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-funnel-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-funnel-analysis)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw-data/bi-funnel-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-funnel-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-funnel-analysis"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw-data/bi-funnel-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 | $0.00089 | $0.01028 |
| Opus 5 | $0.00044 | $0.00514 |
| Sonnet 5 | $0.00018 | $0.00206 |
| Haiku 4.5 | $0.00009 | $0.00103 |
Grade A, and why
bi-funnel-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-funnel-analysis
漏斗分析用于科学衡量用户在关键路径各阶段的转化率与流失情况。它将完整业务流程拆分为连续步骤（如：浏览-点击-下单-支付），直观展示用户从流入到流出的转化情况。
执行流程
Step 1: 关键阶段明确与数据准备
根据转化漏斗分析需求以及提供数据，确定漏斗分析中所需分析的关键阶段，并在数据中定位对应的关键数据列。
整理数据：包含漏斗分析每个阶段可用于转化率计算的关键数据列的 CSV 文件，如，
step_1_user_count,step_2_user_count,step_3_user_count
10000,5000,1000
6000,4000,500
3000,1000,200
1500,100,20
10,2,0
数据不应遗漏任何关键阶段的关键数据。
Step 2: 各个阶段转化率计算
使用 bi-conversion-rate skill 计算相邻阶段的转化率。不遗漏任何阶段的转化率计算。
Step 3: 倒置漏斗状图表绘制（可选）
根据步骤 2 计算得到的各个阶段转化率，绘制用户从流入到流出的倒置漏斗状图表。
Step 4：识别转化卡点
卡点判定规则：
- 绝对阈值法：步骤转化率 < 行业基准的 80%
- 相对落差法：该步骤转化率显著低于其他步骤（低于均值 1 个标准差）
- 流失贡献法：该步骤流失占比 > 30%
当行业基准可获得时优先使用绝对阈值法进行判定，其次，使用相对落差法，最后考虑流失贡献率。
卡点分级：
| 级别 | 判定条件 | 优化优先级 |
|---|---|---|
| 🔴 严重卡点 | 转化率 < 20% 或流失占比 > 40% | 最高 |
| 🟡 中等卡点 | 转化率 20%-50% 或流失占比 20%-40% | 中 |
| 🟢 轻度卡点 | 转化率 50%-70% 或流失占比 10%-20% | 低 |
Step 5：输出漏斗分析结果并给出优化建议
以漏斗分析表的形式统计漏斗分析结果，并针对识别出的卡点，生成优化建议。
输出格式
漏斗分析结论以漏斗分析表+关键发现的形式反馈。
分析表包含关键步骤、原始数据、转化率以及识别卡点等信息，关键发现进行总结分析，如：
| 步骤 | 用户数 | 步骤转化率 | 整体转化率 | 流失率 | 流失占比 | 行业基准 | 相对落差 |
|---|---|---|---|---|---|---|---|
| 首页访问 | 10000 | - | 100% | - | - | - | - |
| 商品详情页 | 6000 | 60% | 60% | 40% | 47% | 65% | -5% |
| 加入购物车 | 3000 | 50% | 30% | 50% | 35% | 65% | -15% 🟡 |
| 提交订单 | 1500 | 50% | 15% | 50% | 18% | 55% | -5% |
| 完成支付 | 1200 | 80% | 12% | 20% | 6% | 85% | -5% |
关键发现：
- 整体转化率 12%，低于行业平均 15%
- 主要卡点在「加入购物车」步骤（转化率 50%，低于行业 15 个百分点）
- 该步骤流失占比 35%，是优化重点
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 · 78 lines · 89 tokens per session scan A f1b9c7f27407
bi-funnel-analysis is a skill published in the GitHub repository agentscope-ai/QwenPaw-Data (72 stars, last pushed today), licensed Apache-2.0. It adds 89 tokens to every session and 1,028 once invoked, about $0.0004 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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