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Sentiment Analysis

Checked
aj-geddes·vlatest·ai-ml·MIT

Analyzes text to determine emotional tone (positive, negative, neutral), providing insights into audience perception.

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 Sentiment Analysis, 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 aj-geddes/useful-ai-prompts --skill "Sentiment Analysis" -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

aj-geddes

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 aj-geddes/useful-ai-prompts at 3f5182cfd7.

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

Commit
3f5182cfd7
Manifest
skills/sentiment-analysis/SKILL.md
Scanned
3 files, 353 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

This skill analyzes text to determine the emotional tone and attitude of the writer, categorizing input as positive, negative, or neutral. It helps AI agents interpret user intent beyond literal meaning by identifying underlying feelings in reviews, feedback, or open-ended responses.

When to use it

  • Automatically triaging customer support tickets based on urgency implied by frustration levels.
  • Summarizing long-form survey results into actionable insights regarding public opinion.
  • Monitoring social media mentions to detect emerging brand crises before they escalate.
  • Personalizing marketing responses by matching the tone of previous interactions with new leads.

Key capabilities

  • Classifies text sentiment into distinct emotional categories (positive, negative, neutral).
  • Processes unstructured natural language to extract subjective opinions.
  • Provides a quantitative measure of emotional intensity within a given context.

Example prompts

  • "Analyze the sentiment of these 50 customer reviews and tell me how many are negative."
  • "Read this email draft and rate its sentiment score from 1 to 10."
  • "Identify any negative sentiments in the following transcript of a support call."

Tips & gotchas

Ensure you provide sufficient context for ambiguous phrases, as sarcasm or idioms can sometimes confuse standard sentiment models. For best results, specify the domain (e.g., "financial news" vs. "social media comments") if your agent supports custom instructions, as tone varies significantly across industries.