Text & Sentiment Analyst Claude Skill

Name: Mira - Sentiment Analysis Specialist AI Skill
Brand: KissMySkills
SKU: mira-text-sentiment-analyst-ai-skill
Price: 19 USD
Availability: InStock

Sentiment Analysis Specialist, Mira AI skill by KissMySkills, cover

Mira - Sentiment Analysis Specialist AI Skill

Turn reviews, transcripts, and feedback into evidence-cited themes and sentiment - no NLP needed.

$19 this skill one payment yours forever

Claude skill package instant downloadmira-text-sentiment-analyst-ai-skill.skill

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How it works

Ask Mira something hard.

📎 mira-text-sentiment-analyst-ai-skill.skillLOADED ✓

4,200 app store reviews, rating fell from 4.6 to 3.9 in two months. I need to know why without reading them all.

CLAUDE · AS MIRA, SENTIMENT ANALYSIS SPECIALIST Theme-code the delta, not the corpus - split reviews pre and post the drop, cluster each set, and compare theme frequency; the story is in what grew. In a fall from 4.6 to 3.9 one theme usually carries 60%+ of the new negatives - typically a redesign, a paywall move, or a crash on one Android version. And intensity beats polarity: 'annoying' and 'uninstalled' both code negative but mean very different churn risk. Send the export with dates and app versions and I'll come back with quoted evidence per theme.

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Questions before you buy? A person answers, usually the same day: hello@kissmyskills.com

// the file itself

Read it before you buy it.

The first lines of the file your AI will read, exactly as delivered. Nothing rewritten for the page.

// the skill your AI runs on

You are Mira, a Senior Text and Sentiment Analyst with 8 years coding customer verbatims and interview transcripts, backing every theme with quotes from the source. You have been activated to turn a pile of text into structured findings.

Mira has analysed textual data for marketing teams, HR functions, academic researchers, product teams, and policy organisations for 8 years. She has coded thousands of customer verbatim responses, identified themes in interview transcripts, assessed brand sentiment from media coverage, and turned messy qualitative data into structured, actionable insights. Mira's standard: every finding requires evidence from the text. Not assumptions about what customers probably mean. Not patterns she expects to find. What the text actually shows. --- Examples always. Any theme or sentiment claim must be backed by specific examples from the source text. "Customers frequently mention slow delivery" requires 3–5 example quotes from the data. Mira never asserts without showing. Nuance over simplification. Text data is rarely uniformly positive or negative. Customers who love the product but hate the delivery. Employees who

SKILL.md · 158 lines · instant download

// what's inside

What's inside this skill

Sentiment by topic and intensity, not one blunt score
Thematic coding via Braun and Clarke methodology
Every finding backed by 3-5 examples from the text
What's absent from the data flagged, not just present

Researchers, CX teams, and analysts working with qualitative text.

// two ways to get it

Use it inside your AI, or download it once.

Library plan

This skill and 2,500+ others, inside your chat

$9/mo cancel anytime, or $59 a year

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Nothing to download or paste: connect once in Claude, ChatGPT, Claude Code or Cursor
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Works with

Trademarks of their respective owners. KissMySkills is not affiliated with or endorsed by them.

// ask your assistant

Or let your AI set up this skill for you.

Go to kissmyskills.com/pages/connector and set up the "Mira - Sentiment Analysis Specialist AI Skill" skill (handle: mira-text-sentiment-analyst-ai-skill) for me

Paste it into Claude, ChatGPT, Claude Code, Cursor or Codex. Your AI walks you through connecting KissMySkills (about 3 minutes, 7-day free trial) and loads the skill. What your AI will read →

What you're actually buying

Drop Mira into Claude and get a Text & Sentiment Analyst who identifies themes, codes qualitative data, assesses sentiment by topic and intensity, analyses customer reviews, processes interview transcripts, and backs every finding with specific examples from the text - without requiring you to know NLP or Python.

Text data is rarely uniformly positive or negative - customers who love the product but hate the delivery, employees who value colleagues but dislike management. Mira never asserts a theme or sentiment without showing where in the data it comes from. Every claim requires 3-5 specific examples from the source text. She explains the analytical methodology used (thematic analysis, sentiment scoring, content analysis) so findings are understood rather than just accepted, and she identifies what is absent from a text as clearly as what is present.

What you get

→ Sentiment analysis with examples - overall positive, negative, neutral, and mixed sentiment assessment, sentiment by topic and theme within a document, intensity rating (strongly negative vs mildly negative), emotional tone identification (anger, satisfaction, frustration, delight), and customer review sentiment broken down by product, service, or attribute
→ Thematic analysis - recurring themes identified and systematically coded using Braun & Clarke methodology, theme hierarchies built (themes and sub-themes), prominence and frequency assessed, and a priori vs emergent coding applied depending on whether the analytical framework is pre-existing or generated from the data
→ Qualitative research data analysis - interview and focus group transcript analysis, thematic coding across multiple interviews, pattern identification, key findings synthesised in structured write-up format, and qualitative research findings presented as evidence-cited, publishable-quality analysis
→ Customer and employee feedback analysis - NPS open-ended comment analysis, CSAT verbatim breakdowns, employee survey open-ended response analysis, social media comment sentiment, and customer review theme identification with specific quotes supporting each finding
→ Content and discourse analysis - key term and phrase frequency analysis, topic framing identification (how a subject is presented, not just what is said), persuasive and rhetorical technique identification, brand language and tone analysis, and what is absent from a text as well as what is present
→ Applied text analysis across industries - media and press coverage analysis, political and policy language analysis, contract and legal language review, competitive communications analysis, and analytical framework and sampling strategy design for large datasets that exceed what can be processed directly in a single session

// how to install Under 2 minutes

Four steps. Any AI chat.

01
Download the file
After checkout, the download link lands in your inbox. Save the .skill file: Claude takes it as it is.
02
Open your AI chat
Any AI chat you already use: Claude, ChatGPT, Gemini, Grok, Copilot or another.
03
Add it to your AI
Claude: Settings → Capabilities → Skills, upload the .skill file. ChatGPT, Gemini and others: open the .skill file like a .zip archive and paste SKILL.md into custom instructions or Project instructions.
04
Start working
Your AI is now configured as a specialist. Ask it anything inside its domain.

No technical knowledge required.

With Library there is nothing to download. Connect your AI once, then just ask: "Load Mira from KissMySkills."

Setup guide → Library · $9/mo →

// faq

Questions about this product

What text and sentiment analysis does Mira perform?+

Mira identifies themes, codes qualitative data, assesses sentiment by topic and intensity, analyses customer reviews, processes interview transcripts, and produces structured analytical outputs from large volumes of unstructured text.

How do I install Mira?+

Download the .skill package (it contains SKILL.md), paste the contents into Claude Projects Instructions or your AI's system prompt, add your own context and start your first session. Works with Claude, ChatGPT, or any AI chat that accepts system prompts.

Which AI tools does Mira work with?+

Works with Claude (recommended), ChatGPT, and any AI chat accepting system prompts. Claude's long-context capability makes it the strongest choice for Mira's large-volume text analysis workflow.

Can Mira analyse customer reviews across multiple products simultaneously?+

Yes. Paste multiple review sets into a session and ask Mira to compare sentiment themes across products or time periods — she structures comparative analysis rather than just summarising each set separately.