Your AI doesn't know which button you mean
SlimSnap is a macOS utility that converts screenshots into structured JSON data specifically built for AI coding agents. It solves the fundamental gap between visual screenshots and text-only terminal environments. Developers using Claude Code, Aider, Codex CLI, and other command-line agents often need to describe UI elements in tedious paragraphs because these agents cannot see images. SlimSnap bridges this by extracting every element's text, position, color, and bounding box into a deterministic JSON format. The core value proposition is that your AI finally has eyes — it can reason about specific buttons, input fields, and labels without guessing. This screenshot-to-JSON approach not only makes communication precise but also significantly reduces token consumption, letting agents spend more context on actual code rather than pixel interpretation.
The primary pain point SlimSnap addresses is the inability of terminal-based AI coding agents to accept image inputs. When a developer wants to discuss a UI element — say a misaligned button or an error message — they must write a lengthy text description of its appearance, location, and context. This is slow, imprecise, and often leads to agent confusion. Moreover, pasting screenshots into cloud-based models costs tokens proportional to the image size, even for small crops. SlimSnap eliminates this by producing a compact JSON representation that captures exactly what matters: element types, values, coordinates, and colors. For iterative debugging sessions, this translates to substantial time and cost savings. Developers can point at the broken thing with an annotation and paste structured data that the agent consumes instantly, without ambiguity about which element is which.
The SlimSnap workflow begins with capture. Users press ⌘⇧S, drag to select any screen area, and release — a native macOS interaction requiring no additional installation. The captured screenshot immediately opens in an editing interface where annotations can be added. Arrows, callouts, and highlights let users visually point at the exact element of interest, such as a broken button or an error state. This annotation step is crucial because it communicates intent directly within the visual data. The agent will see the annotation as part of the JSON, understanding which element was being highlighted. By combining capture and annotation in one seamless step, SlimSnap eliminates the need for separate image editing tools or verbose text explanations. The entire process takes seconds, keeping developers in their workflow.
After capture and annotation, a single click copies the entire scene as a JSON object. The JSON includes schema version, capture timestamp, screen metadata, and an array of elements — each with id, type, value, bounding box in normalized 0-to-1 coordinates, and optional color. This structured data is directly consumable by any text-based agent. The token savings are substantial: a typical screenshot in Claude Code bills 1,568 vision tokens per paste, whereas SlimSnap JSON of the same screen averages 600-800 tokens — about 55% fewer on Sonnet and up to 85% fewer on Opus 4.7 and 4.8. Over a long iterative session, these savings compound, freeing up context window space for actual code and reasoning. Pasting the JSON is as simple as typing text, enabling agents like Claude Code, Aider, and Codex CLI to instantly understand the UI layout.
Publisher
A
admin
Launch Date2026-06-12
Pricingfree
Domain Authority
Ahrefs DR1/100