Borrowing it
Nothing to install: this file belongs to alwkala/tidyfactor-design. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/alwkala/tidyfactor-design/main/.agents/skills/tidyfactor-design/references/commands/assets.mdgit clone --depth 1 https://github.com/alwkala/tidyfactor-designWrote 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/commands/alwkala/tidyfactor-design/assets)<a href="https://agentmods.dev/commands/alwkala/tidyfactor-design/assets"><img src="https://agentmods.dev/badge/commands/alwkala/tidyfactor-design/assets/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/commands/alwkala/tidyfactor-design/assets"><img src="https://agentmods.dev/badge/commands/alwkala/tidyfactor-design/assets.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 | $0.00000 | $0.00410 |
| Opus 5.5 | $0.00000 | $0.00164 |
| Sonnet 5.5 | $0.00000 | $0.00082 |
| Haiku 4.5 | $0.00000 | $0.00041 |
Grade A, and why
assets 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 2mo 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
Command: assets — Asset Processing & Media Optimization Engine
Runtime entry point for generating AI media, removing image backgrounds, and optimizing image payloads.
5-Layer AI Photography Prompt Construction Matrix
When generating visual assets with generate_image or external AI image engines, construct prompts across 5 technical photography layers:
- Subject Layer: Primary subject, micro-textures, pose, surface materials (e.g. brushed aluminum, matte leather).
- Environment Layer: Setting, time of day, atmospheric conditions (fog, volumetric haze, studio backdrop).
- Lighting Layer: Source (softbox, natural light), direction (Rembrandt, split, backlit), color temperature.
- Technical Photography Layer: Perspective, focal length (85mm f/1.4), depth of field / bokeh (
f/1.8shallow focus). - Post-Processing & Film Stock Layer: Color grading, subtle film grain, Kodak Portra 400 aesthetic.
Python Asset Tooling
- Background Removal:
python scripts/remove_backgrounds.py <input_path>(rembg + Pillow transparent PNG cutouts). - Batch Optimization:
python scripts/optimize_images.py <assets_dir>(compress WebP variants). - Asset Inspection:
python scripts/inspect_images.py <assets_dir>(dimension & size budget check).
Output Convention
my-prototype/
└── assets/
├── hero-cutout.png ← BG-removed cutout
├── banner.webp ← WebP compressed asset
└── photo-01.webp ← 5-Layer AI prompt generated asset
Checklist
- AI image prompts structured across all 5 technical photography layers
- Product cutouts have clean transparent backgrounds (
rembg) - Prototype assets optimized to WebP format (< 500KB payload per image)
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.
- 2mo ago First seen · 36 lines · 0 tokens per session scan A 4e9f69273502
assets is a command published in the GitHub repository alwkala/tidyfactor-design (6 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 410 tokens. 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.
assets compared with commands from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| assetsalwkala/tidyfactor-design | Command | 6 | 3mo ago | MIT | A | 0 | Claude Code |
| agentloop-audit-queryalibaba/loongsuite-pilot | Command | 239 | yesterday | Apache-2.0 | A | 5 | Claude Code |
| critique-screenOwl-Listener/designer-skills | Command | 3.3k | 2mo ago | MIT | A | 16 | Claude Code |
| critique-uxOwl-Listener/designer-skills | Command | 3.3k | 2mo ago | MIT | A | 26 | Claude Code |
| pull-repostravisjneuman/.claude | Command | 107 | yesterday | MIT | A | 13 | Claude Code |
| design-onboardingOwl-Listener/designer-skills | Command | 3.3k | 2mo ago | MIT | A | 21 | Claude Code |
| frame-problemOwl-Listener/designer-skills | Command | 3.3k | 2mo ago | MIT | A | 15 | Claude Code |