Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/ProdaZhang/aigdnpx agentmods add skills/prodazhang/aigd/aigd-ui-captureWrote 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/skills/prodazhang/aigd/aigd-ui-capture)<a href="https://agentmods.dev/skills/prodazhang/aigd/aigd-ui-capture"><img src="https://agentmods.dev/badge/skills/prodazhang/aigd/aigd-ui-capture/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/skills/prodazhang/aigd/aigd-ui-capture"><img src="https://agentmods.dev/badge/skills/prodazhang/aigd/aigd-ui-capture.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 | $0.00126 | $0.01415 |
| Opus 5 | $0.00063 | $0.00707 |
| Sonnet 5 | $0.00025 | $0.00283 |
| Haiku 4.5 | $0.00013 | $0.00142 |
Grade A, and why
aigd-ui-capture 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
UI screenshot → UI DSL (tool 1 · capturer)
Reads a UI screenshot into one UI DSL (.md): structure / type / hierarchy / geometry / interaction. It is the input to the deterministic scripts ui_render.py (tool 2, restore) and ui_slice.py (tool 3, slice). This skill orchestrates the flow; the grammar / recipe always defers to ../aigd/references/ui-dsl-spec.md (no need to change this doc when the spec updates).
Step 0: read the source of truth (do every time, use Read, don't go from memory)
| File | What to take |
|---|---|
../aigd/references/ui-dsl-spec.md |
File skeleton + Layout-line grammar + type table + z layering + shapes + source semantics + read-the-image recipe (§7) + skin/theme (§9) |
../aigd/references/scripts/ui_render.py |
Restore + missing-z reminder + calibration export (for verifying the artifact) |
../aigd/references/scripts/ui_palette.py |
Sample colors from the original image → write a ## Skin section (for adding color schemes to the library) |
../aigd/references/scripts/ui_slice.py |
Image + DSL → per-element slices + index.md contact sheet (extract assets, optional) |
../aigd/references/ui-dsl-example.md |
The shape of one package-compliant DSL (example screen) |
Step 1: confirm the input
Need one screenshot + screen ID + source (competitor name / own) + a one-line purpose. Ask if missing. Competitor images default to "discard the original once converted, the DSL goes into the library".
Step 2: produce the DSL per the read-the-image recipe (spec §7)
Strictly follow the order of spec §7: screen header → palette → Layout (outer to inner / top to bottom: containers before leaves) → Events → design review. Every element must be tagged type + @{x y w h} + z=N (the renderer allows omitting z, but capture into the library must tag it fully — you're transcribing a real image, the hierarchy must be recorded accurately); a circle is tagged shape=circle, selected/locked tagged [state], a full-screen base image @{0 0 100 100} z=1. Competitor data is only tagged :observed / :inferred, never write :canonical. Eyeballing the geometry as a percentage is fine (precision is handled by the calibration in step 3). Colors don't go into element lines — step 3 uses ui_palette.py --merge to sample colors and write them into a ## Skin section (spec §9).
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.
- 8d ago First seen · 59 lines · 126 tokens per session scan A d885991e1bca
aigd-ui-capture is a skill published in the GitHub repository ProdaZhang/aigd (2 stars, last pushed 2mo ago), licensed MIT. It adds 126 tokens to every session and 1,415 once invoked, about $0.0006 per session on Opus 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.
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