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 international-lead-researchgit 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/international-lead-research)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/international-lead-research"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/international-lead-research/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/international-lead-research"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/international-lead-research.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 | $0.00109 | $0.03684 |
| Opus 5 | $0.00055 | $0.01842 |
| Sonnet 5 | $0.00022 | $0.00737 |
| Haiku 4.5 | $0.00011 | $0.00368 |
Grade A, and why
international-lead-research 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 — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
International Lead Research
自动调研国际化线索（agent、企业、平台、工具），抓取并总结信息，分析与 PaddleOCR/ERNIE 的合作可能性。
适用场景
当用户需要对国际化线索进行调研时，使用本 Skill。线索类型包括但不限于：
- AI Agent 产品
- 海外企业或公司
- 技术平台或工具
- 其他具有潜在合作价值的国际化产品或服务
触发关键词
以下关键词或相似表达都会触发本 Skill：
- 调研线索
- 分析线索
- 搜集线索信息
- 合作可能性分析
- 线索整理
- 收集信息
- 分析 agent
- 平台功能总结
- 线索信息总结
输入要求
- 线索名称：agent、企业、平台或工具的准确名称（例如：Perplexity） 或
- 网站链接：线索的官网或相关页面 URL
输入类型处理差异：
- 线索名称：先通过 Google 搜索找到官网，然后继续访问 GitHub、社交媒体等其他数据源补充信息
- 网站链接：直接访问该网站作为信息源，但仍需搜索 GitHub、社交媒体等渠道补充完整信息
- 例如：用户输入
https://apify.com→ 直接访问官网获取基础信息 → 继续搜索 GitHub、社交媒体补充技术细节和社区动态
- 例如：用户输入
输入处理规则
中文名称处理
如果用户输入的是中文名称（如"困惑"），需要先通过搜索引擎找到对应的英文官方名称，再继续后续调研流程。
示例：
- 用户输入："困惑" → 搜索确认对应 "Perplexity" → 继续调研
- 用户输入："开放AI" → 搜索确认对应 "OpenAI" → 继续调研
别名与变体处理
用户输入可能是产品别名、域名变体或拼写变体，需要自动识别并扩展搜索关键词：
| 输入类型 | 示例 | 处理方式 |
|---|---|---|
| 域名变体 | perplexity.ai | 自动识别为 Perplexity |
| 常见别名 | chatgpt | 识别为 ChatGPT / OpenAI |
| 拼写变体 | midjourny | 自动纠正为 Midjourney |
| 简称 | cursor | 结合上下文判断是代码编辑器还是其他产品 |
处理原则：
- 如果无法确定用户输入对应的具体产品，先向用户确认
- 搜索时使用扩展关键词提高匹配准确度
同名消歧处理
当用户输入的名称对应多个不同产品时，需要先进行消歧，向用户确认具体是哪一个。
消歧触发条件：
- 搜索结果显示多个同名但功能不同的产品
- 官网 GitHub 存在多个同名仓库且功能差异明显
- 社交媒体或社区讨论中存在多个同名产品
消歧处理流程：
-
收集候选产品信息：收集所有可能匹配的产品，至少包含：
- 产品名称
- 官网/代码库链接
- 一句话功能描述
- 所属领域/行业
-
向用户提问：以清晰格式列出候选产品，让用户选择
示例：
检测到 "Massive" 对应多个产品，请确认你要调研的是哪一个？
选项 产品 功能描述 官网 1 Massive 数据抓取/API绕过平台 massive.dev 2 Massive AI 3D游戏场景生成平台 massive.ai -
等待用户确认：用户选择后，继续调研指定产品
-
未确认处理：如果用户无法确认，提供以下选项：
- 建议用户提供更具体的信息（如领域、功能关键词）
- 或询问是否需要分别调研所有候选产品
数据源优先级
按以下优先级搜索信息，每个数据源都要尝试访问，确保信息完整性：
- Google 搜索：获取基础信息和相关链接
- 官方网站：了解官方介绍、功能定位、产品文档
- GitHub：了解代码库、开源情况、技术实现
- 社交媒体：Twitter / Facebook / LinkedIn，了解用户反馈和社区动态
- 其他来源：技术论坛、AI 相关社区、媒体报道等
信息完整性原则：
- 不因从官网获取到部分信息就跳过后续数据源
- 不同数据源提供不同维度的信息，相互补充
- 官网：官方定位、功能描述
- GitHub：技术实现、开源程度
- 社交媒体：用户反馈、社区动态
- 如后续数据源提供的信息与前面冲突，优先以官方网站信息为准
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 · 346 lines · 109 tokens per session scan A c07eb640ebc5
international-lead-research is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 109 tokens to every session and 3,684 once invoked, about $0.0005 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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