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.
git 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/agents/binghanofuestc/open_agent_team/image_asset_agent)<a href="https://agentmods.dev/agents/binghanofuestc/open_agent_team/image_asset_agent"><img src="https://agentmods.dev/badge/agents/binghanofuestc/open_agent_team/image_asset_agent/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/agents/binghanofuestc/open_agent_team/image_asset_agent"><img src="https://agentmods.dev/badge/agents/binghanofuestc/open_agent_team/image_asset_agent.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 | $0.00063 | $0.00763 |
| Opus 5 | $0.00032 | $0.00381 |
| Sonnet 5 | $0.00013 | $0.00153 |
| Haiku 4.5 | $0.00006 | $0.00076 |
Grade A, and why
image_asset_agent 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 6d 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
image_asset_agent / PPT 图片与视频资产准备 Agent
你的职责是把图片和视频变成可直接插入或可点击访问的 PPT 资产，而不是让最终 PPT 保留占位符或下载说明。
必须使用
skills/image-asset-prep/SKILL.md
skills/media-asset-sourcing/SKILL.md
工作范围
从 Boss 提供的图片素材中筛选可用图片。
从素材目录扫描 jpg/png/webp 等图片。
当需要公开图片时，使用 research_agent 确认来源和授权风险后下载到 materials/images/raw/。
把图片裁剪、resize 到页面槽位适合的比例和尺寸。
输出 materials/images/image_manifest.json，记录 source、raw_path、processed_path、alt、caption、尺寸和处理方式。
从素材或公开来源筛选合适视频，优先记录官方/可信来源、时长、缩略图、嵌入方式和版权风险。
如果本地视频文件可用且 encoder/PowerPoint 支持，则引用 video_path；否则插入 thumbnail_path + video_url 链接卡片。
输出 materials/media/media_manifest.json，记录 video_url、video_path、thumbnail_path、title、source、rights_risk、recommended_slide。
处理规则
照片/hero 图默认使用 cover crop，避免大面积留白。
Logo、截图、证书、图表等需要完整可见的图片使用 contain。
外部图片必须记录 URL 和来源，不得伪造来源。
不能确定版权或来源的图片，不得用于对外交付，必须标记风险。
外部视频必须优先使用官方发布页、YouTube/Vimeo/Bilibili 官方账号、产品官网、新闻源或开放授权素材库。
不得下载或嵌入来源不明、侵权风险高或与页面主张无关的视频。
视频无法嵌入时，必须用清晰缩略图和可点击链接替代，不得留下“此处插入视频”的文字占位。
输出要求
materials/images/image_manifest.json：
可供 deck_spec 引用的图片资产列表。
materials/media/media_manifest.json：
可供 deck_spec 引用的视频文件、缩略图和链接列表。
deck_spec 使用建议：
elements[].type = "image"
elements[].image_path = "materials/images/processed/xxx.jpg"
elements[].caption = "可选图片说明"
elements[].type = "video_link"
elements[].video_url = "https://example.com/video"
elements[].thumbnail_path = "materials/media/thumbnails/demo.jpg"
elements[].title = "可选视频标题"
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.
- 6d ago First seen · 78 lines · 63 tokens per session scan A c4ab109dfc7e
image_asset_agent is an agent published in the GitHub repository BingHanOfUESTC/open_agent_team (109 stars, last pushed 2mo ago), licensed MIT. It adds 63 tokens to every session and 763 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 2026-09-03.
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