Text Caption

Skip the learning curve of professional editing software. Describe what you want — add text captions to my video with accurate timing — and get captioned vid...

Security Scan

The skill's declared functionality (cloud video captioning) matches its runtime instructions, but there are small inconsistencies (local config path mentioned in frontmatter but not in registry) and it will upload user video files and create/hold tokens for a third‑party service with no homepage or publisher details — verify before use.

Purpose & Capability

Name/description describe cloud-based captioning and the SKILL.md exclusively references a Nemo video API and upload/render flows. Required credential (NEMO_TOKEN) and the endpoints are coherent with the stated purpose.

Instruction Scope

Runtime instructions focus on session creation, uploading user-supplied media, SSE streaming, polling render status and returning download URLs. The skill does not instruct reading unrelated system files or other credentials in the provided instructions.

Install Mechanism

Instruction-only skill with no install spec and no code files — nothing is written to disk by an installer step. Lowest install risk.

Credentials

Declared primary env var NEMO_TOKEN is appropriate for a third‑party API. However SKILL.md frontmatter includes a configPaths entry (~/.config/nemovideo/) despite the registry metadata listing no required config paths — this mismatch could indicate hidden behavior (reading local configs) or sloppy metadata. The skill also offers to mint an anonymous token via the API if no NEMO_TOKEN is present, which is consistent but means the skill will obtain and store credentials for 7-day tokens on behalf of the user.

Persistence & Privilege

always:false and default autonomous invocation are used (normal). The skill does not request permanent platform-wide presence or attempt to modify other skills. Attribution header rules request detection of install path for platform labeling (reads path strings), which is limited but worth noting.

What to consider before installing

This skill will upload any video you provide to a third‑party backend (mega-api-prod.nemovideo.ai) and uses a NEMO_TOKEN (or will request an anonymous token it obtains for you). Before installing or using it: 1) Confirm you trust the service — there is no homepage or publisher information in the registry. 2) Don't upload sensitive videos until you verify the provider's privacy/retention policy. 3) Check your system for ~/.config/nemovideo/ (the skill's frontmatter references it) and remove any credentials you don't want accessed. 4) Prefer supplying your own NEMO_TOKEN tied to an account you control (rather than letting the skill mint anonymous tokens) if you need auditability. 5) If you need higher assurance, ask the publisher for source code or a documented privacy/security policy; lack of that increases risk.

Getting Started

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

Try saying:

• "add my video clips"
• "export 1080p MP4"
• "add text captions to my video"

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.

Text Caption — Add Captions to Your Videos

Drop your video clips 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 2-minute tutorial video in MP4 format, ask for add text captions to my video with accurate timing, and about 30-60 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 60 seconds process noticeably faster.

Matching Input to Actions

User prompts referencing text caption, 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.

Base URL: https://mega-api-prod.nemovideo.ai

Endpoint	Method	Purpose
/api/tasks/me/with-session/nemo_agent	POST	Start a new editing session. Body: {"task_name":"project","language":"<lang>"}. Returns session_id.
/run_sse	POST	Send a user message. Body includes app_name, session_id, new_message. Stream response with Accept: text/event-stream. Timeout: 15 min.
/api/upload-video/nemo_agent/me/<sid>	POST	Upload a file (multipart) or URL.
/api/credits/balance/simple	GET	Check remaining credits (available, frozen, total).
/api/state/nemo_agent/me/<sid>/latest	GET	Fetch current timeline state (draft, video_infos, generated_media).
/api/render/proxy/lambda	POST	Start export. Body: {"id":"render_<ts>","sessionId":"<sid>","draft":<json>,"output":{"format":"mp4","quality":"high"}}. Poll status every 30s.

Accepted file types: 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: text-caption
• 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.

Error Handling

Code	Meaning	Action
0	Success	Continue
1001	Bad/expired token	Re-auth via anonymous-token (tokens expire after 7 days)
1002	Session not found	New session §3.0
2001	No credits	Anonymous: show registration URL with ?bind=<id> (get <id> from create-session or state response when needed). Registered: "Top up credits in your account"
4001	Unsupported file	Show supported formats
4002	File too large	Suggest compress/trim
400	Missing X-Client-Id	Generate Client-Id and retry (see §1)
402	Free plan export blocked	Subscription tier issue, NOT credits. "Register or upgrade your plan to unlock export."
429	Rate limit (1 token/client/7 days)	Retry in 30s once

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.

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 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)

Common Workflows

Quick edit: Upload → "add text captions to my video with accurate timing" → 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.

Tips and Tricks

The backend processes faster when you're specific. Instead of "make it look better", try "add text captions to my video with accurate timing" — 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.