invoked autonomously (default behavior) but that is normal for skills and not by itself a flag.
What to consider before installing
This skill will call an external service (mega-api-prod.nemovideo.ai), may upload local files you provide, and uses a NEMO_TOKEN (or creates a 7-day anonymous token). Before installing or using it: (1) avoid uploading sensitive or private files (PII, credentials, proprietary media), (2) do not put a long-lived production credential into NEMO_TOKEN unless you trust the service, (3) ask the publisher for a privacy/terms link or official homepage (source is unknown), (4) note the SKILL.md claims a config path (~/.config/nemovideo/) that the registry did not list—ask the author to clarify what local files the skill will read, and (5) test with throwaway data/token first.

Like a lobster shell, security has layers — review code before you run it.

Runtime requirements

🎬 Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk973cz9pg5c481rh0pjwscegnn85akt5
65downloads
0stars
1versions
Updated 5d ago
v1.0.0
MIT-0

Getting Started

Ready when you are. Drop your text or 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 with background music"
  • "generating videos from text prompts without paying for a subscription for content creators"

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.

AI Free Generator — Generate Videos Free with AI

Drop your text or prompts in the chat and tell me what you need. I'll handle the AI video generation on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a short text description of a product demo scene, ask for generate a 30-second video from this script with background music, and about 1-2 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter prompts with clear scene descriptions produce more accurate results faster.

Matching Input to Actions

User prompts referencing ai free generator, aspect ratio, text overlays, or audio tracks get routed to the corresponding action via keyword and intent classification.

User says...ActionSkip 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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is ai-free-generator, X-Skill-Version comes from the version field, and X-Skill-Platform is detected from the install path (~/.clawhub/ = clawhub, ~/.cursor/skills/ = cursor, otherwise unknown).

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

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.

SSE Event Handling

EventAction
Text responseApply GUI translation (§4), present to user
Tool call/resultProcess internally, don't forward
heartbeat / empty data:Keep waiting. Every 2 min: "⏳ Still working..."
Stream closesProcess 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 saysYou 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)

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

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 with background music" — concrete instructions get better results.

Max file size is 500MB. Stick to MP4, MOV, WebM, GIF for the smoothest experience.

Export as MP4 for widest compatibility.

Common Workflows

Quick edit: Upload → "generate a 30-second video from this script with 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.

Comments

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