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 yunshu0909/yunshu_skillshub --skill case-radargit clone --depth 1 https://github.com/yunshu0909/yunshu_skillshubWrote 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/yunshu0909/yunshu_skillshub/case-radar)<a href="https://agentmods.dev/skills/yunshu0909/yunshu_skillshub/case-radar"><img src="https://agentmods.dev/badge/skills/yunshu0909/yunshu_skillshub/case-radar/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/yunshu0909/yunshu_skillshub/case-radar"><img src="https://agentmods.dev/badge/skills/yunshu0909/yunshu_skillshub/case-radar.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 | $0.00168 | $0.03968 |
| Opus 5 | $0.00084 | $0.01984 |
| Sonnet 5 | $0.00034 | $0.00794 |
| Haiku 4.5 | $0.00017 | $0.00397 |
Grade A, and why
case-radar scanned grade A with 1 finding 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 13d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| 3 | capture | 拉真物（curl 直接资源 + agent-browser 截图） | curl + agent-browser | How it starts
The opening of the file, as written. The whole thing — 299 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Case Radar · 案例雷达
把"想了解新东西"这件事从"读三手 SEO 文" → "拿到一手真物（截图/源码/演示）"。
输入：一个新东西的名字 + 一句话上下文（"我想用来做 X"）。 输出：一份可浏览的 HTML 案例集，每张卡都配真实物料（不是文字描述）。
核心信念：菜单一文不值，菜才是真东西。GitHub repo 主页是文件列表（菜单），README 里的截图、商店页的安装数、SKILL.md 的原文段落、产品落地页的真实建站效果——才是真东西（菜）。
流程概览
| 阶段 | 名称 | 目标 | 主要工具 |
|---|---|---|---|
| 0 | 环境自检 | 确认 agent-browser 装了 + 主题清晰 | Bash + 对话 |
| 1 | scan | 多渠道扫信源，列出候选案例 | WebSearch / Agent（不重新发明，直接调用） |
| 2 | recon | 给每个候选侦察"真物位置"——核心增值 | 读 reference/recon-heuristics.md |
| 3 | capture | 拉真物（curl 直接资源 + agent-browser 截图） | curl + agent-browser |
| 4 | embed | 套 HTML 模板输出 | 参考 reference/html-template-spec.md |
每一步都可中断。用户随时可以说"停"、"跳过这一步"、"回到上一步"。
阶段 0：环境自检 + 主题对齐
0.1 检查 agent-browser 是否装好
command -v agent-browser
- ❌ 未装 → 告诉用户："这个 Skill 依赖 vercel-labs/agent-browser 抓截图。一行命令装：
brew install agent-browser && agent-browser install。装完回来。" 终止。 - ✅ 已装 → 继续
注意：
agent-browser install会下 Chrome 二进制（~169 MB）。如果在中国大陆，可能下载失败。但实测即使 install 卡住，agent-browser 也能复用机器上已有的 Playwright Chromium（~/Library/Caches/ms-playwright/）。所以即使 install 报错，先试一下agent-browser open https://example.com，能跑就跳过 install。
0.2 检查 gh CLI 已登录（recon 阶段要用）
gh auth status
- ❌ 未登录 → 提示
gh auth login，终止 - ✅ → 继续
0.3 主题对齐——3 个问题，最多问一次
用 AskUserQuestion 一次性问完，不要反复 ping-pong：
- 新东西的名字和上下文（"Claude Skills 生态" / "MCP 最新玩法" / "AI Agent 框架对比" 等）
- 用途：① 为研究/产品开发服务（深度优先）② 为内容创作服务（兼顾"我能记住"和"未来能引用"）③ 纯求知/拓展视野（广度优先）④ 三种都要，按事走（边做边定）
- 范围圈：① 锚定一个生态（如 Claude Skills）② 一个工具/产品（如 cline、cursor）③ 一个概念（如 MCP、Subagent）④ 不设限，临时定
⚠️ 这一步不要扩展成讨论会。3 个问题问完就动手。如果用户答不上某条，给一个合理默认值（用途默认"三种都要"，范围默认"锚定一个生态"），继续。
阶段 1：scan · 多渠道扫信源
不重新发明——直接调 WebSearch 或起一个 Agent 跑扫描。
1.1 起一个 scan agent（推荐）
如果案例可能 >20 个，用 Agent 工具起一个 general-purpose subagent 并行跑扫描，避免污染主上下文。Prompt 关键点：
- 信源分级要求：一手源（官方文档/作者博客/原推文/changelog）/ 二手优质（HN 高分贴 / Reddit / Simon Willison 这类深度玩家 / 知名工程师博客）/ 三手中文（公众号/知乎/CSDN，只要有"独立观察"的，纯翻译稿不要）
- 严格砍掉：基础教程、awesome-list（meta 仓库，除非 list 里被点名的好案例）、纯 SaaS API 包装、官方文档操作类示范
- 输出格式：每条
[标题](URL) — 1 句话简介 + 为什么值得深挖 - 数量：目标 25-40 个合格案例
- 末尾要求 100-200 字"扫描印象"（生态全景：3 大热门 / 3 大空白 / 1 个最强信号）
What ships with it
4 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.
- 13d ago First seen · 299 lines · 168 tokens per session scan A e861bdf40f69
case-radar is a skill published in the GitHub repository yunshu0909/yunshu_skillshub (757 stars, last pushed 1mo ago), licensed MIT. It adds 168 tokens to every session and 3,968 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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