Caption Generator Youtube

Get captioned YouTube videos ready to post, without touching a single slider. Upload your YouTube video files (MP4, MOV, AVI, WebM, up to 500MB), say somethi...

Security Scan

The skill's requests and runtime instructions are largely consistent with a cloud captioning service, but there are small metadata mismatches and you should be aware that your video files and a token (NEMO_TOKEN or an anonymously generated token) are sent to an external API domain.

Purpose & Capability

Name/description (YouTube caption generation) align with the actions described: uploading videos, generating captions on cloud GPUs, and exporting MP4s. Requesting a service token (NEMO_TOKEN) is proportionate to this functionality. Note: SKILL.md frontmatter lists a config path (~/.config/nemovideo/) while the registry metadata indicated no required config paths—this is an internal inconsistency.

Instruction Scope

SKILL.md instructs the agent to obtain/use NEMO_TOKEN (or request an anonymous token), create sessions, upload files (multipart or URL), receive SSE updates, and poll render endpoints. Those instructions stay within the captioning workflow, but they explicitly cause user video files to be uploaded to https://mega-api-prod.nemovideo.ai (expected for a cloud service). The skill also requires adding attribution headers and auto-detecting platform info (which may read install path).

Install Mechanism

Instruction-only skill with no install spec and no code files; nothing is written to disk by an installer. This is the lower-risk installation model.

Credentials

Only a single credential (NEMO_TOKEN) is declared as required and is appropriate for an external API. The instructions also support creating an anonymous token via the service's anonymous endpoint if no env var is set, which reduces the need for a persistent secret. No unrelated credentials are requested.

Persistence & Privilege

always:false and normal model invocation are used. The skill does not request system-wide configuration changes or permanent agent-level privileges. It does ask to 'save session_id' but does not instruct modifying other skills or global settings.

Assessment

This skill appears to do what it says: it will upload your videos to an external service (mega-api-prod.nemovideo.ai) to generate captions. Before installing, consider: (1) Don’t upload sensitive or private footage without checking the service’s privacy/terms and trustworthiness of the domain. (2) If you don’t want a persistent token in your environment, rely on the anonymous-token flow or avoid setting NEMO_TOKEN globally. (3) Note the small inconsistency: the SKILL.md references a config path (~/.config/nemovideo/) that the registry didn’t list—ask the developer whether the skill reads or writes files there. (4) If you need higher assurance, request the skill’s source or an audited endpoint domain and verify the API hostname and TLS certificate before use.

Getting Started

Ready when you are. Drop your YouTube video files here or describe what you want to make.

Try saying:

• "add a 10-minute YouTube tutorial video into a 1080p MP4"
• "add captions in English and Spanish to my YouTube video"
• "adding subtitles to YouTube videos for YouTubers"

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.

Caption Generator for YouTube — Generate and Embed Video Captions

Drop your YouTube video files in the chat and tell me what you need. I'll handle the AI caption generation on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a a 10-minute YouTube tutorial video, ask for add captions in English and Spanish to my YouTube video, and about 30-90 seconds later you've got a MP4 file ready to download. The whole thing runs at 1080p by default.

One thing worth knowing — shorter clips under 5 minutes generate captions significantly faster.

Matching Input to Actions

User prompts referencing caption generator youtube, 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 requests must include: Authorization: Bearer <NEMO_TOKEN>, X-Skill-Source, X-Skill-Version, X-Skill-Platform. Missing attribution headers will cause export to fail with 402.

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

Header	Value
X-Skill-Source	caption-generator-youtube
X-Skill-Version	frontmatter version
X-Skill-Platform	auto-detect: clawhub / cursor / unknown from install path

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

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

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)

Common Workflows

Quick edit: Upload → "add captions in English and Spanish to my YouTube video" → Download MP4. Takes 30-90 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.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "add captions in English and Spanish to my YouTube 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 YouTube and other platforms.