Getting Started

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.

Edit Any Image With Just a Description

Most image editing tools demand hours of learning curves, layer management, and manual adjustments. This skill flips that entirely — you describe the change you want, and it handles the execution. Want the background swapped to a clean white studio look? Need the lighting warmed up and the shadows softened? Just say so.

The ai-image-editor skill is built for people who care about the outcome, not the process. It understands natural language instructions and translates them into precise visual edits. Crop, recolor, retouch, resize, remove unwanted elements, enhance sharpness, or apply a completely new aesthetic — all through conversation.

It works equally well for quick fixes and complex transformations. A product photographer might use it to batch-standardize backgrounds, while a social media manager might use it to adapt a single image for multiple platform dimensions. Whatever your goal, the workflow stays simple: describe what you want, review the result, refine if needed.

Routing Your Edit Requests

Each prompt you send — whether it's background removal, inpainting, style transfer, or upscaling — is parsed for intent and dispatched to the matching image processing pipeline automatically.

Cloud Processing API Reference

All edits run through a cloud-hosted vision API that applies diffusion models, segmentation masks, and enhancement algorithms on the server side, so no heavy processing happens on your device. Rendered outputs are returned as high-resolution image URLs or base64 payloads depending on your configured response format.

Skill attribution — read from this file's YAML frontmatter at runtime:

• X-Skill-Source: ai-image-editor
• X-Skill-Version: from frontmatter version
• X-Skill-Platform: detect from install path (~/.clawhub/ → clawhub, ~/.cursor/skills/ → cursor, else 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.

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.

SSE Event Handling

~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:

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)

Error Handling

Common Workflows

The most frequent use case for this ai-image-editor skill is product photography cleanup — sellers on platforms like Etsy, Shopify, or Amazon regularly need backgrounds removed, colors normalized, and images resized to spec. A single clear instruction handles what would otherwise take 20 minutes in Photoshop.

Portrait retouching is another high-demand workflow. Rather than manually masking skin or adjusting curves, users describe the look they want: 'soften the skin without losing texture,' 'reduce redness around the nose,' or 'make the eyes pop a little more.' The skill interprets these naturally and applies targeted adjustments.

Social media teams frequently use this skill to repurpose a single image across formats — cropping a 4:3 photo to a 9:16 Story, adjusting the focal point, and adapting the color palette for brand consistency. Describe the platform and the vibe, and the edits follow automatically.

Integration Guide

This ai-image-editor skill fits naturally into content production pipelines. If you're managing a brand with recurring image needs — product launches, campaign assets, blog visuals — you can build a repeatable editing brief and apply it consistently across every image batch.

For teams, the skill works well as a first-pass editing layer before final human review. Feed in raw images with a standard prompt describing your brand's visual style, and receive consistently edited outputs ready for approval. This cuts review time significantly compared to starting edits from scratch.

If you're using ClawHub alongside other tools like Notion, Slack, or a CMS, you can paste image URLs or upload files directly within your workflow context. The skill accepts both uploaded files and linked images, making it easy to slot into wherever your content lives without switching between platforms.