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 agentmods add skills/konglong87/superpm/pm-feedbacknpx skills add konglong87/superPM --skill pm-feedbackgit 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-feedback)<a href="https://agentmods.dev/skills/konglong87/superpm/pm-feedback"><img src="https://agentmods.dev/badge/skills/konglong87/superpm/pm-feedback.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00050 | $0.03295 |
| Opus 5 | $0.00025 | $0.01648 |
| Sonnet 5 | $0.00010 | $0.00659 |
| Haiku 4.5 | $0.00005 | $0.00330 |
Grade A, and why
pm-feedback scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 479 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Preamble (run first)
bash "$(dirname "${BASH_SOURCE[0]}")/../../check-update.sh" 2>/dev/null || true
# 创建增长迭代目录
mkdir -p docs/03-增长迭代
echo "📊 PM-Feedback V2 - 用户反馈分析工具"
echo "支持并发分析:反馈分类 | 情感分析 | 优先级评估 | 问题归类"
echo ""
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: 收集反馈数据(主 agent - 用户交互)
使用 AskUserQuestion 询问:
📊 用户反馈分析 - 数据来源
请提供用户反馈数据:
A) 从文件读取(输入文件路径) B) 直接粘贴反馈内容 C) 从应用商店/社交媒体爬取(需 WebSearch) D) 使用示例数据演示 E) 其他(请手动输入)
用户选择后,获取反馈数据。
Subagent 并发分析架构
架构图
步骤 2: 并发分析(Subagent 并行执行)
使用 Agent tool 并发派发 4 个 subagent:
在一条消息中并发调用 4 个 Agent tool:
**Subagent 1: Feedback Categorization**
- type: "general-purpose"
- prompt: "分析用户反馈,进行分类(功能需求/Bug报告/体验问题/价格反馈/其他),输出到 docs/03-增长迭代/feedback-categories.md"
**Subagent 2: Sentiment Analysis**
- type: "general-purpose"
- prompt: "分析用户反馈情感倾向(正面/中性/负面),识别关键情绪点,输出到 docs/03-增长迭代/sentiment-analysis.md"
**Subagent 3: Priority Assessment**
- type: "general-purpose"
- prompt: "评估用户反馈优先级(P0-P3),基于影响面/紧急程度/实现成本,输出到 docs/03-增长迭代/priority-assessment.md"
**Subagent 4: Problem Clustering**
- type: "general-purpose"
- prompt: "对用户反馈问题进行聚类分析,识别核心问题群,输出到 docs/03-增长迭代/problem-clusters.md"
**并发执行,等待所有 subagent 完成**
步骤 3: 主 Agent 整合分析
读取所有 subagent 分析结果:
read docs/03-增长迭代/feedback-categories.md
read docs/03-增长迭代/sentiment-analysis.md
read docs/03-增长迭代/priority-assessment.md
read docs/03-增长迭代/problem-clusters.md
整合成综合报告:
使用 Write 生成:docs/03-增长迭代/用户反馈分析报告.md
综合报告结构
# 用户反馈分析报告
## 一、反馈概览
**数据来源**: [来源]
**反馈数量**: [总数]
**时间范围**: [时间段]
---
## 二、反馈分类统计
### 2.1 分类分布
[来自 feedback-categories.md]
| 类型 | 数量 | 占比 |
|------|------|------|
| 功能需求 | XX | XX% |
| Bug报告 | XX | XX% |
| 体验问题 | XX | XX% |
| 价格反馈 | XX | XX% |
| 其他 | XX | XX% |
### 2.2 高频反馈 TOP 10
1. [反馈内容] - XX 次
2. [反馈内容] - XX 次
...
---
## 三、情感分析
### 3.1 情感分布
[来自 sentiment-analysis.md]
| 情感 | 数量 | 占比 |
|------|------|------|
| 正面 | XX | XX% |
| 中性 | XX | XX% |
| 负面 | XX | XX% |
### 3.2 关键情绪点
**正面情绪**:
- [用户喜欢的地方]
**负面情绪**:
- [用户不满的地方]
---
## 四、优先级评估
### 4.1 优先级分布
[来自 priority-assessment.md]
| 优先级 | 数量 | 说明 |
|--------|------|------|
| P0 (紧急) | XX | 影响核心功能/大量用户 |
| P1 (高) | XX | 重要但不紧急 |
| P2 (中) | XX | 需要关注 |
| P3 (低) | XX | 可延后处理 |
### 4.2 P0 问题清单
1. [问题描述] - 影响:XX 用户
2. [问题描述] - 影响:XX 用户
...
---
## 五、问题聚类分析
### 5.1 核心问题群
[来自 problem-clusters.md]
**问题群 1: [问题类别]**
- 关联反馈:XX 条
- 典型描述:[用户原话]
- 根本原因:[分析]
**问题群 2: [问题类别]**
- 关联反馈:XX 条
- 典型描述:[用户原话]
- 根本原因:[分析]
---
## 六、改进建议
### 6.1 短期行动(1-2 周)
**紧急修复(P0)**:
1. [改进建议]
2. [改进建议]
**快速优化(P1)**:
1. [改进建议]
2. [改进建议]
### 6.2 中期规划(1-3 月)
**功能迭代**:
1. [功能需求] - P1 优先级
2. [功能需求] - P2 优先级
**体验优化**:
1. [优化点]
2. [优化点]
### 6.3 长期规划(3-6 月)
**战略改进**:
1. [战略建议]
2. [战略建议]
---
## 七、下一步建议
建议执行:
1. **pm-priority** - 对改进建议进行优先级排序
2. **pm-iteration** - 制定迭代计划
3. **pm-docs** - 更新产品文档
---
**分析时间**: 2026-XX-XX
**数据来源**: 用户反馈
**分析方法**: 多维度并发分析
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.
- 5d ago First seen · 479 lines · 50 tokens per session scan A f497a003d6d8
pm-feedback is a skill published in the GitHub repository konglong87/superPM (60 stars, last pushed 22d ago), licensed MIT. It adds 50 tokens to every session and 3,295 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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