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 baimaomaomao556/eagleeye-mcp --skill eagleeyegit clone --depth 1 https://github.com/baimaomaomao556/eagleeye-mcpWrote 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/baimaomaomao556/eagleeye-mcp/eagleeye)<a href="https://agentmods.dev/skills/baimaomaomao556/eagleeye-mcp/eagleeye"><img src="https://agentmods.dev/badge/skills/baimaomaomao556/eagleeye-mcp/eagleeye/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/baimaomaomao556/eagleeye-mcp/eagleeye"><img src="https://agentmods.dev/badge/skills/baimaomaomao556/eagleeye-mcp/eagleeye.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 | $0.00117 | $0.01218 |
| Opus 5 | $0.00059 | $0.00609 |
| Sonnet 5 | $0.00023 | $0.00244 |
| Haiku 4.5 | $0.00012 | $0.00122 |
Grade A, and why
eagleeye 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 10d 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.
This is a copy
86% identical to eagleeye — 7 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
EagleEye — DSH / Agent Skills 薄包装
这不是第二个 glance/ground CLI。能力在 MCP 工具 eagleeye_* 上。
本文件只规定:何时用、何时不用、先调哪一层。
仓库计划账号:baimaomaomao556。发布后安装 skill:
npx skills add baimaomaomao556/eagleeye-mcp --skill eagleeye
仍须在 Host 里接入 MCP Server(见仓库 docs/HOSTS.md);本 skill 不替代 stdio 进程。
先看工具列表
- 对话里已经出现
eagleeye_capture_screen等工具 → 直接调用,不要自己写 Pillow/Tesseract,也不要改去跑 modlens/vision-tools 的脚本。 - 没有这些工具 → 停下来告诉用户:需要按仓库
docs/HOSTS.md把 EagleEye 以 stdio MCP 接入当前 Host。不要假装已经截到了屏。
不要用 EagleEye 的时候
- 你已经能看见用户贴的图(原生多模态)→ 自己看,不要再截屏。
- 任务只是「读这张已有图片里的字/布局,输出一份 JSON」→ modlens。
- 任务是长截图 OCR、把界面复原成 HTML、把图标描成 SVG → vision-tools。
- 当前模型被用户规则禁止使用外挂视觉 → 遵守用户规则。
要用 EagleEye 的时候
- 需要当前桌面/指定窗口的真实像素(游戏 UI、前端页面、桌面应用)。
- 需要像素级事实:颜色、偏移、模板是否一致、回归有没有坏。
- 需要把一次捕获的图裁开、放大、再量,并且坐标还能追回屏幕。
- 需要量化「画质/审美」分数、做前后对比(改 UI/美术后再截一张评分,看有没有变好)→
score_image(task=aesthetic|quality,模型qrealign-mini默认 /musiq-ava/nima;source_type=model_inference, 非校准概率,用于粗排与对比)。
Windows 上可以用 list_windows / capture_window / capture_window_graphics。
其它平台这些工具会返回 PLATFORM_UNSUPPORTED:改用 capture_screen 或 load_image。
调用顺序
- Observe:需要等界面出现或加载完成时先
wait_for_visual,再list_windows/list_monitors(需要时)→capture_screen/capture_window/load_image。命中后的image_ref可直接给后续工具,不要再截一张。 - Locate:已知坐标 →
crop_image;已知颜色 →find_color;已知模板 →template_match;已知文字 →locate_text/ocr;只知道「是什么」→locate_visual_element(推断,必须复核)。 - Verify:颜色/尺寸用
get_pixel/sample_region/measure_region;差异用compare_images;pass/fail 用compare_with_reference或visual_regression(动态区域配ignore_regions);画质/审美评分用score_image。
优先级:算法视觉 > OCR > 视觉模型 > 组合工具。能算不算,能测不猜。
region 参数可传对象 {x,y,width,height},也兼容 [x,y,w,h] 与 {w,h} 别名。
小尺寸裁剪/放大图(最长边 <512px)送 analyze_image 等前,server 会自动放大预处理
(EAGLEEYE_VISION_MIN_DIM 可配);若仍 VISION_CALL_FAILED,改用更大范围裁剪或直接对整图提问。
capture_window = 用户屏幕上实际看到的窗口(可能被遮挡)。
capture_window_graphics = 应用渲染表面(可后台),不代表用户当前看见的画面。
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.
- 10d ago First seen · 71 lines · 117 tokens per session scan A 4af7b4c4f88b
eagleeye is a skill published in the GitHub repository baimaomaomao556/eagleeye-mcp (0 stars, last pushed 26d ago), licensed MIT. It adds 117 tokens to every session and 1,218 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to eagleeye, differing in 7 lines, and is treated as a copy.
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