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social-listening-analyst skill

/skills/social-listening-analyst

This skill helps monitor brand mentions, analyze sentiment, and identify trends across social platforms to inform strategy and insights.

npx playbooks add skill eddiebe147/claude-settings --skill social-listening-analyst

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---
name: Social Listening Analyst
slug: social-listening-analyst
description: Monitor brand mentions, analyze sentiment, and identify trends across social platforms
category: research
complexity: simple
version: "1.0.0"
author: "ID8Labs"
triggers:
  - "social listening"
  - "brand monitoring"
  - "sentiment analysis"
  - "social trends"
  - "brand mentions"
tags:
  - listening
  - monitoring
  - sentiment
  - trends
  - analytics
---

# Social Listening Analyst

Monitor brand mentions, analyze sentiment, and identify trends across social platforms

## When to Use This Skill

Use this skill when you need to:
- Analyze data and extract insights
- Conduct thorough investigation
- Synthesize complex information

**Not recommended for:**
- Tasks requiring creative content generation
- business operations

## Quick Reference

| Action | Command/Trigger |
|--------|-----------------|
| Create social listening analyst | `social listening` |
| Review and optimize | `review social listening analyst` |
| Get best practices | `social listening analyst best practices` |

## Core Workflows

### Workflow 1: Initial Social Listening Analyst Creation

**Goal:** Create a high-quality social listening analyst from scratch

**Steps:**
1. **Discovery** - Understand requirements and objectives
2. **Planning** - Develop strategy and approach
3. **Execution** - Implement the plan
4. **Review** - Evaluate results and iterate
5. **Optimization** - Refine based on feedback

### Workflow 2: Advanced Social Listening Analyst Optimization

**Goal:** Refine and optimize existing social listening analyst for better results

**Steps:**
1. **Research** - Gather relevant information
2. **Analysis** - Evaluate options and approaches
3. **Decision** - Choose the best path forward
4. **Implementation** - Execute with precision
5. **Measurement** - Track success metrics

## Best Practices

1. **Start with Clear Objectives**
   Define what success looks like before beginning work.

2. **Follow Industry Standards**
   Leverage proven frameworks and best practices in research.

3. **Iterate Based on Feedback**
   Continuously improve based on results and user input.

4. **Document Your Process**
   Keep track of decisions and outcomes for future reference.

5. **Focus on Quality**
   Prioritize excellence over speed, especially in early iterations.

## Checklist

Before considering your work complete:

- [ ] Objectives clearly defined and understood
- [ ] Research and discovery phase completed
- [ ] Strategy or plan documented
- [ ] Implementation matches requirements
- [ ] Quality standards met
- [ ] Stakeholders informed and aligned
- [ ] Results measured against goals
- [ ] Documentation updated
- [ ] Feedback collected
- [ ] Next steps identified

## Common Mistakes

| Mistake | Why It's Bad | Better Approach |
|---------|--------------|-----------------|
| Skipping research | Leads to misaligned solutions | Invest time in understanding context |
| Ignoring best practices | Reinventing the wheel | Study successful examples first |
| No clear metrics | Can't measure success | Define KPIs upfront |

## Integration Points

- **Tools**: Integration with common research platforms and tools
- **Workflows**: Fits into existing analysis and research workflows
- **Team**: Collaborates with research and analytics stakeholders

## Success Metrics

Track these metrics to measure effectiveness:
- Quality of output
- Time to completion
- Stakeholder satisfaction
- Impact on business goals
- Reusability of approach

---

*This skill is part of the ID8Labs Skills Marketplace. Last updated: 2026-01-07*

Overview

This skill helps monitor brand mentions, analyze sentiment, and identify emerging trends across social platforms. It combines structured discovery, data-driven analysis, and iterative optimization to deliver actionable insights for brand and research teams. Use it to turn noisy social data into clear, measurable outcomes.

How this skill works

The skill ingests mention streams and social data, applies sentiment analysis and trend-detection routines, then synthesizes findings into concise reports and recommended actions. It follows a repeatable lifecycle: discovery to define objectives, planning to set scope and KPIs, execution to gather and analyze data, then review and optimization to refine models and workflows. Outputs include dashboards, prioritized issues, and a roadmap for follow-up.

When to use it

  • Launching or protecting a brand during a campaign or crisis
  • Tracking sentiment shifts after a product release or announcement
  • Identifying influencers, recurring complaints, and emerging topics
  • Validating hypotheses from customer research or market intelligence
  • Establishing baseline metrics and ongoing monitoring for reputation management

Best practices

  • Start with clear objectives and measurable KPIs before collecting data
  • Define scope: platforms, languages, date ranges, and mention types to reduce noise
  • Use industry-standard sentiment models and validate with manual samples
  • Document assumptions, filters, and decision criteria for reproducibility
  • Iterate: tune queries and models based on feedback and measured performance

Example use cases

  • Monitor sentiment during a product launch to detect early negative feedback
  • Detect and prioritize recurring support issues surfaced on social channels
  • Track competitor mentions and industry topics to inform strategy
  • Generate weekly insight briefs for stakeholders with recommended actions
  • Identify micro-trends and rising keywords that signal new opportunities

FAQ

What inputs are required to start social listening?

Define objectives, select target platforms and time windows, provide brand keywords, competitors, and any relevant hashtags or phrases.

How do you ensure sentiment accuracy?

Combine automated models with manual sampling for validation, tune models for domain language, and iterate using labeled examples to reduce false positives.