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-distribution-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-distribution-analysis)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-distribution-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-distribution-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-distribution-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-distribution-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 | $0.00052 | $0.01331 |
| Opus 5 | $0.00026 | $0.00665 |
| Sonnet 5 | $0.00010 | $0.00266 |
| Haiku 4.5 | $0.00005 | $0.00133 |
Grade A, and why
bi-distribution-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.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
bi-distribution-analysis
观察数据在不同区间的分布特征，计算均值、标准差、中位数等统计信息，用于刻画集中趋势与离散程度。
执行步骤
Step 0：检查数据以及确定区间维度与计算指标
- 明确分析对象：明确区间维度，以及相应具体数据列。例如，分析“不同国家用户数的分布情况”，区间维度为“国家”，具体数据列为“用户数”；
- 数据以 CSV 格式保存，且数据中已包含区间维度信息以及关键数据列。如
date,访问用户数,国家
20250101,10000,英国
20250102,10500,法国
20250103,9800,德国
Step 1：计算数据分布特征
对用于分析的数值序列 (x_1,\ldots,x_n)，计算以下数据分布特征：
| 指标 | 说明 |
|---|---|
| 均值 | (\bar{x} = \frac{1}{n}\sum_{i=1}^n x_i) |
| 标准差 | (\sigma = \sqrt{\frac{1}{n}\sum_{i=1}^n (x_i-\bar{x})^2}) |
| 中位数 | 数值序列中位数 |
| top 5 的维度 | 数据值最大的 5 个维度 ｜ |
| top 5 维度各数值占比 | 数值最大的 5 个维度，每个维度对应数值占所有维度数值和的比例 |
| 频率分布（按累计占比分桶） | 将各维度按数值从大到小排序，逐项累计求和并除以总和得到累计占比 (r)，按 (r) 落入以下 7 个桶：<0.5、[0.5, 0.6)、[0.6, 0.7)、[0.7, 0.8)、[0.8, 0.9)、[0.9, 0.95)、>=0.95。每个桶的值为该桶包含的维度名称列表 |
使用 <skill-dir>/scripts/distribution_stats.py 脚本，计算上述 6 个数值分布特征（数值类指标计算结果保留小数点后 5 位）。
python <skill-dir>/scripts/distribution_stats.py --input_file "<输入数据文件路径 (CSV)>" --value_col "<数值列名>" --dimension_col "<区间维度取值列>"
参数说明：
| 参数 | 说明 | 默认值 |
|---|---|---|
| --input_file | 输入数据文件路径 (.csv) | (必填) |
| --value_col | 区间各维度对应数值列名 | (必填) |
| --dimension_col | 区间维度值列 | (必填) |
fallback（指引模式）
无脚本环境时按以下计算方式手动计算，不可遗漏任何指标计算。
对用于分析的数值序列 (x_1,\ldots,x_n)，计算以下数据分布特征：
| 指标 | 计算方式 |
|---|---|
| 均值 | (\bar{x} = \frac{1}{n}\sum_{i=1}^n x_i) |
| 标准差 | (\sigma = \sqrt{\frac{1}{n}\sum_{i=1}^n (x_i-\bar{x})^2}) |
| 中位数 | 1. 将数据进行从小到大排序；2. n 是奇数，中位数为第 (\frac{n+1}{2}) 个数；n 是偶数，中位数为第 (\frac{n}{2}) 位和第 (\frac{n+1}{2}) 为数的平均数 |
| top 5 的维度 | 数据最大的 5 个维度 ｜ |
| top 5 维度各数值占比 | 数值最大的 5 个维度，每个维度对应数值占所有维度数值和的比例 |
| 频率分布（按累计占比分桶） | 1. 将所有维度按数值 (x_i) 从大到小排序；2. 计算总和 (S=\sum_i x_i)；3. 依次计算累计和 (C_k=\sum_{i=1}^{k} x_i) 与累计占比 (r_k = C_k / S)；4. 按 (r_k) 将第 (k) 个维度名归入对应的桶：<0.5（(r_k<0.5)）、[0.5, 0.6)、[0.6, 0.7)、[0.7, 0.8)、[0.8, 0.9)、[0.9, 0.95)、>=0.95（(r_k \ge 0.95)）；5. 输出为 dict，key 为桶名，value 为该桶包含的维度名称列表 |
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 · 69 lines · 52 tokens per session scan A b68bd5566a1d
bi-distribution-analysis is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (72 stars, last pushed today), licensed Apache-2.0. It adds 52 tokens to every session and 1,331 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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