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 chubbyguan/chubbyskills --skill industry-intelligence-radargit clone --depth 1 https://github.com/chubbyguan/chubbyskillsWrote 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/chubbyguan/chubbyskills/industry-intelligence-radar)<a href="https://agentmods.dev/skills/chubbyguan/chubbyskills/industry-intelligence-radar"><img src="https://agentmods.dev/badge/skills/chubbyguan/chubbyskills/industry-intelligence-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/chubbyguan/chubbyskills/industry-intelligence-radar"><img src="https://agentmods.dev/badge/skills/chubbyguan/chubbyskills/industry-intelligence-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 | $0.00062 | $0.01685 |
| Opus 5 | $0.00031 | $0.00843 |
| Sonnet 5 | $0.00012 | $0.00337 |
| Haiku 4.5 | $0.00006 | $0.00169 |
Grade A, and why
industry-intelligence-radar 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.
How it starts
The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
行业情报雷达
核心价值
信息差优势：早知道 = 早行动 = 早收益
数据源
| 数据源 | 接入方式 | 是否需配置 | 信号强度 |
|---|---|---|---|
| Hacker News | scan.py 内置（Algolia API） |
否（免费） | ⭐⭐⭐⭐ |
| V2EX | scan.py 内置（官方热门 API） |
否（免费） | ⭐⭐⭐ |
| 36kr / 虎嗅 / 少数派等 | scan.py RSS 源（config.rss） |
否（填 RSS 地址即可） | ⭐⭐⭐ |
| X/Twitter | Agent 联网搜索 / X API | 需 X API Key | ⭐⭐⭐⭐⭐ |
| 即刻 | Agent 联网搜索 | 需搜索能力 | ⭐⭐⭐⭐ |
scan.py仅用 Python 标准库即可跑通 HN + V2EX + 任意 RSS 源，零依赖、无需 API Key。 X/即刻没有稳定的公开接口，由 Agent 用联网搜索能力补充（见 Phase 1）。
关键词矩阵
用户兴趣领域
AI/Agent:
- "AI agent" OR "AI 工具" OR "LLM" OR "大模型"
- "Claude" OR "GPT" OR "Gemini" OR "DeepSeek"
- "MCP" OR "tool use" OR "function calling"
半导体:
- "半导体" OR "芯片" OR "chip" OR "semiconductor"
- "台积电" OR "TSMC" OR "英伟达" OR "NVIDIA"
航天:
- "航天" OR "火箭" OR "卫星" OR "SpaceX"
- "星链" OR "Starlink"
新能源:
- "新能源" OR "电动车" OR "电池"
- "特斯拉" OR "Tesla" OR "比亚迪"
游戏:
- "游戏" OR "Steam" OR "Switch" OR "PS5"
- "米哈游" OR "原神" OR "黑神话"
跨境电商:
- "跨境电商" OR "TikTok Shop" OR "SHEIN"
- "独立站" OR "DTC"
创业/投资:
- "创业" OR "融资" OR "投资"
- "YC" OR "a16z" OR "红杉"
工作流程
Phase 1: 多源扫描
① 脚本扫描（HN + V2EX + RSS，免费、可 cron）
# 用内置关键词矩阵扫最近 24 小时，输出简报到文件
python3 scripts/scan.py --hours 24 --output 简报-$(date +%F).md
# 自定义关键词 / 追加 RSS 源（36kr、虎嗅、少数派等）
python3 scripts/scan.py --config keywords.json --hours 24
keywords.json 示例：
{
"keywords": {
"AI/Agent": ["AI agent", "LLM", "Claude", "MCP"],
"半导体": ["半导体", "NVIDIA", "TSMC"]
},
"high_signal": ["融资", "发布", "launch", "raise"],
"rss": ["https://www.36kr.com/feed", "https://sspai.com/feed"]
}
② Agent 联网补充（X / 即刻，需搜索能力）
脚本覆盖不到的实时社交信号，由 Agent 用联网搜索补充，再并入简报：
搜索 X/Twitter："AI agent OR LLM OR 大模型" 最近 24 小时高互动内容
搜索即刻："AI OR Agent OR 创业" 高质量讨论
Phase 2: 信号过滤
过滤规则：
- 去重：同一事件只保留最高质量来源
- 时效：只保留 24 小时内的内容
- 信号强度：
- 🔴 高信号：融资、发布、政策变化、重大合作
- 🟡 中信号：产品更新、行业讨论、观点碰撞
- 🟢 低信号：日常动态、重复信息
Phase 3: 趋势检测
检测维度：
- 突发趋势：某话题突然大量出现
- 持续趋势：某话题持续一周以上高频出现
- 新兴趋势：新概念/新工具首次出现
What ships with it
1 file 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.
- 10d ago First seen · 191 lines · 62 tokens per session scan A 4858c8d01e11
industry-intelligence-radar is a skill published in the GitHub repository chubbyguan/chubbyskills (665 stars, last pushed 22d ago), licensed MIT. It adds 62 tokens to every session and 1,685 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 2026-08-30.
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