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 agentscope-ai/QwenPaw-Data --skill data-intent-routergit clone --depth 1 https://github.com/agentscope-ai/QwenPaw-DataWrote 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/agentscope-ai/qwenpaw-data/data-intent-router)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/data-intent-router"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/data-intent-router/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/agentscope-ai/qwenpaw-data/data-intent-router"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/data-intent-router.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 | $0.00028 | $0.00715 |
| Opus 5 | $0.00014 | $0.00358 |
| Sonnet 5 | $0.00006 | $0.00143 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
data-intent-router 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.
What it actually says
data-intent-router
判定用户数据相关请求的任务类型，路由至对应的处理链路。
判定规则
依次检查，命中即停：
1. 查询类
获取数据或了解数据定义，不涉及分析推理（诊断、归因、对比、解释、建模等）
1a. 元数据查询：询问数据的定义或结构——字段含义、计算口径、枚举值、表结构、关联关系、可用数据源
- 例如："这个字段什么意思""表里有哪些字段""这两张表怎么关联"
1b. 数据查询：直接获取数据结果（某个值、某批记录、某张表的内容）
- 例如："昨天 DAU 多少""查一下上月销售额""这个客户的订单有哪些"
2. 分析类
涉及分析、建模、计算、报告等数据处理任务
2a. BI 业务分析：围绕业务指标进行基本数据观察，趋势洞察、诊断、归因等、或直接要求产出分析结果，例如日/周/月报
- 例如："转化率为什么下降"、"分析 3 月用户趋势"、"某产品 12 月月报"
2b. 数据探索：没有预设的业务问题，目的是探索性地了解数据——描述特征（分布、缺失、异常值、相关性）、诊断数据问题（异常成因、质量瓶颈）、评估数据可用性
- 例如："看一下数据分布"、"检查数据质量"、"这份数据能不能用"、"为什么数目偏斜这么严重"
2c. 统计建模：涉及回归、分类、聚类、预测、特征工程等建模概念，或要求训练模型、评估效果、交叉验证，或需要验证某个假设（假设检验、显著性分析）
- 例如："预测下个月的收入"、"对用户做聚类"
2d. 定量计算：按已知公式或算法执行确定性计算（财务指标、工程公式、统计量、数值模拟等），输入→公式→输出，无需拟合数据
- 例如："算一下这个方案的 ROI"、"模拟不同参数下的结果"、"计算标准差"
2e. 报告生成：上下文中已有分析结论，用户直接要求将其格式化为报告
- 例如："把刚才的分析整理成报告"、"将结果导出为报告"
3. 非数据任务
以上均不命中，核心意图不涉及数据操作或数据分析，如代码开发、文档写作、知识问答、闲聊等
- 例如："帮我写个脚本""写一份 AI 领域的调研报告""今天天气怎样"
边界情况
- 复合意图：同时包含查数和分析（"查一下 DAU，并分析下降原因"），以分析类为准
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.
- 10d ago First seen · 50 lines · 28 tokens per session scan A 471bb9ffa116
data-intent-router is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (71 stars, last pushed yesterday), licensed Apache-2.0. It adds 28 tokens to every session and 715 once invoked, about $0.0001 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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