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 datasets-searchgit 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/datasets-search)<a href="https://agentmods.dev/skills/agenticaiplan/agenticaiskills/datasets-search"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/datasets-search/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/datasets-search"><img src="https://agentmods.dev/badge/skills/agenticaiplan/agenticaiskills/datasets-search.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 | $0.00107 | $0.01722 |
| Opus 5 | $0.00053 | $0.00861 |
| Sonnet 5 | $0.00021 | $0.00344 |
| Haiku 4.5 | $0.00011 | $0.00172 |
Grade A, and why
datasets-search 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 — 174 lines — stays where its author put it; the contents beside it link to each section on GitHub.
数据集搜索 (datasets-search)
爬取网络上关于数据集发布、开源、认证的新闻，自动提取关键信息并生成结构化报告。
核心功能
- 多源新闻爬取 - 从技术媒体、企业博客、开源社区等渠道获取数据集相关新闻
- 智能信息提取 - 自动识别数据集名称、企业名称、数据集描述
- 企业名称扩展 - 将企业简称（如"百度"、"阿里"）自动扩展为全称（如"北京百度网讯科技有限公司"、"阿里巴巴集团控股有限公司"）
- 质量标记 - 识别并标记被认证为"高质量数据集"的内容
- 增量报告 - 对比历史数据，输出本周新增的数据集信息
- 链接提取 - 抓取数据集开源链接、官方描述页面
工作流程
用户请求爬取数据集新闻
│
▼
运行爬虫脚本 → 抓取多个数据源
│
▼
解析提取信息 → 数据集名称/企业/描述/链接
│
▼
企业名称扩展 → 简称→全称映射
│
▼
质量标记识别 → 标记高质量数据集
│
▼
生成增量报告 → 对比历史输出新增内容
│
▼
返回结构化列表 → Markdown格式报告
使用方法
1. 爬取最新数据集新闻
python3 scripts/dataset_crawler.py --days 7 --output report.md
2. 查看增量报告（对比历史）
python3 scripts/dataset_crawler.py --incremental --state-file ~/.dataset_crawler_state.json
3. 指定数据源
python3 scripts/dataset_crawler.py --sources jiqizhixin,infoq,oschina
输出格式
生成的报告为Markdown格式，包含以下字段：
## 数据集新闻报告 (2024-01-15)
### 本周新增数据集 (5条)
| 数据集名称 | 企业名称 | 数据集描述 | 高质量标记 | 相关链接 |
|-----------|---------|-----------|-----------|---------|
| 百度文心大模型数据集 | 北京百度网讯科技有限公司 | 用于训练文心大模型的中文数据集，包含... | ⭐ | [链接](https://...) |
| AliQA开源问答数据集 | 阿里巴巴集团控股有限公司 | 面向电商领域的问答数据集，包含100万... | - | [GitHub](https://...) |
数据源配置
爬虫默认支持以下数据源，可在references/data_sources.md中查看详情和添加新源：
稳定数据源 ✅
| 数据源 | 类型 | 可用性 | 说明 |
|---|---|---|---|
| GitHub | 开源平台 | ⭐⭐⭐⭐⭐ | API 稳定，成功率高 |
| Gitee | 国内开源 | ⭐⭐⭐⭐ | API 可用，部分已知数据集 |
| ModelScope | 阿里云平台 | ⭐⭐⭐⭐ | API 可用，高质量数据集 |
| 智源研究院 | AI研究院 | ⭐⭐⭐⭐ | 已知数据集列表 |
| 数据堂/标贝/拓尔思/海天瑞声 | 数据服务商 | ⭐⭐⭐⭐ | 已知数据集列表 |
不稳定数据源 ⚠️
| 数据源 | 类型 | 可用性 | 说明 |
|---|---|---|---|
| 机器之心 | AI媒体 | ⭐⭐ | 有反爬措施，可能失败 |
| InfoQ | 技术平台 | ⭐⭐ | 有反爬措施，可能失败 |
| 开源中国 | 开源社区 | ⭐⭐ | 有反爬措施，可能失败 |
| 站长之家 | 技术媒体 | ⭐⭐ | 有反爬措施，可能失败 |
⚠️ 重要提示: 机器之心、InfoQ、开源中国等站点均有反爬措施，User-Agent 轮换和随机延迟不足以保证稳定可用。建议：
- 多次运行脚本以获取更多数据
- 或使用 web-access skill 作为降级方案检索这些站点
企业名称映射
企业简称到全称的映射表存储在references/company_mappings.md中。如需扩展映射关系，请编辑该文件。
What ships with it
8 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.
- agents/openai.yaml 246 B
- examples/report_sample.md 5.3 KB
- references/company_mappings.md 1.9 KB
- references/data_sources.md 2.5 KB
- scripts/dataset_crawler_gov.py 19 KB runs code
- scripts/dataset_crawler_real.py 27 KB runs code
- scripts/dataset_crawler_v2.py 23 KB runs code
- scripts/dataset_crawler.py 27 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.
- 10d ago First seen · 174 lines · 107 tokens per session scan A 604f2c048d8c
datasets-search is a skill published in the GitHub repository AgenticAIPlan/AgenticAISkills (11 stars, last pushed 3mo ago), licensed MIT. It adds 107 tokens to every session and 1,722 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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