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 AgenticAIPlan/AgenticAISkills --skill ai-overseas-dailygit clone --depth 1 https://github.com/AgenticAIPlan/AgenticAISkillsWrote 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-overseas-daily)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/ai-overseas-daily"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/ai-overseas-daily/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-overseas-daily"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/ai-overseas-daily.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 | $0.00133 | $0.01913 |
| Opus 5 | $0.00067 | $0.00957 |
| Sonnet 5 | $0.00027 | $0.00383 |
| Haiku 4.5 | $0.00013 | $0.00191 |
Grade A, and why
ai-overseas-daily 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 — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
海外大模型每日情报观察
适用场景
- 需要每日自动生成海外 AI/LLM 领域结构化情报报告的运营或管理团队
- 需要跟踪海外模型厂商商业化动态、数据工程前沿、应用创新趋势的决策者
- 需要将 AI 行业情报自动分发到飞书知识库和如流群的团队协作场景
- 需要定时（如每日工作日早晨）执行情报采集和分析的自动化需求
输入要求
环境配置（首次使用必须完成）
在项目根目录创建 .env 文件，以 assets/.env.example 为模板，填入以下配置：
必填项：
| 变量 | 说明 | 获取方式 |
|---|---|---|
RAPIDAPI_KEY |
RapidAPI 密钥（Twitter、Reddit、Product Hunt 数据抓取） | 注册 rapidapi.com，订阅 twitter-api45、reddit34、product-hunt-scraper-api |
LLM_API_KEY |
报告生成 LLM 的 API Key | OpenAI 兼容 LLM 提供商 |
LLM_BASE_URL |
LLM 接口 Base URL | LLM 提供商的 API 端点 |
LLM_MODEL |
模型名称 | 如 gpt-4o、ernie-5.0-thinking-latest |
FEISHU_APP_ID |
飞书应用 App ID | 飞书开放平台 |
FEISHU_APP_SECRET |
飞书应用 App Secret | 飞书开放平台 |
FEISHU_WIKI_SPACE_ID |
飞书知识库空间 ID | 知识库页面 URL |
RULIU_WEBHOOK_URL |
如流群 Webhook URL | 如流群设置 |
RULIU_GROUP_ID |
如流群 ID（数字） | 如流群设置 |
选填项：
| 变量 | 说明 | 默认值 |
|---|---|---|
BOCHA_API_KEY |
博查搜索 API Key | 未配置则跳过雷达6 |
X_LIST_URL |
Twitter/X 精选 List URL | 未配置则跳过雷达1 |
FEISHU_WIKI_PARENT_NODE_TOKEN |
飞书知识库父节点 | 空（空间根目录） |
FEISHU_BASE_URL |
飞书 API 域名 | https://open.feishu.cn |
FEISHU_WIKI_DOMAIN |
飞书知识库页面域名 | bytedance.feishu.cn |
http_proxy / https_proxy |
代理地址（支持 PAC 自动解析） | 无代理 |
依赖安装
pip install -r scripts/requirements.txt
执行步骤
步骤1：确认配置就绪
检查项目根目录是否存在 .env 文件且必填项已配置。若未配置，引导用户按上方配置表填写。检查 Python 依赖是否已安装。
步骤2：运行情报管线
# 完整运行（采集 + 生成报告 + 分发）
python3 scripts/daily_report.py
# 仅生成报告，不分发（测试用）
python3 scripts/daily_report.py --no-distribute
管线自动执行以下流程：
- 8 雷达数据采集：依次从 Twitter List、Twitter 全网搜索、Hacker News、Reddit、Product Hunt、博查搜索、ArXiv、HuggingFace Papers 采集数据
- 跨源数据清洗：去重、去过期、去空内容，合并为统一情报流
- LLM 情报加工：将清洗后的情报流送入 LLM，按
references/system_prompt.md中的 prompt 生成三板块结构化报告 - 自动分发：飞书知识库（完整报告）→ 如流群（精简摘要 + 飞书链接）
步骤3：验证输出
- 检查脚本输出中各雷达的采集结果（条数）
- 确认报告文件已生成（
YYYY-MM-DD-Overseas-LLM-Insight.md） - 若启用了分发，确认飞书知识库页面和如流群消息发送成功
步骤4（可选）：设置定时调度
# crontab 示例：工作日 9:03 AM 自动执行
3 9 * * 1-5 cd /path/to/project && python3 scripts/daily_report.py >> /path/to/project/cron.log 2>&1
What ships with it
5 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.
- 12d ago First seen · 142 lines · 133 tokens per session scan A 8ec296701c06
ai-overseas-daily is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 133 tokens to every session and 1,913 once invoked, about $0.0007 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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