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-attribution-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-attribution-analysis)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-attribution-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-attribution-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-attribution-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-attribution-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 51 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
- medium Prompt Injection · line 58 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00054 | $0.01931 |
| Opus 5 | $0.00027 | $0.00966 |
| Sonnet 5 | $0.00011 | $0.00386 |
| Haiku 4.5 | $0.00005 | $0.00193 |
Grade A, and why
bi-attribution-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.
How it starts
The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.
bi-attribution-analysis
计算各维度（组）值对指标变动的贡献度，量化每个维度值对整体变动的贡献大小，常见场景：
- 维度下拆归因：指标异常后，逐维度下拆定位驱动变化的关键因素
- 交叉维度归因：多维度组合下拆，定位高贡献的维度组合
适用指标：
- 维度间可加的量值指标：各维度值可直接求和得到整体值，如访问用户数、GMV、订单量
- 率值指标：整体值 = 分子总和 / 分母总和，如转化率、留存率
- 加权平均型指标：整体值由各维度值按权重加权得到。如按渠道拆解客单价（= 各渠道总收入 / 各渠道订单数，各渠道客单价不可直接求和）、按客户群拆解人均收入（= 各群总收入 / 各群用户数）
执行步骤
1：数据准备
包含维度往期/当期指标数据的 CSV 文件，至少包含以下列：
维度间可加的量值指标：
| 列 | 说明 | 示例 |
|---|---|---|
| 维度列 | 维度名称 | 渠道 |
| 往期值列 | 往期指标值 | 销售额_往期 |
| 当期值列 | 当期指标值 | 销售额_当期 |
率值指标额外需包含分子和分母的往期/当期值：
| 列 | 说明 | 示例 |
|---|---|---|
| 分子_往期 | 往期分子值 | 转化人数_往期 |
| 分子_当期 | 当期分子值 | 转化人数_当期 |
| 分母_往期 | 往期分母值 | 访问人数_往期 |
| 分母_当期 | 当期分母值 | 访问人数_当期 |
加权平均型指标额外需包含指标的分子（被加权的总量）和分母（权重来源）的往期/当期值，示例同上。
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：选择贡献度计算方法
根据指标类型选择计算方法：
维度间可加的量值指标：
| 方法 | 适用场景 |
|---|---|
| quantity-standard（标准法） | 默认选择。按维度增量占总增量的比例计算贡献度 |
| quantity-separate（正负分离法） | 当正负变化相互抵消严重时使用（例如，某方向变动量超过整体变动的 30%），将正贡献和负贡献分别归一化，避免抵消掩盖真实驱动因素 |
加权平均型指标和率值指标：
| 方法 | 适用场景 |
|---|---|
| ratio-cross-term（保留交叉项法） | 默认选择。将变动拆解为三部分：结构效应（权重变化的影响）、水平效应（指标值本身变化的影响）和交互效应（两者同时变化产生的交叉影响） |
| ratio-average（平均权重法） | 当不需要区分交互效应、希望结果更简洁时使用。用往期和当期的平均权重消除交互项，只输出结构效应和水平效应两项 |
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.
- 12d ago First seen · 160 lines · 54 tokens per session scan A 9987c9ee0e86
bi-attribution-analysis is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (73 stars, last pushed yesterday), licensed Apache-2.0. It adds 54 tokens to every session and 1,931 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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