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-new-dimension-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-new-dimension-analysis)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-new-dimension-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-new-dimension-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-new-dimension-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-new-dimension-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 | $0.00048 | $0.01038 |
| Opus 5 | $0.00024 | $0.00519 |
| Sonnet 5 | $0.00010 | $0.00208 |
| Haiku 4.5 | $0.00005 | $0.00104 |
Grade A, and why
bi-new-dimension-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 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.
What it actually says
bi-new-dimension-analysis
识别归因维度中当期首次出现的新增维度值（如新渠道、新品类、新功能），评估其在北极星指标上的表现，判断其对业务的贡献和效果。通常适用于周度或月度对比。
执行步骤
1：数据准备
当期和往期的维度指标数据（CSV），至少包含以下列：
| 列 | 说明 | 示例 |
|---|---|---|
| 时间列 | 周期标识 | 日期 |
| 维度列 | 维度名称 | 渠道 |
| 指标列 | 北极星指标值 | 访问用户数 |
| 父维度列 | 父维度名称（可选，层级维度时需要） | 对话模式 |
示例（维度列=渠道，北极星指标列=访问用户数，1 月为往期，2 月为当期，"抖音渠道"为新增维度值）：
日期,渠道,访问用户数
2025-01-01,自然流量,10000
2025-01-01,付费投放,8000
2025-02-01,自然流量,11000
2025-02-01,付费投放,7500
2025-02-01,抖音渠道,3000
若上游步骤已产出可用数据文件则直接使用，否则自行取数。
2：识别新增维度值
识别新增且趋于平稳的维度值：
- 对比当期和往期的维度值集合，找出当期存在但往期不存在的维度值
- 对每个新增维度值，计算其首次出现后在当期内每天的日环比变化幅度，判断是否平稳：
- 每日变化幅度均 < 10%：趋于平稳，纳入后续分析
- 存在日变化幅度 ≥ 10%：仍在波动，暂不分析
3：对比指标表现
根据新增维度的层级，选择不同的对比基准和对比指标：
0 级维度（无父维度）：对比基准为整体均值和整体 Top1 维度值
| 对比指标 | 计算方式 |
|---|---|
| 绝对值 | 新增维度的北极星指标值 |
| 整体占比 | 新增维度值 / 整体指标值 |
| 均值偏离度 | (新增维度值 - 整体均值) / 整体均值 |
| Top-1 偏离度 | (新增维度值 - 整体 Top-1 值) / 整体 Top-1 值 |
子维度（有父维度）：对比基准为父维度内均值和父维度内 Top-1 维度值
| 对比指标 | 计算方式 |
|---|---|
| 绝对值 | 新增维度的北极星指标值 |
| 均值偏离度 | (新增维度值 - 父维度内均值) / 父维度内均值 |
| Top-1 偏离度 | (新增维度值 - 父维度内 Top-1 值) / 父维度内 Top-1 值 |
| 组内排名 | 在相同父维度下的排名位置 |
4：汇总结果
将各归因维度的新增维度分析结果汇总输出。示例：
0 级维度：
维度值 北极星指标值 整体占比 均值偏离度 Top1偏离度
新渠道A 15000 8.0% +25.0% -10.0%
子维度：
维度值 父维度 北极星指标值 均值偏离度 Top1偏离度 组内排名
新品类B 父类X 8000 -15.0% -30.0% 5/8
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 · 83 lines · 48 tokens per session scan A 48568e5ace1d
bi-new-dimension-analysis is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (73 stars, last pushed yesterday), licensed Apache-2.0. It adds 48 tokens to every session and 1,038 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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