Video Hindi Ai

Turn a 3-minute YouTube video in Hindi into 1080p Hindi captioned videos just by typing what you need. Whether it's adding Hindi subtitles to videos using AI...

Security Scan

The skill's stated purpose (add Hindi subtitles via a cloud backend) aligns with most of its instructions, but there are small inconsistencies and a few runtime file/metadata reads that aren't declared — you should review the external service and the token usage before installing.

Purpose & Capability

The skill claims to run video subtitle generation via a cloud render backend and the SKILL.md exclusively targets API calls to mega-api-prod.nemovideo.ai using an API token. Requiring a NEMO_TOKEN and calling the described endpoints is proportional to the stated purpose. However, the skill frontmatter also lists configPaths (~/.config/nemovideo/) and an install-path detection mechanism (checking ~/.clawhub/ and ~/.cursor/skills/) that are not reflected in the registry summary (which reported no required config paths). This mismatch is a coherence issue (metadata mismatch) but not itself a direct malicious signal.

Instruction Scope

The runtime instructions will: read NEMO_TOKEN from the environment (expected), POST to the backend to create an anonymous token if none exists (also documented), upload user video files, and poll/export results. In addition, the skill instructs the agent to read this skill file's YAML frontmatter at runtime and to detect install paths in the user's home to set X-Skill-Platform — those steps require reading local file paths and the SKILL.md content. Reading local install/config paths is not strictly required for subtitle generation and was not declared in the registry metadata; this broader filesystem access is a scope creep and should be acknowledged before use.

Install Mechanism

This is an instruction-only skill with no install spec and no code files, so nothing is written to disk by an installer. That is the lowest install risk.

Credentials

The skill requests a single credential (NEMO_TOKEN) which is proportional to a cloud backend client. The SKILL.md also describes a flow to obtain an anonymous token from the backend if NEMO_TOKEN is not present (the agent POSTs to /api/auth/anonymous-token and uses the returned data.token). That behavior is reasonable for an anonymous usage flow but means the skill will contact an external auth endpoint and store/use the returned token for all subsequent requests. The SKILL.md also references reading install/config paths and local frontmatter (not declared as required in the registry summary), which expands the environment/data it touches beyond just the NEMO_TOKEN.

Persistence & Privilege

The skill does not request always:true, no install scripts, and no system-level configuration changes are indicated. It keeps session tokens for the runtime session only (as needed). Autonomous invocation is allowed (platform default) but not combined with unusual persistence or system-wide changes.

What to consider before installing

What to consider before installing: - Trust the backend: the skill will upload your videos and use a token for all requests to https://mega-api-prod.nemovideo.ai. Only proceed if you trust that service's privacy and retention policies. - Token behavior: the skill will use NEMO_TOKEN from your environment if present; otherwise it will obtain an anonymous token by calling the auth endpoint. If you don't want automatic token creation, do not provide the skill with shell access that can create/persist tokens. - Local file access: the skill intends to read its own SKILL.md frontmatter and check common install/config paths (~/.config/nemovideo/, ~/.clawhub/, ~/.cursor/skills/) to set an attribution header. These filesystem reads are not declared in the registry summary and are not strictly necessary for subtitle work — decide whether you're comfortable with that. - Minimal red flags, but exercise caution: metadata inconsistencies and the undeclared filesystem checks make this suspicious rather than clearly benign. If you plan to use it, verify the service domain, consider providing your own scoped NEMO_TOKEN (with limited permissions/expiry), and avoid installing in high-trust environments until you confirm the backend's policies.

Getting Started

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

Try saying:

• "generate a 3-minute YouTube video in Hindi into a 1080p MP4"
• "add Hindi subtitles to my video automatically"
• "adding Hindi subtitles to videos using AI for Hindi content creators"

Quick Start Setup

This skill connects to a cloud processing backend. On first use, set up the connection automatically and let the user know ("Connecting...").

Token check: Look for NEMO_TOKEN in the environment. If found, skip to session creation. Otherwise:

• Generate a UUID as client identifier
• POST https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with X-Client-Id header
• Extract data.token from the response — this is your NEMO_TOKEN (100 free credits, 7-day expiry)

Session: POST https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer auth and body {"task_name":"project"}. Keep the returned session_id for all operations.

Let the user know with a brief "Ready!" when setup is complete. Don't expose tokens or raw API output.

Video Hindi AI — Generate Hindi Subtitles Automatically

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

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

Tip: shorter clips under 5 minutes get the most accurate Hindi transcription.

Matching Input to Actions

User prompts referencing video hindi ai, 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.

All calls go to https://mega-api-prod.nemovideo.ai. The main endpoints:

1. Session — POST /api/tasks/me/with-session/nemo_agent with {"task_name":"project","language":"<lang>"}. Gives you a session_id.
2. Chat (SSE) — POST /run_sse with session_id and your message in new_message.parts[0].text. Set Accept: text/event-stream. Up to 15 min.
3. Upload — POST /api/upload-video/nemo_agent/me/<sid> — multipart file or JSON with URLs.
4. Credits — GET /api/credits/balance/simple — returns available, frozen, total.
5. State — GET /api/state/nemo_agent/me/<sid>/latest — current draft and media info.
6. Export — POST /api/render/proxy/lambda with render ID and draft JSON. Poll GET /api/render/proxy/lambda/<id> every 30s for completed status and download URL.

Formats: mp4, mov, avi, webm, mkv, jpg, png, gif, webp, mp3, wav, m4a, aac.

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

• X-Skill-Source: video-hindi-ai
• 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.

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)

Backend Response Translation

The backend assumes a GUI exists. Translate these into API actions:

Backend says	You 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

SSE Event Handling

Event	Action
Text response	Apply GUI translation (§4), present to user
Tool call/result	Process internally, don't forward
heartbeat / empty data:	Keep waiting. Every 2 min: "⏳ Still working..."
Stream closes	Process 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.

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 "add Hindi subtitles to my video automatically" — 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 Hindi streaming platforms.

Common Workflows

Quick edit: Upload → "add Hindi subtitles to my video automatically" → 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.