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 konglong87/superPM --skill pm-funnelgit clone --depth 1 https://github.com/konglong87/superPMWrote 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/konglong87/superpm/pm-funnel)<a href="https://agentmods.dev/skills/konglong87/superpm/pm-funnel"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-funnel.svg" alt="Measured on agentmods" 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 | $0.00059 | $0.04224 |
| Opus 5 | $0.00030 | $0.02112 |
| Sonnet 5 | $0.00012 | $0.00845 |
| Haiku 4.5 | $0.00006 | $0.00422 |
Grade A, and why
pm-funnel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 600 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preamble (run first)
bash "$(dirname "${BASH_SOURCE[0]}")/../../check-update.sh" 2>/dev/null || true
# 创建目录
mkdir -p docs/05-产品战略
# 检查是否有用户旅程地图作为输入
if [ -f "docs/01-需求调研/用户旅程地图.md" ]; then
echo "✅ 检测到用户旅程地图,将基于此进行漏斗分析"
fi
跨 Agent 交互规则
当流程要求与用户交互时:
- 如果当前环境支持 AskUserQuestion,使用 AskUserQuestion(最佳体验)。
- 如果当前环境不支持 AskUserQuestion,必须用普通聊天消息提出同样问题。
- 一次只问一个问题。
- 提问后必须停止当前回合,等待用户回答(STOP and WAIT)。
- 不得在用户回答前生成文档、写入 docs。
- 已有 docs 文件不能替代本轮用户回答。
执行流程
步骤 1: 定义转化漏斗
询问方式:使用 AskUserQuestion 逐个询问
问题 1: 业务场景
使用 AskUserQuestion 询问:
请问您要分析哪个业务场景的转化漏斗?
A) 注册转化漏斗(访客→注册用户) B) 购买转化漏斗(浏览→下单→支付) C) 激活转化漏斗(下载→注册→首次使用) D) 留存转化漏斗(新增→次日留存→7日留存) E) 其他(请手动输入)
问题 2: 漏斗步骤定义
根据问题1的场景,引导用户定义具体步骤:
示例(购买转化漏斗):
请确认购买转化漏斗的关键步骤
引导用户输入:
- 步骤1:[浏览商品详情页]
- 步骤2:[加入购物车]
- 步骤3:[进入结算页]
- 步骤4:[提交订单]
- 步骤5:[完成支付]
问题 3: 当前数据
针对每个步骤,收集当前数据:
使用 AskUserQuestion 询问:
步骤[1]:[浏览商品详情页]的用户数是多少?
引导用户输入每个步骤的用户数:
| 步骤 | 用户数 | 占比 |
|---|---|---|
| 步骤1 | [X]万 | 100% |
| 步骤2 | [X]万 | [X]% |
| 步骤3 | [X]万 | [X]% |
| 步骤4 | [X]千 | [X]% |
| 步骤5 | [X]千 | [X]% |
步骤 2: 计算转化率和流失率
计算公式:
步骤转化率 = (下一步用户数 / 当前步骤用户数) × 100%
步骤流失率 = 100% - 步骤转化率
整体转化率 = (最后一步用户数 / 第一步用户数) × 100%
示例:
| 步骤 | 用户数 | 步骤转化率 | 步骤流失率 |
|---|---|---|---|
| 浏览详情页 | 10,000 | - | - |
| 加入购物车 | 3,000 | 30% | 70% |
| 进入结算页 | 2,100 | 70% | 30% |
| 提交订单 | 1,470 | 70% | 30% |
| 完成支付 | 882 | 60% | 40% |
整体转化率:882 / 10,000 = 8.82%
步骤 3: 流失原因诊断
针对流失率最高的步骤,分析原因:
步骤3.1: 识别关键流失节点
关键流失节点定义:流失率 > 30% 的步骤
示例:
- 步骤1→2:流失率70%(关键流失节点)
- 步骤4→5:流失率40%(关键流失节点)
步骤3.2: 诊断流失原因
针对关键流失节点,使用 AskUserQuestion 询问:
步骤1→2(浏览详情页→加入购物车)流失率高达70%,可能的原因是?
A) 产品价格过高 B) 商品信息不清晰 C) 用户购买意愿不强 D) 竞品对比流失 E) 页面加载慢 F) 其他(请手动输入)
针对每个流失原因,引导用户深入分析:
请提供数据支撑或用户反馈(如有)
步骤3.3: 用户反馈收集
询问:
是否有用户调研或客服反馈数据?
A) 有用户调研数据 B) 有客服反馈记录 C) 有用户评论/投诉 D) 暂无数据,需要收集
如果选择D,提供数据收集建议:
建议收集方法:
- 用户问卷调研(流失用户问卷)
- 客服反馈分析
- 用户访谈(深度访谈5-10人)
- 数据埋点(详细行为数据)
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.
- 8d ago First seen · 600 lines · 59 tokens per session scan A 2438ad79ba86
pm-funnel is a skill published in the GitHub repository konglong87/superPM (62 stars, last pushed 4d ago), licensed MIT. It adds 59 tokens to every session and 4,224 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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