Hindi Editor Ai

Cloud-based hindi-editor-ai tool that handles editing videos with Hindi subtitles and captions. Upload MP4, MOV, AVI, WebM files (up to 500MB), describe what...

Security Scan

The skill's requirements and runtime instructions are mostly coherent with a cloud video-editing tool, but there are a few small inconsistencies and privacy considerations to review before installing.

Purpose & Capability

Name/description (cloud Hindi video editing, subtitles, exports) align with the runtime instructions: the SKILL.md describes uploading video files, creating sessions, sending SSE messages, and exporting MP4s using an external NemoVideo API. Requesting a single service token (NEMO_TOKEN) is expected for this purpose.

Instruction Scope

Instructions are focused on the editor workflow (session creation, SSE, upload, export). They also instruct the agent to derive some headers from the skill's YAML frontmatter and detect the agent install path (~/.clawhub, ~/.cursor/skills) which implies reading its install context; this is plausible but worth noting. The skill will instruct the agent to read files provided by the user for upload (expected).

Install Mechanism

No install spec or code files — instruction-only skill (lowest install risk). No downloads or third-party packages are requested.

Credentials

Declared required credential is one token (NEMO_TOKEN), which fits a cloud API. However the SKILL.md frontmatter lists a config path (~/.config/nemovideo/) that could contain credentials or user data; the registry summary earlier reported no required config paths — this mismatch should be clarified. Also note that if NEMO_TOKEN is absent the skill will request an anonymous token from https://mega-api-prod.nemovideo.ai, which is reasonable but means requests will be made to that domain.

Persistence & Privilege

Skill is not forced-always, uses default autonomous invocation setting, and does not request system-wide changes. No evidence it modifies other skills or agent-wide config beyond deriving headers from its frontmatter.

Assessment

This skill appears to do what it says: it uploads user video files to a cloud API and returns edited exports, and it needs one service token (NEMO_TOKEN). Before installing: 1) Confirm you trust the unknown publisher and the API domain (mega-api-prod.nemovideo.ai) since video uploads may contain sensitive content. 2) Clarify the config-path discrepancy: SKILL.md mentions ~/.config/nemovideo/ while registry metadata listed none — that folder may hold credentials or cached data. 3) Understand that if you don't provide NEMO_TOKEN the skill will obtain an anonymous token from the public API (ephemeral credits). 4) Avoid uploading highly sensitive material unless you verify the service's privacy policy and operator. If you want higher assurance, ask the publisher for a homepage, documentation, or an official source before use.

Getting Started

Ready when you are. Drop your raw video footage here or describe what you want to make.

Try saying:

• "edit a 2-minute Hindi vlog recording into a 1080p MP4"
• "add Hindi subtitles and trim pauses from my video"
• "editing videos with Hindi subtitles and captions for Hindi content creators"

Getting Connected

Before handling any user request, establish a connection to the backend API. Show a brief status like "Connecting...".

If NEMO_TOKEN is in the environment, use it directly and create a session. Otherwise, acquire a free starter token:

• Generate a UUID as client identifier
• POST to https://mega-api-prod.nemovideo.ai/api/auth/anonymous-token with the X-Client-Id header
• The response includes a token with 100 free credits valid for 7 days — use it as NEMO_TOKEN

Then create a session by POSTing to https://mega-api-prod.nemovideo.ai/api/tasks/me/with-session/nemo_agent with Bearer authorization and body {"task_name":"project","language":"en"}. The session_id in the response is needed for all following requests.

Tell the user you're ready. Keep the technical details out of the chat.

Hindi Editor AI — Edit and Caption Hindi Videos

This tool takes your raw video footage and runs AI Hindi editing through a cloud rendering pipeline. You upload, describe what you want, and download the result.

Say you have a 2-minute Hindi vlog recording and want to add Hindi subtitles and trim pauses from my video — the backend processes it in about 1-2 minutes and hands you a 1080p MP4.

Tip: shorter clips under 3 minutes process significantly faster.

Matching Input to Actions

User prompts referencing hindi editor 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.

Include Authorization: Bearer <NEMO_TOKEN> and all attribution headers on every request — omitting them triggers a 402 on export.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is hindi-editor-ai, 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).

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.

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

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

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.

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)

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "add Hindi subtitles and trim pauses from my 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 Hindi streaming platforms.

Common Workflows

Quick edit: Upload → "add Hindi subtitles and trim pauses from my video" → 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.