Caption Generator Hitler

add video clips into captioned video files with this skill. Works with MP4, MOV, AVI, WebM files up to 500MB. educators, documentary creators, history conten...

Security Scan

The skill's declared purpose (cloud captioning) matches most of its runtime instructions, but there are metadata inconsistencies and a few behaviours (probing install paths / config paths, uploading user videos to an external API) that you should review before installing or providing credentials.

Purpose & Capability

The skill's name/description (captioning historical videos) aligns with the runtime actions: obtaining a token, creating a session, uploading videos, requesting renders at nemovideo.ai. Requesting NEMO_TOKEN as the primary credential is expected. However the frontmatter metadata mentions a config path (~/.config/nemovideo/) even though the registry metadata listed no required config paths — that's an inconsistency worth questioning.

Instruction Scope

Instructions require contacting an external API and uploading user media (expected for cloud captioning). They also instruct the agent to read this file's frontmatter for attribution headers and to detect the agent's install path (~/.clawhub/ or ~/.cursor/skills/) to set X-Skill-Platform — that requires probing local filesystem/paths beyond simply using a provided token. While these actions can be justified for attribution, they broaden the skill's scope and should be explicitly authorized.

Install Mechanism

No install spec or code files — instruction-only skill. No downloads or archive extraction. This is lower risk since nothing is written/executed locally by an installer.

Credentials

The only declared required env var is NEMO_TOKEN (primaryEnv), which fits the described API usage. But the SKILL.md frontmatter also lists a configPaths entry (~/.config/nemovideo/) that could expose local config files if the agent follows that metadata; the registry-level requirements did not declare this. Confirm whether the skill will actually read that path and what it would look for before supplying credentials.

Persistence & Privilege

The skill does not request always:true or other elevated persistence. It stores short-lived session IDs for render jobs (normal). It does not declare modifying other skills or system-wide settings.

What to consider before installing

This skill will upload your video files to an external service (mega-api-prod.nemovideo.ai) and requires a NEMO_TOKEN to authenticate. Before installing or invoking: (1) Verify you are comfortable uploading the video content (privacy/rights implications). (2) Prefer the anonymous token flow if you don't want to supply a long-lived credential; check how long tokens/credits last. (3) Ask the skill author (or registry) to clarify the configPaths/inconsistency — will the agent read ~/.config/nemovideo/ or probe installation paths? If so, what will it read and why? (4) Confirm the service's domain and review its privacy/retention policy if you plan to upload sensitive footage. (5) If you have concerns, don't provide any persistent/privileged credentials; use ephemeral/anonymous tokens or test with non-sensitive media first.

Getting Started

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

Try saying:

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

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 — Add Captions to Historical Videos

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

Here's a typical use: you send a a 3-minute documentary clip about World War II history, ask for add captions to a historical video about Hitler and Nazi Germany, 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 5 minutes process significantly faster.

Matching Input to Actions

User prompts referencing caption generator hitler, 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: caption-generator-hitler
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else unknown)

Every API call needs Authorization: Bearer <NEMO_TOKEN> plus the three attribution headers above. If any header is missing, exports return 402.

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

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 captions to a historical video about Hitler and Nazi Germany" → 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 captions to a historical video about Hitler and Nazi Germany" — 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.