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 bi-semantic-layer-guidegit 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/bi-semantic-layer-guide)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-semantic-layer-guide"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-semantic-layer-guide/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/bi-semantic-layer-guide"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-semantic-layer-guide.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 | $0.00058 | $0.00715 |
| Opus 5 | $0.00029 | $0.00358 |
| Sonnet 5 | $0.00012 | $0.00143 |
| Haiku 4.5 | $0.00006 | $0.00072 |
Grade A, and why
bi-semantic-layer-guide 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
bi-semantic-layer-guide
概述
语义层是统一管理业务语义元数据的服务,包括指标定义、维度定义、指标维度绑定和数据集映射。
可用工具
语义层暴露三类工具:
- 全局:
list_domains() - 指标查询:
list_metrics(domain)、search_metrics(query, domain)、get_metric(name, domain)、get_north_star_metrics(domain) - 维度查询:
list_dimensions(domain)、get_dimension(name, domain)、list_dimensions_of_metric(name, domain)、get_dimension_hierarchy(name, domain)、get_dimension_values(name, domain) - 数据集查询:
list_datasets(domain)、get_dataset_columns(name, domain)、get_dataset(name, domain)
具体参数和返回格式参见工具自身描述。
查询策略
指标匹配
在语义层中查找指标,可选策略:
- 语义检索(
search_metrics),语义层会自动匹配同义词 - 查看具体业务域的北极星指标列表(
get_north_star_metrics),从中选择最相关的 - 若以上均未命中,列出该域全部指标(
list_metrics),逐一判断和分析条目的相关性。
指标属性确认
通过 get_metric 获取指标属性,重点属性:
- 是否是北极星指标(
is_north_star):用于角色分配 - 是否展示指标(
is_display):该指标是否应出现在展示面板中 - 是否展示分布(
is_display_distribution):展示指标时是否同时展示分布图 - 维度绑定(
dimensions):指标可拆解的维度列表,含is_display_dimension、is_contribution_dimension标记。
维度信息获取
通过 list_dimensions_of_metric 获取指标可拆解的维度列表(is_contribution_dimension=true 的维度才可用于贡献度拆解),通过 get_dimension_hierarchy 获取维度间的父子层级关系。
消歧规则
搜索指标时可能遇到歧义,按以下规则处理:
- 匹配到多个指标:优先选北极星指标(
is_north_star=true)。若仍有多个候选,结合指标类型和标签综合判断,必要时向用户确认。 - 精确名称 vs 同义词:若同时出现精确名称匹配和同义词匹配,精确名称优先。例如搜索"DAU",若同时命中指标名为"DAU"的指标和同义词含"DAU"的"访问用户数",取前者。
- 同名指标跨业务域:若同一指标名出现在多个业务域中,用当前分析任务的业务域限定范围。
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 · 58 lines · 58 tokens per session scan A d6a70b0a0a05
bi-semantic-layer-guide is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (71 stars, last pushed yesterday), licensed Apache-2.0. It adds 58 tokens to every session and 715 once invoked, about $0.0003 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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