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.
npx agentmods add commands/alwkala/tidyfactor-design/assetsgit 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.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00410 |
| Opus 5 | $0.00000 | $0.00205 |
| Sonnet 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 4d 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.
- 4d ago First seen · 36 lines · 0 tokens per session scan A 4e9f69273502
assets is a command published in the GitHub repository alwkala/tidyfactor-design (5 stars, last pushed 1mo 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.
Other commands, from other repositories
github-issue
Create a GitHub issue from a natural language description.
test
Hello world test command.
coograph-verify
Verify that the described work is complete and correct. Provide evidence for every claim. You verify — you do not implement or fix style.
prune
Trim transcript clutter to extend session lifetime — analyze, prune a copy, or toggle the after-each-turn service. Dry-run by default; gains land at resume/compaction, not the current turn.
pr-description
Generate a PR title and description from the current branch diff against main.
conflict
Stop everything and surface a rule conflict — persona vs. docs vs. code. Present both sides and the conflict-hierarchy level; the user resolves. No silent reconciliation.