Subtitle Generator In Video Free

v1.0.0

add video files into captioned video files with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. YouTubers and content creators use it for addin...

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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 susan4731-wilfordf/subtitle-generator-in-video-free.

Previewing Install & Setup.
Prompt PreviewInstall & Setup
Install the skill "Subtitle Generator In Video Free" (susan4731-wilfordf/subtitle-generator-in-video-free) from ClawHub.
Skill page: https://clawhub.ai/susan4731-wilfordf/subtitle-generator-in-video-free
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 subtitle-generator-in-video-free

ClawHub CLI

Package manager switcher

npx clawhub@latest install subtitle-generator-in-video-free
Security Scan
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Benign
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OpenClawOpenClaw
Benign
high confidence
Purpose & Capability
Name/description match the actions in SKILL.md: uploading video, creating a session, sending SSE, and requesting renders from nemo video API. Requiring a NEMO_TOKEN (Bearer auth) is proportionate. Minor inconsistency: registry metadata reported no required config paths, but the frontmatter metadata declares a configPaths value (~/.config/nemovideo/). That discrepancy should be clarified.
Instruction Scope
Instructions are detailed and stay within the stated purpose (auth, session creation, SSE messaging, upload, export, polling status). They do instruct the agent to read YAML frontmatter at runtime and detect install path (~/.clawhub/ or ~/.cursor/skills/) to set an attribution header; probing install paths is not required for subtitle functionality and leaks local environment information, so it's unnecessary scope creep but not obviously malicious. The skill instructs not to print tokens or raw JSON and to persist session_id, which is expected for sessions.
Install Mechanism
No install spec and no code files — instruction-only skill — lowest-risk installation footprint. Nothing is downloaded or written by an installer step according to the package metadata.
Credentials
Only NEMO_TOKEN is required and listed as primary credential, which fits a cloud API client. The frontmatter also mentions a config path (~/.config/nemovideo/) while registry metadata listed none; this mismatch should be reconciled. The skill also supports generating an anonymous token via a POST when NEMO_TOKEN is not present (reasonable). No unrelated secrets or broad credential access requested.
Persistence & Privilege
always:false and no special persistence or system-wide configuration changes are requested. Saving a session_id for job tracking is reasonable. The skill does not request to modify other skills or system-wide settings.
Assessment
This skill appears to be what it says: it uploads videos to an external nemo video API and uses a NEMO_TOKEN (or obtains an anonymous token) to create sessions and render/callbacks. Before installing/use, confirm: (1) where the NEMO_TOKEN will come from and whether you trust that external service (uploads will leave your machine and go to their cloud); (2) whether you are comfortable the agent will probe local install paths (~/.clawhub or ~/.cursor) to set an attribution header — if that concerns you, ask the skill author to remove that probe; and (3) clarify the config-path discrepancy (frontmatter lists ~/.config/nemovideo/ while registry shows none). If you handle sensitive video content, avoid using the skill until you verify the service's privacy policy and token lifecycle (the skill can also obtain a 7-day anonymous token automatically).

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

Runtime requirements

💬 Clawdis
EnvNEMO_TOKEN
Primary envNEMO_TOKEN
latestvk97c2r4x2qn35gn88j225ct2ws84s8n0
84downloads
0stars
1versions
Updated 2w ago
v1.0.0
MIT-0

Getting Started

Share your video files and I'll get started on AI subtitle generation. Or just tell me what you're thinking.

Try saying:

  • "add my video files"
  • "export 1080p MP4"
  • "generate subtitles in English and add"

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.

Subtitle Generator in Video Free — Auto-Generate and Embed Video Subtitles

This tool takes your video files and runs AI subtitle generation through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 3-minute YouTube tutorial video and want to generate subtitles in English and add them to the video — the backend processes it in about 30-60 seconds and hands you a 1080p MP4.

Tip: shorter clips under 5 minutes process significantly faster.

Matching Input to Actions

User prompts referencing subtitle generator in video free, 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.

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

  • X-Skill-Source: subtitle-generator-in-video-free
  • 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.

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.

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)

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 subtitles in English and add them to the video" — concrete instructions get better results.

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

Export as MP4 for widest compatibility across platforms.

Common Workflows

Quick edit: Upload → "generate subtitles in English and add them to the video" → 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.

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