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Feedback Analyzer

Community
Mehdibargach
feedback-analyzer

Classify and analyze raw user feedback into themes, sentiment, and actionable insights. Takes pasted feedback text or a path to a CSV file and returns structured patterns and recommendations.

Overview

PublisherMehdibargach
Repositoryclaude-code-pm-skills
Skill namefeedback-analyzer
Stars
97
Forks
38
Bundled files
Instructions only
Links
Skill sourceRepository
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Mehdibargach on GitHub. Read the source before you install it.

Installation

Install the Feedback Analyzer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Mehdibargach/claude-code-pm-skills/tree/main/skills/feedback-analyzer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Mehdibargach/claude-code-pm-skills.git /tmp/claude-code-pm-skills
mkdir -p .claude/skills
cp -r /tmp/claude-code-pm-skills/skills/feedback-analyzer .claude/skills/feedback-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Feedback Analyzer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Feedback Analyzer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Feedback Analyzer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Feedback Analyzer

Take raw, messy user feedback and turn it into structured, actionable insights. Works with pasted text, CSV files, or any text-based feedback dump.

Process

  1. Ingest the feedback — If a file path is provided, read it. If CSV, parse it using Bash (csvtool, awk, or python). If pasted text, split by logical entries (one per line, or by paragraph).
  2. Classify each piece — For every feedback entry, assign:
    • Theme (e.g., "Onboarding", "Pricing", "Performance", "Missing Feature")
    • Sentiment (Positive / Neutral / Negative)
    • Urgency (High / Medium / Low — based on language intensity and frequency)
    • Type (Bug report / Feature request / Complaint / Praise / Question)
  3. Aggregate patterns — Count themes, calculate sentiment distribution, identify the top recurring issues.
  4. Surface insights — Find the non-obvious patterns: themes that correlate, sentiment shifts, signals that suggest churn risk or expansion opportunity.
  5. Write recommendations — Translate patterns into concrete next steps for a PM.

Output Format

Summary Stats

  • Total feedback entries: X
  • Sentiment breakdown: X% positive, X% neutral, X% negative
  • Top theme: [theme] (X mentions)
  • Date range (if available): [range]

Themes Table

ThemeCountSentiment (avg)UrgencyExample Quote
............"..."

Top 3 Patterns

Numbered list. Each pattern includes: what it is, why it matters, and how confident we are (based on volume).

Recommended Actions

PriorityActionBased OnExpected Impact
P0.........
P1.........
P2.........

Raw Classified Data

If fewer than 50 entries, include a full table with each entry classified. If more than 50, save to a file and note the path.

Rules

  • Do not editorialize feedback. Classify what users said, not what you think they meant.
  • If a feedback entry touches multiple themes, assign the dominant one but note the secondary theme.
  • Urgency is based on language intensity ("broken", "can't use", "blocking" = High) and frequency, not on your opinion of importance.
  • Minimum 3 themes required. If all feedback collapses into one theme, split it into sub-themes.
  • Always include at least one verbatim quote per theme. Real words beat summaries.
  • Write in English.
  • Keep analysis output under 2 pages. The raw classified data table can be longer if needed.
  • Be opinionated in recommendations: tell the PM what to do first and why, don't just list options.
  • If the feedback volume is too small to draw conclusions (fewer than 5 entries), say so explicitly and flag which patterns are weak signals.

Frequently asked questions

What does the Feedback Analyzer AI skill do?

Classify and analyze raw user feedback into themes, sentiment, and actionable insights. Takes pasted feedback text or a path to a CSV file and returns structured patterns and recommendations.

Why use Feedback Analyzer on TypingMind?

Because you install it once and use it with any model. Feedback Analyzer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Feedback Analyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mehdibargach/claude-code-pm-skills/tree/main/skills/feedback-analyzer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Feedback Analyzer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Feedback Analyzer?

As many as you like. As long as a model supports skills, you can use Feedback Analyzer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Feedback Analyzer AI skill free?

It is published on GitHub by Mehdibargach. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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