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-conversion-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-conversion-analysis)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-conversion-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-conversion-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-conversion-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-conversion-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 | $0.00141 | $0.02646 |
| Opus 5 | $0.00071 | $0.01323 |
| Sonnet 5 | $0.00028 | $0.00529 |
| Haiku 4.5 | $0.00014 | $0.00265 |
Grade A, and why
bi-conversion-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 9d 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 — 194 lines — stays where its author put it; the contents beside it link to each section on GitHub.
bi-conversion-analysis
对转化执行完整的分析流程，核心目标是算清楚用户转不转、转化率多少、有没有变化：定义口径 → 取数 → 计算转化率 →（可选）异常分析 →（可选）拆分维度 / 对比归因 → 解读与输出。
前置条件
开始执行前，确认以下信息已就绪：
- 转化漏斗明确，即起始行为（接触目标行为，如访问、曝光、进入页面）与结束行为（完成目标行为，如对话、注册、付费）已定义
- 转化率指标已确定（如对话转化率、付费转化率等），对应分子/分母口径清晰
- 数据获取能力可用，能取到计算转化率所需的 CSV 或等价数据
- 分析范围明确，包括时间窗口、对象范围，以及是否需要拆分维度或做对比
若转化漏斗或指标口径不明确，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
主要步骤
| 步骤 | 用途 | 是否必选 |
|---|---|---|
| 定义口径 | 确定转化漏斗与转化率指标 | 必选，见步骤 1 |
| 取数 | 按口径取分子/分母数据 | 必选，见步骤 2 |
| 计算转化率 | 计算起始行为到结束行为的转化率 | 必选，见步骤 3 |
| 异常分析 | 识别转化率时间序列中的异常波动点 | 可选，见步骤 4 |
| 拆分维度 / 对比 | 按维度分别计算并跨时间、群体对比差异 | 可选，见步骤 5 |
| 解读 & 输出 | 转化漏斗、关键节点、异常点、差异结论 | 必选，见步骤 6 |
典型产出：转化率表、转化漏斗图、转化率时间序列（含异常点标注）、分维度转化对比。
最短路径：定义口径 → 取数 → 算转化率 → 报数字 / 看趋势（即步骤 1 → 2 → 3 → 6，跳过步骤 4、5）。
本 workflow 主要提供转化率分析场景下的编排与数据衔接逻辑，各步骤的具体执行按相应分析方法的标准流程进行。
1. 定义口径
明确转化漏斗与转化率指标，这是后续取数与计算的基础：
| 要素 | 含义 | 常见取值 |
|---|---|---|
| 起始行为（分母） | 进入漏斗、接触目标行为的口径 | 访问、曝光、进入页面、加购等 |
| 结束行为（分子） | 完成目标行为的口径 | 对话、注册、下单、付费等 |
| 转化率指标 | 由分子/分母构成的比率 | 对话转化率、付费转化率、下单转化率等 |
要点：
- 同一分析可包含多级漏斗（如访问 → 对话 → 付费），需为每一环节分别定义分子/分母
- 同一分析可包含多种转化率指标，需分别定义口径
- 口径一旦确定，后续取数、计算、对比须保持一致，避免分子分母错配
2. 取数
按步骤 1 确定的口径取数，准备转化率计算所需的数据。
数据准备
整理 CSV，包含转化率计算所需的列：
- 起始行为用户数（分母，如访问用户数）
- 结束行为用户数（分子，如对话用户数）
- 如需多级漏斗或多种转化率指标，分别取对应的分子/分母列
示例：
date,访问用户数,对话用户数,付费用户数
2025-01-01,10000,3000,300
2025-01-02,10500,3200,280
若计划在步骤 5 拆分维度，取数时一并带出维度列（如渠道、端、国家、版本），或按维度分别取数。
3. 计算转化率
计算从起始行为到结束行为的转化率。
计算执行
- 对每个需要报告的转化率指标按转化率公式（结束行为用户数 / 起始行为用户数）或可用脚本进行计算
- 多级漏斗时，计算各环节转化率（及整体转化率），供绘制转化漏斗图
- 不遗漏任何必要的转化率指标
- 保存计算结果
若已规划拆分维度，可在此步先算整体转化率，维度拆分在步骤 5 展开。
4. （可选）转化率异常分析
查看计算得到的转化率是否是时间相关的序列，若是，则对步骤 3 产出的转化率时间序列进行异常波动点识别，若否，则跳过本步骤。
数据准备
基于步骤 3 的转化率结果，整理含时间序列的 CSV，至少包含：
- 日期列：时间标识
- 指标列：转化率（如对话转化率）
示例：
date,对话转化率
2025-01-01,0.3000
2025-01-02,0.0952
2025-01-03,0.0095
若已拆分维度，对需要报告的各维度/cohort 及总体分别准备时间序列并执行异常检测。
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.
- 9d ago First seen · 194 lines · 141 tokens per session scan A e8840cc191cd
bi-conversion-analysis is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (67 stars, last pushed yesterday), licensed Apache-2.0. It adds 141 tokens to every session and 2,646 once invoked, about $0.0007 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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