Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/AgenticAIPlan/AgenticAISkillsnpx agentmods add skills/agenticaiplan/agenticaiskills/ai-hardware-ecosystem-monitorWrote 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/agenticaiplan/agenticaiskills/ai-hardware-ecosystem-monitor)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/ai-hardware-ecosystem-monitor"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/ai-hardware-ecosystem-monitor/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/agenticaiplan/agenticaiskills/ai-hardware-ecosystem-monitor"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/ai-hardware-ecosystem-monitor.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 | $0.00257 | $0.03597 |
| Opus 5 | $0.00129 | $0.01799 |
| Sonnet 5 | $0.00051 | $0.00719 |
| Haiku 4.5 | $0.00026 | $0.00360 |
Grade A, and why
ai-hardware-ecosystem-monitor 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 12d 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI 硬件生态监控
用途
用这个 Skill 监控 AI 相关多硬件生态新闻、产业动态和风险信号。它的重点不是单一硬件产品口碑，而是围绕 AI 产业链观察上游、中游、下游以及大模型厂商之间的联动关系。
监测重点覆盖：晶圆、先进封装、HBM、GPU / NPU / ASIC、服务器整机、机柜与液冷、网络与光模块、云厂商、数据中心、企业部署，以及 OpenAI、Anthropic、Google、Meta、xAI、DeepSeek、阿里、百度、腾讯、字节、智谱等模型厂商。
不要编造新闻、合作、产能、价格、交付、情绪或风险等级。遇到来源不足、时间不明、引用链条断裂、价格未核验或爆料未证实时，必须明确披露。
适用场景
在以下场景优先使用本 Skill：
- 跟踪 AI 硬件上下游新闻和产业链变化
- 观察 GPU、NPU、ASIC、HBM、服务器、液冷、网络等关键环节动态
- 监控大模型厂商、云厂商和硬件供应商的合作、采购、部署和替代关系
- 跟踪新产品发布、路线图、交付、扩产、价格、制裁和政策变化
- 输出日报、周报、专题复盘、竞争态势分析和风险预警
监测对象
按以下层级组织监测对象：
- 上游：晶圆、先进封装、HBM / DRAM、基板、互连、散热、电源、交换芯片、光模块
- 中游：GPU、NPU、ASIC、加速卡、服务器整机、AI 一体机、机柜、液冷系统、ODM / OEM
- 下游：云厂商、数据中心、企业客户、行业解决方案商、开发者生态
- 模型厂商：OpenAI、Anthropic、Google、Meta、xAI、DeepSeek 及国内外大模型厂商
- 外部环境：政策、制裁、出口管制、资本开支、融资、并购、行业标准
工作流
步骤 1：明确监测范围
先补齐以下信息：
- 监测主体：品牌、厂商、机构或产业链环节
- 重点对象：产品线、技术环节、供应链节点、模型厂商、云厂商
- 时间范围：默认近 7 天；重大事件建议 24 小时或 72 小时滚动监控
- 重点议题：产能、交付、价格、路线图、合作、部署、兼容、政策、资本开支
- 竞品范围：明确主要对手、替代方案和对比对象
- 模块范围：
news、supply-chain、model-vendor、competitor、kol、risk、all
如果用户没有给全，优先补足“监测主体、重点对象、时间范围、重点议题、竞品范围”五项。
步骤 2：组织关键词包
至少准备 6 组关键词：
- 核心词：品牌名、产品名、型号、SKU、代号
- 上游词：wafer、CoWoS、HBM、substrate、封装、良率、光模块、电源、散热
- 中游词：GPU、NPU、ASIC、server、rack、liquid cooling、interconnect、NIC
- 下游词：cloud、datacenter、inference、deployment、enterprise、cluster、capex
- 模型厂商词：OpenAI、Anthropic、Google、Meta、xAI、DeepSeek 等
- 风险词：缺货、跳票、断供、制裁、出口管制、过热、兼容问题、价格波动、砍单
优先覆盖中英文、简称、代号、旧型号和行业常用说法。
步骤 3：规划采集路径
至少覆盖 4 类来源：
- 行业媒体 / 科技媒体：Tom's Hardware、AnandTech、ServeTheHome、SemiAnalysis、36氪、量子位、机器之心
- 官方与合作伙伴页面：厂商博客、产品页、驱动更新日志、伙伴新闻稿、客户案例
- 社区与开发者站点：Reddit、GitHub Issues、Hacker News、Chiphell、V2EX
- 模型厂商与云厂商信息源：OpenAI、Google、Meta、Anthropic、阿里云、AWS、Azure、GCP 等博客或公告
- 社交与视频平台：微博、小红书、B站、抖音、X、LinkedIn
- 资本与政策信息源：财报摘要、监管公告、政策网站、行业协会与研究机构
以下采集能力属于可选前置 Skill / 工具，不是硬依赖：
web-research：行业媒体、论坛、官网、伙伴页面调研chrome-devtools、playwright-mcp：复杂页面、动态页面、价格页、评论页抓取daily-hot-news：微博 / 知乎 / B站 / 抖音热点观察wechat-article-to-markdown：公众号文章抓取xiaohongshu：消费级硬件与装机内容搜索arxiv-search：论文、研究资料、技术趋势检索
What ships with it
9 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.
- assets/report_template.md 3.0 KB
- references/data_schema.md 2.8 KB
- references/kol_framework.md 1.9 KB
- references/monitoring_guide.md 3.2 KB
- references/risk_assessment.md 2.2 KB
- references/sentiment_analysis.md 1.7 KB
- references/source_integration.md 2.7 KB
- references/taxonomy.md 2.4 KB
- scripts/ai_hardware_ecosystem_monitor.py 51 KB runs code
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.
- 12d ago First seen · 264 lines · 257 tokens per session scan A 752babc61e8b
ai-hardware-ecosystem-monitor is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 257 tokens to every session and 3,597 once invoked, about $0.0013 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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