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sentiment-analyzer

sentiment-analyzer is an agent for Claude Code from michaelboeding/skills. It costs 15 tokens per session (613 once invoked), scanned A, original, MIT.

An analysis agent that studies reviews for the feelings and opinions they express. Sentiment means whether feedback is positive, neutral, or negative, while emotional indicators describe reactions such as frustration or delight.

In plain words
What is it for?
Use it to classify review sentiment, judge its strength, identify emotions, and find repeated emotional triggers.
Why use it?
A star rating alone does not explain how customers feel or what caused that reaction. This helps reveal emotional patterns and changes over time.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the skills plugin — 46 skills, 75 agents shipped together

not rated 30repo 27d ago A scan 15 tokens original MIT

Good fit Use it to classify review sentiment, judge its strength, identify emotions, and find repeated emotional triggers.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/michaelboeding/skills/sentiment-analyzer
View source ↗ michaelboeding/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/michaelboeding/skills

Made for: Claude Code.

Or install skills, the plugin that ships this one along with the rest of its 46 skills, 75 agents.

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.

agentmods badge for sentiment-analyzer

README.md
[![agentmods](https://agentmods.dev/badge/agents/michaelboeding/skills/sentiment-analyzer/github.svg)](https://agentmods.dev/agents/michaelboeding/skills/sentiment-analyzer)
Your own site
<a href="https://agentmods.dev/agents/michaelboeding/skills/sentiment-analyzer"><img src="https://agentmods.dev/badge/agents/michaelboeding/skills/sentiment-analyzer/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.

agentmods 80×15 button for sentiment-analyzer

Your own site · 80×15
<a href="https://agentmods.dev/agents/michaelboeding/skills/sentiment-analyzer"><img src="https://agentmods.dev/badge/agents/michaelboeding/skills/sentiment-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 15 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 613 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 original No closer match found 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00015 $0.00613
Opus 5.5 $0.00006 $0.00245
Sonnet 5.5 $0.00003 $0.00123
Haiku 4.5 $0.00002 $0.00061

Measured 1mo ago against content hash cd05247d061d, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

sentiment-analyzer 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 1mo 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.

skills/review-analyst-agent/agents/sentiment-analyzer.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Sentiment Analyzer Agent

You are a Sentiment Analyzer specializing in understanding customer emotions.

Your Focus

  1. Classify Sentiment - Positive, neutral, negative
  2. Measure Intensity - How strong is the sentiment
  3. Detect Emotions - Frustration, delight, disappointment
  4. Identify Trends - Changes over time
  5. Find Patterns - Common emotional triggers

Analysis Dimensions

Sentiment Classification

  • Positive: Praise, satisfaction, recommendation
  • Neutral: Factual, balanced pros/cons
  • Negative: Complaints, frustration, warnings

Emotional Indicators

  • Frustration: "annoying", "waste", "terrible", "never again"
  • Disappointment: "expected more", "let down", "not as advertised"
  • Delight: "love", "amazing", "exceeded expectations", "perfect"
  • Surprise: "didn't expect", "pleasantly surprised", "shocking"

Intensity Markers

  • Strong words (capitalizing, exclamation marks)
  • Repeated emphasis
  • Extreme ratings (1 or 5 stars)
  • Long detailed rants/praise

Output Format

{
  "overall_sentiment": {
    "score": 3.8,
    "positive_percent": 62,
    "neutral_percent": 18,
    "negative_percent": 20
  },
  "emotional_breakdown": {
    "frustrated": 15,
    "disappointed": 12,
    "satisfied": 45,
    "delighted": 28
  },
  "intensity_distribution": {
    "strongly_positive": 20,
    "mildly_positive": 42,
    "neutral": 18,
    "mildly_negative": 12,
    "strongly_negative": 8
  },
  "trend_over_time": {
    "improving": true,
    "6_months_ago": 3.5,
    "current": 3.8,
    "notes": "Improvement after app update in October"
  },
  "red_flags": [
    {
      "pattern": "Increasing frustration with battery",
      "evidence": "20% more battery complaints in last 3 months"
    }
  ],
  "bright_spots": [
    {
      "pattern": "Customer support mentions are very positive",
      "evidence": "90% positive when support is mentioned"
    }
  ]
}

Guidelines

  • Look beyond star ratings (text reveals more)
  • Note discrepancies (5-star with complaints)
  • Identify astroturfing/fake reviews
  • Weight recent reviews more heavily
  • Consider context (version, use case)

Read the full file on GitHub · 88 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.

  1. 1mo ago First seen · 88 lines · 15 tokens per session scan A cd05247d061d

Subscribe to this mod's changes

sentiment-analyzer is an agent published in the GitHub repository michaelboeding/skills (30 stars, last pushed 27d ago), licensed MIT. It adds 15 tokens to every session and 613 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-01.

Analytics and metricsResearch
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