rab-icon-grid-processing

rab-icon-grid-processing is a skill for Claude Code, Codex from BloomBooks/BloomDesktop. It costs 62 tokens per session (1,979 once invoked), scanned A, original, MIT.

A workflow for turning icon grids, sprite sheets, folders, or padded PNG files into Reading App Builder icons. Reading App Builder is the part of Bloom that uses these icons in app-building layouts.

In plain words
What is it for?
Use it to split icon sheets, crop excess transparent space, repad or flatten PNGs, update icon discovery paths and tests, and validate sample icons before a larger batch.
Why use it?
It fixes common asset problems such as icons appearing too small, being stored in the wrong structure, or not being discovered by the application. It encourages checking sample results before processing everything.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/bloombooks/bloomdesktop/rab-icon-grid-processing
Any agent
npx skills add BloomBooks/BloomDesktop --skill rab-icon-grid-processing
Clone the repo
git clone --depth 1 https://github.com/BloomBooks/BloomDesktop

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for rab-icon-grid-processing

README.md
[![agentmods](https://agentmods.dev/badge/skills/bloombooks/bloomdesktop/rab-icon-grid-processing.svg)](https://agentmods.dev/skills/bloombooks/bloomdesktop/rab-icon-grid-processing)
Your own site
<a href="https://agentmods.dev/skills/bloombooks/bloomdesktop/rab-icon-grid-processing"><img src="https://agentmods.dev/badge/skills/bloombooks/bloomdesktop/rab-icon-grid-processing.svg" alt="Measured on agentmods" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,979 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00062 $0.01979
Opus 5 $0.00031 $0.00989
Sonnet 5 $0.00012 $0.00396
Haiku 4.5 $0.00006 $0.00198

Measured 4d ago against content hash 911ba96c11da, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

rab-icon-grid-processing 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 4d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (cropAppBuilderIconSamples.ps1, extractAppBuilderIconGridSamples.ps1, flattenAppBuilderIcons.mjs), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

.github/skills/rab-icon-grid-processing/SKILL.md · 144 lines

How it starts

The opening of the file, as written. The whole thing — 144 lines — stays where its author put it; the contents beside it link to each section on GitHub.

RAB Icon Grid Processing

Outcome

Turn incoming icon artwork into usable Reading App Builder icon assets that are visually legible at app-icon size, stored in the layout Bloom expects, and validated before broad rollout.

When To Use

  • You receive a grid, sprite sheet, or batch of candidate icon images for Reading App Builder.
  • The icons are technically valid PNGs but look too small because of excessive transparent padding.
  • The icons are stored one-per-folder and need to be flattened into a single bundled icon directory.
  • Bloom's RAB icon discovery code needs to be updated to match an asset-layout change.
  • You want to try one or two sample icons first before touching the entire set.

Default Assumptions

  • Target icons should remain square PNGs with transparent backgrounds.
  • A 512x512 master icon is the safest working size when repadding or regenerating icon art.
  • Unless the user says otherwise, name outputs bloom-app-icon-<number>.
  • In this repo, bundled App Builder icons live under DistFiles/appbuilder-icons.
  • If the asset layout changes, update both the backend discovery path and the tests in the same pass.
  • Prefer process tasks or direct scripts over bash-heavy image tooling if the shell environment is unstable.

Inputs To Confirm

  • What is the incoming source: a grid image, sprite sheet, flat PNG batch, or folder-per-icon bundle?
  • Is the task visual-only, layout-only, or both?
  • Should the agent run a one-or-two-icon pilot first, or has the user already approved a full batch?
  • Are filenames already the desired final IDs, or do they need renaming?
  • Does Bloom code currently assume the old bundled layout?
  • What is the narrowest validation available after the change?

Core Workflow

  1. Inspect the incoming asset set. Determine whether the source is a single grid, a sprite sheet, flat PNG files, or a folder-per-icon layout.
  2. Identify the controlling problem before editing. Decide whether the main issue is splitting, transparent padding, directory layout, naming, or code compatibility.
  3. Start with a representative sample. Pick one or two icons that clearly show the problem and use them as a reversible pilot.
  4. If the icons are already individual files but too padded, crop to visible alpha bounds. Measure visible pixels using alpha, trim the transparent border, then redraw onto a square transparent canvas with a consistent margin.
  5. If the assets are stored one-per-folder, flatten them only after checking for filename collisions. Move the PNGs into the target root, remove emptied folders, and keep the filename-based identity stable.
  6. If the source is a grid or sprite sheet, split it into individual transparent PNGs before any crop-and-repad pass. Use the grid cell geometry or visible bounds consistently across the full batch.
  7. Validate the pilot visually before batch rollout. Open the updated sample icons and confirm they read better without clipping or awkward framing.
  8. Only after the pilot succeeds, run the same transformation across the remaining icons.
  9. If Bloom code depends on the old asset layout, update the code path and tests in the same change.
  10. Finish with the narrowest executable validation available and note any unrelated blockers separately.

Read the full file on GitHub · 144 lines

Files

What ships with it

3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 4d ago First seen · 144 lines · 62 tokens per session scan A 911ba96c11da

Subscribe to this mod's changes

rab-icon-grid-processing is a skill published in the GitHub repository BloomBooks/BloomDesktop (44 stars, last pushed today), licensed MIT. It adds 62 tokens to every session and 1,979 once invoked, about $0.0003 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 2026-08-30.

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