pm-feedback

pm-feedback is a skill for Claude Code, Codex from konglong87/superPM. It costs 50 tokens per session (3,295 once invoked), scanned A, original, MIT.

A workflow for analysing user feedback and reviews, including categories, sentiment, priority, and recurring problems. It is documented in Chinese and uses several separate analyses that are combined into product-improvement notes.

In plain words
What is it for?
Use it to analyse feedback from a file, pasted text, an app store or social media source, or sample data. It produces notes covering feedback categories, positive or negative sentiment, priority levels, and grouped issues.
Why use it?
It turns a collection of comments into organised findings, so product teams can see what users like, dislike, request, or report as broken. It also requires choosing where the feedback comes from before analysis begins.

Skill for Claude CodeCodex

Part of the superPM plugin — 55 skills, 1 hook shipped together

Install

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.

agentmods
npx agentmods add skills/konglong87/superpm/pm-feedback
Any agent
npx skills add konglong87/superPM --skill pm-feedback
Clone the repo
git clone --depth 1 https://github.com/konglong87/superPM

Made for: Claude Code, Codex.

Or install superPM, the plugin that ships this one along with the rest of its 55 skills, 1 hook.

Wrote 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.

agentmods badge for pm-feedback

README.md
[![agentmods](https://agentmods.dev/badge/skills/konglong87/superpm/pm-feedback.svg)](https://agentmods.dev/skills/konglong87/superpm/pm-feedback)
Your own site
<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>
Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,295 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 5d ago against content hash f497a003d6d8, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

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.

skills/03-growth-iteration/pm-feedback/SKILL.md · 479 lines

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:

  1. 如果当前环境支持 AskUserQuestion,使用 AskUserQuestion(最佳体验)。
  2. 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。
  3. 一次只问一个问题。
  4. 提问后必须停止当前回合,等待用户回答(STOP and WAIT)。
  5. 不得在用户回答前生成文档、写入 docs。
  6. 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

Read the full file on GitHub · 479 lines

Changes

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.

  1. 5d ago First seen · 479 lines · 50 tokens per session scan A f497a003d6d8

Subscribe to this mod's changes

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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