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 skills add BingHanOfUESTC/open_agent_team --skill image-asset-prepgit clone --depth 1 https://github.com/BingHanOfUESTC/open_agent_teamWrote 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/binghanofuestc/open_agent_team/image-asset-prep)<a href="https://agentmods.dev/skills/binghanofuestc/open_agent_team/image-asset-prep"><img src="https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/image-asset-prep/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/binghanofuestc/open_agent_team/image-asset-prep"><img src="https://agentmods.dev/badge/skills/binghanofuestc/open_agent_team/image-asset-prep.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00554 |
| Opus 5 | $0.00000 | $0.00277 |
| Sonnet 5 | $0.00000 | $0.00111 |
| Haiku 4.5 | $0.00000 | $0.00055 |
Grade A, and why
image-asset-prep 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 7d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image Asset Prep
Use this skill when a PPT needs real images from Boss-provided materials or public search results. For video search/linking, use it together with media-asset-sourcing.
Purpose
Prepare image assets before PPTX rendering so the encoder can insert concrete local files instead of leaving placeholders or asking the author to download images manually.
Tool
python teams/ppt_writer_team/skills/image-asset-prep/scripts/prepare_images.py \
--scan-dir source_materials \
--output-dir materials/images \
--aspect-ratio 16:9 \
--width 1600 \
--height 900
For selected URLs or local files:
python teams/ppt_writer_team/skills/image-asset-prep/scripts/prepare_images.py \
--input local/photo.jpg \
--url https://example.com/image.jpg \
--output-dir materials/images \
--aspect-ratio 4:3
For a curated manifest:
python teams/ppt_writer_team/skills/image-asset-prep/scripts/prepare_images.py \
--manifest materials/image_requests.json \
--output-dir materials/images
Manifest Shape
[
{
"id": "hero_product",
"source": "downloads/product.jpg",
"alt": "Product in use",
"aspect_ratio": "16:9",
"fit": "cover"
},
{
"id": "market_chart_photo",
"url": "https://example.com/photo.jpg",
"alt": "Storefront image",
"aspect_ratio": "4:3"
}
]
Outputs
materials/images/raw/
materials/images/processed/
materials/images/image_manifest.json
image_manifest.json contains id, original source, processed local path, dimensions, aspect ratio and alt/caption metadata. deck_spec.json should reference the processed local path or the image id from this manifest.
When preparing thumbnails for videos, save them under:
materials/media/thumbnails/
and record them in materials/media/media_manifest.json.
Rules
Prefer images from Boss-provided material when available.
When using public web images, save the image into materials/images/raw/ and record the source URL.
Never leave final slides with "download image" instructions.
Crop/resize before rendering so image boxes are visually full and do not contain avoidable empty margins.
Use cover crop for hero/photo slots and contain only when the full image must remain visible.
Do not use images whose source or rights are unclear for external-facing decks without review.
What ships with it
2 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.
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.
- 7d ago First seen · 86 lines · 0 tokens per session scan A 920125ba3a58
image-asset-prep is a skill published in the GitHub repository BingHanOfUESTC/open_agent_team (106 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 554 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 2026-09-03.
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