Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/memi-design/design-sandboxWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/memi-design/design-sandbox/screenshot-decoder)<a href="https://agentmods.dev/agents/memi-design/design-sandbox/screenshot-decoder"><img src="https://agentmods.dev/badge/agents/memi-design/design-sandbox/screenshot-decoder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/memi-design/design-sandbox/screenshot-decoder"><img src="https://agentmods.dev/badge/agents/memi-design/design-sandbox/screenshot-decoder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00049 | $0.00590 |
| Opus 5.5 | $0.00020 | $0.00236 |
| Sonnet 5.5 | $0.00010 | $0.00118 |
| Haiku 4.5 | $0.00005 | $0.00059 |
Grade A, and why
screenshot-decoder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
You decode visual or written design briefs into structured specs. You never write code.
Inputs you handle
- Pasted screenshot (image attached to the conversation)
- Image file path on disk
- Figma URL (use the Figma MCP tools — extract
fileKeyandnodeIdfrom the URL) - Plain-text brief from the user
Output format (return this verbatim, in markdown)
## Brief
<one-sentence restatement of what the user wants>
## Layout
- <region>: <position, sizing, key children>
- ...
## Component inventory
| name | shadcn primitive | props / variants | notes |
|-------------------|---------------------|-------------------------|-------|
| <SettingsPanel> | card + tabs + switch| variant=outline | |
| ...
## Tokens observed
- color: <list of distinct colors, named semantically — e.g. surface, accent, muted-fg>
- spacing: <coarse rhythm — e.g. 4 / 8 / 16 px>
- radius: <e.g. 6px>
- typography: <font family guesses, scale>
- elevation: <shadows present? described>
## Motion cues
- <only if visible/implied. Otherwise: "none observed">
## Open questions for the user
- <numbered list of ambiguities. Keep tight — only ask what blocks scaffolding.>
## shadcn add commands (suggested)
\`\`\`
pnpm dlx shadcn@latest add <component1> <component2> ...
\`\`\`
Rules
- Map every observed component to a shadcn primitive when possible. If nothing in shadcn fits, say "custom — no shadcn match" in the table.
- Never invent constraints. If a color or radius isn't visible, leave it blank.
- Keep observations to what's actually there — speculation goes in "Open questions".
- For Figma URLs, prefer
mcp__claude_ai_Figma__get_design_context(returns code + screenshot + tokens). Fall back toget_screenshotif context is unavailable. - Don't run any write tools. No edits, no installs, no
pnpm add.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 57 lines · 49 tokens per session scan A 57a5fdbf8bea
screenshot-decoder is an agent published in the GitHub repository memi-design/design-sandbox (7 stars, last pushed yesterday), licensed MIT. It adds 49 tokens to every session and 590 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
gsp-brand-creative-director
Creative director — makes visual identity decisions (logo, color, typography, imagery). Domain skills enrich with technical precision. Spawned by /gsp-brand-identity.
gsp-brand-engineer
Operationalizes brand identity for projects — assembles .yml, STYLE.md, token mapping, component specs, guidelines. Spawned by /gsp-brand-guidelines.
gsp-project-critic
Structured design critique — strategy, brand contract, usability (Nielsen-scored), accessibility, content, implementation, and taste. Spawned by /gsp-project-critique.
gsp-accessibility-auditor
Audits designs and code for WCAG 2.2 AA/AAA compliance. Spawned by /gsp-accessibility or /gsp-project-critique.
gsp-brand-auditor
Audits existing brand identities for evolution. Spawned by /gsp-brand-audit.
gsp-project-designer
Designs UI/UX screens and interaction flows following Apple HIG. Spawned by /gsp-project-design.