Text To Video Hulk

v1.0.0

Get AI generated videos ready to post, without touching a single slider. Upload your text prompts (TXT, DOCX, PDF, plain text, up to 500MB), say something li...

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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/text-to-video-hulk.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Text To Video Hulk" (dsewell-583h0/text-to-video-hulk) from ClawHub.
Skill page: https://clawhub.ai/dsewell-583h0/text-to-video-hulk
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 text-to-video-hulk

ClawHub CLI

Package manager switcher

npx clawhub@latest install text-to-video-hulk
Security Scan
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OpenClawOpenClaw
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medium confidence
Purpose & Capability
The skill claims to generate videos from text and only requests a single service token (NEMO_TOKEN) and uses cloud API endpoints for rendering and uploads. Requesting a token and session management is consistent with that purpose.
Instruction Scope
SKILL.md instructs the agent to authenticate (using NEMO_TOKEN or an anonymously minted token), create sessions, send SSE messages, upload files (multipart or URLs), poll render status, and deliver download URLs — all expected for a cloud render service. It does not instruct reading unrelated system files or other environment variables. It does, however, assume handling user-uploaded files (up to 500MB) and keeping session IDs in runtime state; users should be aware uploads go to the external API.
Install Mechanism
Instruction-only skill with no install spec and no code files — minimal local install risk (nothing is downloaded or written by the skill itself).
Credentials
Only NEMO_TOKEN is declared as required (primaryEnv), which is proportional to a hosted rendering service. There is a minor inconsistency: SKILL.md frontmatter lists a config path (~/.config/nemovideo/) but the registry metadata shows no required config paths — unclear whether the skill expects to read or write a local config directory.
Persistence & Privilege
The skill does not request persistent or always-on privileges (always:false) and does not ask to modify other skills or system-wide settings. It asks only to manage ephemeral session tokens for render jobs.
Assessment
This skill is internally coherent for a cloud text→video service, but it calls an external API (mega-api-prod.nemovideo.ai) and requires a NEMO_TOKEN. Before installing: (1) confirm you trust the external service and its privacy/terms, because any uploaded text/files (including up to 500MB) will be sent there; avoid uploading sensitive data. (2) Prefer generating a throwaway/limited token for testing rather than reusing credentials used elsewhere. (3) Note the SKILL.md frontmatter references a local config path (~/.config/nemovideo/) while the registry metadata does not—ask the author whether the skill will read/write that directory. (4) Because the skill's source/homepage is unknown, exercise extra caution: verify the service domain, and do not expose other secrets (AWS keys, personal tokens) via NEMO_TOKEN or uploads. If you need higher assurance, request the author's homepage/source or a signed provenance record before use.

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

Runtime requirements

💚 Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk97277nf65m4qh6n9jy8wpjn9984y0vw
72downloads
0stars
1versions
Updated 1w ago
v1.0.0
MIT-0

Getting Started

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

Try saying:

  • "generate a two-sentence description of the Hulk smashing through a city into a 1080p MP4"
  • "generate a short video of the Hulk running through a forest based on this text description"
  • "generating Hulk-themed video clips from written descriptions for Marvel fans, content creators, social media users"

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.

Text to Video Hulk — Generate Hulk Videos from Text

Drop your text 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 two-sentence description of the Hulk smashing through a city, ask for generate a short video of the Hulk running through a forest based on this text description, and about 1-3 minutes later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter, more specific text prompts produce more accurate Hulk scenes.

Matching Input to Actions

User prompts referencing text to video hulk, 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.

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

HeaderValue
X-Skill-Sourcetext-to-video-hulk
X-Skill-Versionfrontmatter version
X-Skill-Platformauto-detect: clawhub / cursor / unknown from install path

Every API call needs Authorization: Bearer <NEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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.

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.

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 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)

Error Handling

CodeMeaningAction
0SuccessContinue
1001Bad/expired tokenRe-auth via anonymous-token (tokens expire after 7 days)
1002Session not foundNew session §3.0
2001No creditsAnonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001Unsupported fileShow supported formats
4002File too largeSuggest compress/trim
400Missing X-Client-IdGenerate Client-Id and retry (see §1)
402Free plan export blockedSubscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429Rate limit (1 token/client/7 days)Retry in 30s once

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "generate a short video of the Hulk running through a forest based on this text description" — 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 social platforms.

Common Workflows

Quick edit: Upload → "generate a short video of the Hulk running through a forest based on this text description" → Download MP4. Takes 1-3 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.

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