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Sentiment Analyzer

Checked
guia-matthieu·vlatest·other·MIT

This Sentiment Analyzer skill detects and categorizes emotional tones (positive, negative, neutral) in text, boosting understanding & response accuracy.

Repository

Install on your platform

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

  1. 1

    Run this in your project

    terminal
    $ npx skills add guia-matthieu/clawfu-skills --skill sentiment-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

guia-matthieu

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 guia-matthieu/clawfu-skills at 4108f5c010.

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

Commit
4108f5c010
Manifest
skills/analytics/sentiment-analyzer/SKILL.md
Scanned
3 files, 401 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 sentiment analyzer skill determines the emotional tone expressed in text. It can classify text as positive, negative, or neutral, providing a quantitative assessment of subjective language. This allows AI agents to understand and react appropriately to user input and analyze large volumes of textual data for insights.

When to use it

  • Customer Service: Analyze customer reviews or support tickets to identify areas needing improvement.
  • Social Media Monitoring: Gauge public opinion about a brand, product, or event by analyzing social media posts.
  • Content Analysis: Determine the overall sentiment of articles, blog posts, or other written content.
  • Feedback Processing: Automatically categorize and prioritize feedback based on its emotional tone.

Key capabilities

  • Sentiment classification (positive, negative, neutral)
  • Text analysis
  • Emotional tone detection

Example prompts

  • "Analyze the sentiment of this customer review: 'The product was amazing!'"
  • "What is the overall sentiment expressed in this tweet?"
  • "Can you classify the sentiment of this email as positive, negative, or neutral?"

Tips & gotchas

The accuracy of the sentiment analysis can be influenced by sarcasm and nuanced language. Consider combining this skill with other skills for more complex understanding.