Skills free-text-to-image-video

install

source · Clone the upstream repo

git clone https://github.com/openclaw/skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/bwbernardweston18/free-text-to-image-video" ~/.claude/skills/clawdbot-skills-free-text-to-image-video && rm -rf "$T"

manifest: skills/bwbernardweston18/free-text-to-image-video/SKILL.md

source content

Getting Started

Got text prompts to work with? Send it over and tell me what you need — I'll take care of the AI video generation.

Try saying:

"generate a short descriptive paragraph about a sunset over the ocean into a 1080p MP4"
"turn my text description into a video with matching images and smooth transitions"
"generating videos from written descriptions without any source footage for marketers, content creators, educators"

First-Time Connection

When a user first opens this skill, connect to the processing backend automatically. Briefly let them know (e.g. "Setting up...").

Authentication: Check if

NEMO_TOKEN

is set in the environment. If it is, skip to step 2.

Obtain a free token: Generate a random UUID as client identifier. POST to
```
https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token
```
with header
```
X-Client-Id
```
set to that UUID. The response
```
data.token
```
is your NEMO_TOKEN — 100 free credits, valid 7 days.

Create a session: POST to

https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent

with

Authorization: Bearer <token>

Content-Type: application/json

, and body

{"task_name":"project","language":"<detected>"}

. Store the returned

session_id

for all subsequent requests.

Keep setup communication brief. Don't display raw API responses or token values to the user.

Free Text to Image Video — Generate Videos from Text Descriptions

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 descriptive paragraph about a sunset over the ocean, type "turn my text description into a video with matching images and smooth transitions", 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 visuals than vague long descriptions.

Matching Input to Actions

User prompts referencing free text to image video, 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.

Headers are derived from this file's YAML frontmatter.

X-Skill-Source

free-text-to-image-video

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

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 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

Reading the SSE Stream

Text events go straight to the user (after GUI translation). Tool calls stay internal. Heartbeats and empty

data:

lines mean the backend is still working — show "⏳ Still working..." every 2 minutes.

About 30% of edit operations close the stream without any text. When that happens, poll

/api/state

to confirm the timeline changed, then tell the user what was updated.

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:

for tracks,

tt

for track type (0=video, 1=audio, 7=text),

sg

for segments,

for duration in ms,

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 my text description into a video with matching images and smooth transitions" → 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 my text description into a video with matching images and smooth transitions" — 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 social platforms and devices.