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Feedback Analyzer

Checked
jmsktm·vlatest·other

Analyzes user feedback to identify key themes and sentiment, streamlining insights for product improvement and customer understanding.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

Installs under a different name. Its SKILL.md names it Feedback Analyzer, not the name it is listed under, so the command below uses that name.
  1. 1

    Run this in your project

    terminal
    $ npx skills add jmsktm/claude-settings --skill "Feedback Analyzer" -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

jmsktm

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned jmsktm/claude-settings at c113b0d7e1.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
c113b0d7e1
Manifest
skills/feedback-analyzer/SKILL.md
Scanned
1 file, 379 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

The Feedback Analyzer skill provides a way to analyze feedback text and extract key insights. It can identify sentiment, categorize topics within the feedback, and summarize overall themes. This allows users to quickly understand customer opinions or evaluate responses from AI agents.

When to use it

  • Analyzing customer reviews for product improvement.
  • Evaluating the quality of responses generated by an AI chatbot.
  • Summarizing survey results to identify common concerns.
  • Gauging public reaction to a new marketing campaign.

Key capabilities

  • Sentiment analysis (positive, negative, neutral)
  • Topic categorization
  • Feedback summarization

Example prompts

  • "Analyze this customer review: 'The product was great but the shipping took too long.'"
  • "Summarize these survey responses about our new feature."
  • "What is the overall sentiment expressed in this collection of feedback?"

Tips & gotchas

This skill requires a clear and concise input text for accurate analysis. Very short or ambiguous feedback may yield less reliable results.