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-metric-analysisgit 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-metric-analysis)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-metric-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-metric-analysis/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-metric-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-metric-analysis.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 | $0.00045 | $0.01127 |
| Opus 5 | $0.00023 | $0.00563 |
| Sonnet 5 | $0.00009 | $0.00225 |
| Haiku 4.5 | $0.00005 | $0.00113 |
Grade A, and why
bi-metric-analysis 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 11d 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-metric-analysis
针对原子的 BI 分析条目，完成基本的指标观测和重点指标的异常归因。
前置条件
开始执行前，确认以下信息已就绪：
- 分析条目明确，例如"用户规模分析"、"增长归因"等，清楚本次要分析什么
- 指标与角色已确定，知道要分析哪些指标，每个指标是北极星、展示还是分布角色
- 数据获取能力可用，有可以获取数据的工具或 API
若以上信息不完整（比如不清楚要分析哪些指标，或指标角色未确定），需先回到规划阶段补充，或向用户确认后再开始执行。
分析原则
域覆盖优先级
执行分析时，部分参数和设定可能被多个来源提供。当同一参数存在冲突时，按以下优先级取值：
| 优先级 | 来源 | 示例 |
|---|---|---|
| 最高 | 用户显式指定 | 用户要求"归因取 Top 5" |
| 中 | 域知识包 | 某业务域指定"正/负贡献度各取 Top 3" |
| 中 | 语义层接口 | 通过接口查询到的指标维度关联、阈值等 |
| 最低 | skill 默认值 | Top 3 贡献 |
技能调用
具体的分析方法（异常检测、维度归因、时间归因、新增维度分析等），若存在对应的原子技能则按需调用，否则自行推理完成。
1. 确定关联维度
在开始分析之前，需要：
- 为北极星指标确定归因拆解维度（从哪些角度下拆归因），为展示指标确定展示拆解维度（按什么维度展示分布和占比）；
- 明确各维度之间的层级关系（哪些是平级维度、哪些存在父子层级），层级关系决定了后续下拆的路径。
判断方式参见 references/dimension-association.md。
2. 基础观测
对指标执行基础数据观测，包括时间周期确定、指标聚合、衍生指标计算和维度交叉展示表构建。
3. 北极星指标异常检测与归因
基于第 2 步的观测结果，对每个北极星指标依次执行步骤 3.1-3.4。
3.1 异常检测
对北极星指标的时间序列进行异常波动点识别：
- 若未发现异常，给出数据现状的描述性总结，跳过步骤 3.2-3.3，直接进入第 3.4 步。
- 若发现异常，继续执行下一步骤。
3.2 维度下拆
发现异常后，对北极星指标按归因拆解维度逐层下拆（单维度扫描 → 交叉分析 → 层级下钻），定位驱动变动的关键维度（组）值，同步计算分布角色指标的分布占比。
3.3 时间归因
识别指标异常波动是否由具体事件导致并计算影响度。数据来源可能包括（但不限于）：
- 业务事件接口（若有） ：通过工具查询当期和往期发生的业务事件（如产品发布、营销活动等）
- 维度变化识别 ：对比当期与往期的维度构成，识别当期新增但上期没有的维度值（如新增产品功能）
3.4 新增维度分析
依次检查各归因拆解维度是否出现了新增值（上期不存在、本期首次出现）。新增维度值（如新上线的功能、新开拓的渠道）可能是指标变动的重要驱动因素，需评估其对北极星指标的贡献大小和表现水平。
4. 汇总结论
完成以上分析后，按步骤汇总该条目的结论，每个步骤记录以下内容：
| 字段 | 说明 |
|---|---|
| 执行内容 | 本步骤做了什么 |
| 取数文件 | 使用的原始数据文件路径 |
| 计算结果 | 产出的结果文件路径（归因表、异常检测结果等） |
| 结论 | 本步骤的分析发现 |
依次汇总：基础观测 → 异常检测 → 维度下拆 → 时间归因 → 新增维度分析。
最后附上整体不确定性（数据缺失、口径差异等）和后续建议。
What ships with it
1 file 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.
- 11d ago First seen · 95 lines · 45 tokens per session scan A 5e146c7add7d
bi-metric-analysis is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (72 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 1,127 once invoked, about $0.0002 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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