Image To Video Hd

Skip the learning curve of professional editing software. Describe what you want — turn my photos into a smooth HD video with transitions — and get HD video...

Security Scan

The skill's behaviour mostly matches a cloud-based image→video service, but there are small inconsistencies around declared config paths and how the required token is handled that you should understand before installing.

Purpose & Capability

The skill's name and description align with the runtime instructions which call a nemo-video cloud API and upload images for rendering. However the SKILL.md frontmatter metadata lists a config path (~/.config/nemovideo/) while the registry metadata reported no required config paths — this mismatch is unexplained and worth asking about. Otherwise requested headers and the NEMO_TOKEN credential are coherent with a cloud render service.

Instruction Scope

The instructions are focused on interacting with the remote API (auth, session creation, upload, SSE streaming, render/poll). They do not instruct reading arbitrary system files or unrelated credentials, and they explicitly say not to expose tokens or raw API output. The anonymous-token acquisition flow (POST to mega-api-prod.nemovideo.ai) is in-scope for a cloud-first skill.

Install Mechanism

This is an instruction-only skill with no install spec and no code files, so there is no install-time code to write to disk — lowest-risk install model.

Credentials

The skill declares a single required env var (NEMO_TOKEN), which fits the claimed cloud API usage. Two informal inconsistencies: (1) the SKILL.md implements an anonymous-token flow if NEMO_TOKEN is missing, meaning the env var is not strictly necessary; (2) the frontmatter references a config path (~/.config/nemovideo/) that the registry summary did not list — it's unclear whether the skill will read that directory. Both items are not necessarily malicious but are unexplained and worth clarifying.

Persistence & Privilege

always is false and the skill does not request system-wide changes or access to other skills' configs. Autonomous model invocation is allowed but is the platform default; nothing in the instructions asks for persistent privileges beyond keeping an in-memory session token/session_id.

What to consider before installing

This skill appears to be a cloud-based photo→video converter that uploads your images to mega-api-prod.nemovideo.ai and uses a NEMO_TOKEN for authorization. Before installing: (1) confirm you are comfortable with your images being uploaded to that domain and review the service's privacy/retention policy; (2) ask the publisher why the frontmatter mentions ~/.config/nemovideo/ (will the skill read or write that directory?) and why the skill marks NEMO_TOKEN as required when it can obtain an anonymous token itself; (3) avoid putting highly sensitive images or secrets in chats handled by the skill; and (4) if you prefer tighter control, provide your own NEMO_TOKEN rather than relying on the anonymous-token flow so you can revoke it later. If the author can clarify the config-path usage and token persistence, the remaining concerns are minor.

Getting Started

Share your still images and I'll get started on AI video creation. Or just tell me what you're thinking.

Try saying:

• "convert my still images"
• "export 1080p MP4"
• "turn my photos into a smooth"

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.

Image to Video HD — Convert Photos into HD Video

Drop your still images in the chat and tell me what you need. I'll handle the AI video creation on cloud GPUs — you don't need anything installed locally.

Here's a typical use: you send a three product photos in JPG format, ask for turn my photos into a smooth HD video with transitions, 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 — using fewer than 10 images keeps render time under a minute.

Matching Input to Actions

User prompts referencing image to video hd, 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.

Headers are derived from this file's YAML frontmatter. X-Skill-Source is image-to-video-hd, 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).

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.

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.

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

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 → "turn my photos into a smooth HD video with transitions" → 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 "turn my photos into a smooth HD video with transitions" — concrete instructions get better results.

Max file size is 200MB. Stick to JPG, PNG, WEBP, HEIC for the smoothest experience.

Export as MP4 for widest compatibility across platforms and devices.