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Maker Free Ai

v1.0.0

Get finished video files ready to post, without touching a single slider. Upload your images or clips (MP4, MOV, JPG, PNG, up to 500MB), say something like "...

0· 10·0 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 susan4731-wilfordf/maker-free-ai.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Maker Free Ai" (susan4731-wilfordf/maker-free-ai) from ClawHub.
Skill page: https://clawhub.ai/susan4731-wilfordf/maker-free-ai
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 maker-free-ai

ClawHub CLI

Package manager switcher

npx clawhub@latest install maker-free-ai
Security Scan
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Benign
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OpenClawOpenClaw
Suspicious
medium confidence
Purpose & Capability
Name/description describe cloud video creation and the SKILL.md exclusively calls a nemo-video API and file upload endpoints — asking for NEMO_TOKEN is coherent. However registry metadata (required config paths: none) disagrees with the skill frontmatter (metadata lists ~/.config/nemovideo/), which is an inconsistency to clarify.
Instruction Scope
Runtime instructions are focused on session creation, SSE streaming, uploads, and exports to the specified API — all expected for a render-as-a-service video tool. Points to watch: the skill directs generating/saving a token if none exists, long-lived SSE connections (up to 15 minutes) and uploads of large files (up to 500MB). The frontmatter implies detecting an install path / config path for platform/version attribution which could require reading agent install/config locations; the doc does not explicitly justify reading arbitrary config beyond the nemo config directory.
Install Mechanism
Instruction-only (no install spec, no code files). This is lower-risk because nothing is written or downloaded by an automated installer. Network calls will still occur at runtime.
Credentials
Only NEMO_TOKEN is declared (primaryEnv). That is proportionate for a hosted API. The skill also provides an anonymous-token flow to generate a temporary token, so it does not strictly require pre-existing secrets. The implicit frontmatter configPaths (~/.config/nemovideo/) suggests the agent may look for local stored tokens/config — reasonable but not documented in the instruction steps, creating ambiguity about what local files will be read.
Persistence & Privilege
always:false and normal autonomous invocation settings. The skill asks the agent to save a session_id/token for the session lifecycle (expected). It does not request always:true or system-wide config modifications.
What to consider before installing
This skill looks like a normal cloud video creation integration, but before installing you should: 1) confirm the publisher or a homepage/source code so you can verify who runs mega-api-prod.nemovideo.ai; 2) ask whether the agent will read ~/.config/nemovideo/ (and what it contains) and whether any local files beyond user-uploaded media will be accessed; 3) prefer using a throwaway/anonymous token (the SKILL.md provides an anonymous-token flow) rather than a long-lived personal credential; 4) avoid uploading sensitive or private files (IDs, unreleased assets) until you trust the service; 5) consider network and storage implications of uploading large files and long-lived SSE streams; and 6) revoke any token after use or set a short expiry if possible. If the publisher/source code or clarifying documentation are provided, re-evaluating could raise confidence to benign.

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

Runtime requirements

🎬 Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk97azkwpbk6vt0cdhpb20jnhcx85qg2n
10downloads
0stars
1versions
Updated 3h ago
v1.0.0
MIT-0

Getting Started

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

Try saying:

  • "create five product photos and a logo file into a 1080p MP4"
  • "turn my photos into a 30-second promotional video with music and text"
  • "creating videos from images or clips without paid software for small business owners and content creators"

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.

Maker Free AI — Create Videos with AI Free

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

Say you have five product photos and a logo file and want to turn my photos into a 30-second promotional video with music and text — the backend processes it in about 30-60 seconds and hands you a 1080p MP4.

Tip: using fewer than ten images keeps render time under a minute.

Matching Input to Actions

User prompts referencing maker free ai, 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-Sourcemaker-free-ai
X-Skill-Versionfrontmatter version
X-Skill-Platformauto-detect: clawhub / cursor / unknown from install path

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 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 "turn my photos into a 30-second promotional video with music and text" — concrete instructions get better results.

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

Export as MP4 for widest compatibility.

Common Workflows

Quick edit: Upload → "turn my photos into a 30-second promotional video with music and text" → Download MP4. Takes 30-60 seconds 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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