What is analyze-feedback?

A workflow for turning reviews, surveys, support tickets, NPS responses, or other user comments into sentiment, themes, counts, and segment comparisons.

Use it with pasted text, CSV or Excel files, uploaded documents, or tool exports to find recurring issues and patterns by user group.

It helps make sense of large amounts of feedback that are difficult to read and compare one item at a time.

analyze-feedback — Command for Claude Code by unixcrh/phuryn-pm-skills

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.

Clone the repo

git clone --depth 1 https://github.com/unixcrh/phuryn-pm-skills

Made for: Claude Code.

Or install pm-market-research, the plugin that ships this one along with the rest of its 7 skills, 3 commands.

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.

README.md

[![agentmods](https://agentmods.dev/badge/commands/unixcrh/phuryn-pm-skills/analyze-feedback/github.svg)](https://agentmods.dev/commands/unixcrh/phuryn-pm-skills/analyze-feedback)

Your own site

<a href="https://agentmods.dev/commands/unixcrh/phuryn-pm-skills/analyze-feedback"><img src="https://agentmods.dev/badge/commands/unixcrh/phuryn-pm-skills/analyze-feedback/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.

Your own site · 80×15

<a href="https://agentmods.dev/commands/unixcrh/phuryn-pm-skills/analyze-feedback"><img src="https://agentmods.dev/badge/commands/unixcrh/phuryn-pm-skills/analyze-feedback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 16 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.

When invoked 845 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin 100% copy Near-identical to another mod 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.

Model	Per session	Once invoked
Fable 5.1	$0.00016	$0.00845
Opus 5.5	$0.00006	$0.00338
Sonnet 5.5	$0.00003	$0.00169
Haiku 4.5	$0.00002	$0.00085

Measured 2mo ago against content hash 23f85c49ee23, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

analyze-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 2mo 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.

Origin

This is a copy

100% identical to analyze-feedback — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

pm-market-research/commands/analyze-feedback.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

/analyze-feedback -- User Feedback Analysis

Process large volumes of user feedback (reviews, surveys, support tickets, NPS responses) into structured insights with sentiment analysis and segment-level patterns.

Invocation

/analyze-feedback [upload a CSV of NPS responses]
/analyze-feedback [paste app store reviews or survey responses]
/analyze-feedback [upload support ticket export]

Workflow

Step 1: Accept Feedback Data

Accept in any format:

CSV/Excel with feedback text (and optional metadata: date, segment, rating)
Pasted text (reviews, survey responses, Slack messages)
Uploaded documents or exports from feedback tools

Ask:

What kind of feedback is this? (NPS, reviews, support tickets, survey, etc.)
Any segments to analyze separately? (user tier, plan, geography)
What are you looking for? (general themes, specific issues, trends over time)

Step 2: Analyze

Apply the sentiment-analysis skill:

Sentiment scoring: Classify each piece of feedback (positive, neutral, negative)
Theme extraction: Identify recurring topics and cluster related feedback
Frequency analysis: Count how often each theme appears
Segment analysis: Break down sentiment and themes by user segment (if data available)
Trend detection: If dates are available, identify sentiment shifts over time

Step 3: Generate Analysis Report

## Feedback Analysis Report

**Date**: [today]
**Feedback analyzed**: [count] responses
**Source**: [NPS survey / app reviews / support tickets / etc.]
**Period**: [date range if available]

### Overall Sentiment
- Positive: [X%] | Neutral: [Y%] | Negative: [Z%]
- Average sentiment score: [X/10]
- Trend: [improving / stable / declining]

### Top Themes
| # | Theme | Mentions | Sentiment | Segments Most Affected |
|---|-------|----------|-----------|----------------------|

### Theme Deep-Dive

#### Theme 1: [Name] — [X] mentions, [sentiment]
- **What users are saying**: [summary with representative quotes]
- **Root cause**: [what's driving this feedback]
- **Impact**: [how this affects retention, satisfaction, or revenue]
- **Recommendation**: [what to do about it]

[Repeat for top 5-8 themes]

### Segment Analysis
| Segment | Volume | Avg Sentiment | Top Theme | Key Difference |
|---------|--------|-------------|-----------|---------------|

### Notable Quotes
> "[quote]" — [segment, sentiment]

### Trends Over Time
[If date data available: chart-ready data showing sentiment shifts]

### Actionable Insights
1. [Insight + recommended action]
2. ...

### Gaps
[What this feedback doesn't tell you — suggested follow-up research]

Read the full file on GitHub · 104 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.

2mo ago First seen · 104 lines · 16 tokens per session scan A 23f85c49ee23

Subscribe to this mod's changes

analyze-feedback is a command published in the GitHub repository unixcrh/phuryn-pm-skills (2 stars, last pushed 8mo ago), licensed MIT. It adds 16 tokens to every session and 845 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to analyze-feedback, differing in 0 lines, and is treated as a copy.

Analytics and metrics

analyze-feedback next to 6 commands from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
analyze-feedbackunixcrh/phuryn-pm-skills	Command	2	8mo ago	MIT	A	16	Claude Code
checklistgithub/spec-kit	Command	148k	today	MIT	A	13	Claude Code
clarifygithub/spec-kit	Command	148k	today	MIT	A	27	Claude Code
specifygithub/spec-kit	Command	148k	today	MIT	A	13	Claude Code
analyzegithub/spec-kit	Command	148k	today	MIT	A	26	Claude Code
convergegithub/spec-kit	Command	148k	today	MIT	A	35	Claude Code
implementgithub/spec-kit	Command	148k	today	MIT	A	14	Claude Code