スキル一覧に戻る

social-listening

guia-matthieu
更新日 Yesterday
3 閲覧
111
20
111
GitHubで表示
その他general

について

このスキルは、ソーシャルメディアやオンライン上の言及を自動監視し、ブランドの評判感情を追跡、新たに発生する問題を特定、会話のトレンドを分析することを可能にします。キーワード監視と感情分析を活用し、ブランド健全性の監視、危機の早期警告、競合他社の追跡を目的として設計されています。開発者はこれを活用して顧客インサイトを収集し、体系的な分析に基づいた対応戦略を立案できます。

クイックインストール

Claude Code

推奨
メイン
npx skills add guia-matthieu/clawfu-skills -a claude-code
プラグインコマンド代替
/plugin add https://github.com/guia-matthieu/clawfu-skills
Git クローン代替
git clone https://github.com/guia-matthieu/clawfu-skills.git ~/.claude/skills/social-listening

このコマンドをClaude Codeにコピー&ペーストしてスキルをインストールします

ドキュメント

Social Listening

Systematically monitor social media and online conversations to track brand sentiment, identify emerging issues, and spot opportunities.

When to Use This Skill

  • Brand health monitoring
  • Crisis early warning
  • Competitor tracking
  • Campaign performance
  • Customer insight gathering

Methodology Foundation

Based on Sprout Social methodology and Brandwatch analytics, combining:

  • Keyword monitoring
  • Sentiment analysis
  • Trend identification
  • Influencer tracking

What Claude Does vs What You Decide

Claude DoesYou Decide
Designs monitoring strategyTool selection
Creates keyword listsAlert thresholds
Analyzes sentiment patternsResponse strategy
Identifies trendsResource allocation
Suggests response approachesEscalation calls

Instructions

Step 1: Define Monitoring Scope

Monitoring Categories:

CategoryWhat to TrackExamples
BrandCompany name, products"@CompanyName", "CompanyProduct"
PeopleExecutives, spokespeopleCEO name, founder
IndustrySector terms, trendsIndustry keywords
CompetitorsCompetitor mentionsCompetitor names
IssuesPotential crisesProduct issues, service complaints

Step 2: Build Keyword Matrix

Keyword Types:

TypePurposeExamples
PrimaryDirect brand mentionsBrand name, product names
SecondaryRelated conversationsIndustry terms, use cases
NegativeCrisis indicators"problem", "issue", "broken"
CompetitorCompetitive intelligenceCompetitor names, comparison

Step 3: Set Up Alerts

Alert Levels:

LevelTriggerResponse Time
GreenNormal volume, positive/neutralDaily review
YellowElevated volume, mixed sentimentSame-day review
OrangeHigh volume spike, negative trendHours
RedViral negative, crisis indicatorsImmediate

Step 4: Analyze Findings

Analysis Framework:

  • Volume trends (up/down/stable)
  • Sentiment distribution (positive/neutral/negative)
  • Key themes and topics
  • Influential voices
  • Geographic patterns

Step 5: Report and Act

Reporting Cadence:

ReportFrequencyAudience
Real-time alertsAs triggeredCrisis team
Daily digestDailyMarketing
Weekly summaryWeeklyLeadership
Monthly deep diveMonthlyStrategy

Examples

Example 1: Social Listening Strategy

Input:

Create a social listening strategy for:
- B2B SaaS company (project management)
- Main competitors: Asana, Monday, Notion
- Recent product launch: AI features

Output:

## Social Listening Strategy: Project Management SaaS

### Monitoring Objectives

| Objective | Priority | Key Questions |
|-----------|----------|---------------|
| Brand health | High | How are we perceived? |
| Product feedback | High | What do users say about AI features? |
| Competitive intel | Medium | What are competitors doing? |
| Industry trends | Medium | Where is PM software heading? |
| Crisis detection | High | Any emerging issues? |

---

### Keyword Matrix

#### Brand Keywords

| Category | Keywords | Platforms |
|----------|----------|-----------|
| Company Name | "[Company]", "@company" | All |
| Product Name | "[Product]", "[Product] app" | All |
| Misspellings | Common variants | All |
| Hashtags | #[Company], #[Product] | Twitter, LinkedIn |

#### Product Keywords

| Category | Keywords | Why Monitor |
|----------|----------|-------------|
| AI Features | "[Company] AI", "AI project management" | Launch feedback |
| Core Features | "[Company] tasks", "[Company] boards" | Product sentiment |
| Integrations | "[Company] Slack", "[Company] integration" | Partnership health |

#### Competitive Keywords

| Competitor | Keywords | What to Track |
|------------|----------|---------------|
| Asana | "Asana vs [Company]", "switching from Asana" | Win/loss signals |
| Monday | "Monday.com", "Monday vs [Company]" | Competitive positioning |
| Notion | "Notion for projects", "Notion PM" | Category overlap |
| General | "best project management", "PM tool 2026" | Category conversations |

#### Crisis Keywords

| Category | Keywords | Alert Level |
|----------|----------|-------------|
| Outage | "[Company] down", "[Company] not working" | Red |
| Security | "[Company] hack", "[Company] breach" | Red |
| Pricing | "[Company] expensive", "[Company] price increase" | Orange |
| Churn | "leaving [Company]", "cancelled [Company]" | Yellow |
| Bugs | "[Company] bug", "[Company] broken" | Yellow |

---

### Platform Strategy

| Platform | Focus | Keywords | Frequency |
|----------|-------|----------|-----------|
| Twitter/X | Real-time sentiment | All brand, crisis | Continuous |
| LinkedIn | B2B discussions | Industry, competitor | Daily |
| Reddit | Deep user feedback | r/projectmanagement, product | Daily |
| G2/Capterra | Review sentiment | Product reviews | Weekly |
| Hacker News | Tech community | Product, competitor | As trending |

---

### Alert Configuration

#### Red Alerts (Immediate Response)

**Triggers:**
- Volume spike >300% normal
- Sentiment shift >50% negative
- Viral post (>1000 engagements)
- Keywords: "outage", "down", "breach", "hack"

**Response:**
- Slack #crisis-alerts channel
- SMS to on-call team
- Auto-pause scheduled posts

---

#### Orange Alerts (Same-Day Response)

**Triggers:**
- Volume spike >100% normal
- Negative sentiment >30%
- Trending competitor comparison
- Keywords: "expensive", "worse", "frustrated"

**Response:**
- Slack #social-alerts
- Email to marketing lead
- Review within 4 hours

---

#### Yellow Alerts (Next-Day Review)

**Triggers:**
- Volume spike >50% normal
- Notable influencer mention
- Competitor activity spike
- Keywords: "considering", "alternative", "switching"

**Response:**
- Daily digest inclusion
- Assign for monitoring
- Review within 24 hours

---

### Analysis Template

#### Daily Report

Social Listening Daily Digest

Date: [Date]

Volume Summary

MetricTodayvs. Yesterdayvs. 7-Day Avg
Total MentionsX+/-Y%+/-Z%
Brand MentionsX+/-Y%+/-Z%
Sentiment ScoreX+/-Y+/-Z

Sentiment Breakdown

  • Positive: X%
  • Neutral: Y%
  • Negative: Z%

Top Conversations

  1. [Summary + Link]
  2. [Summary + Link]
  3. [Summary + Link]

Competitor Activity

  • Asana: [Notable activity]
  • Monday: [Notable activity]
  • Notion: [Notable activity]

Action Items

  • [Action if needed]

Alert Status: GREEN/YELLOW/ORANGE/RED


---

### AI Feature Launch Monitoring

**Specific Keywords:**
- "[Company] AI"
- "AI project management"
- "[Company] automation"
- "AI features PM tool"

**What to Track:**
| Metric | Target | Alert If |
|--------|--------|----------|
| Mention volume | 2x normal | <50% or >300% |
| Sentiment | >60% positive | <40% positive |
| Feature requests | Document all | - |
| Bug reports | <5/day | >10/day |

**Competitor Response Monitoring:**
- Are competitors announcing similar features?
- How is our positioning being compared?
- What gaps are users identifying?

Example 2: Quick Sentiment Analysis

Input:

Analyze this week's social mentions:
- Total mentions: 450 (up 30%)
- Positive: 40%
- Neutral: 35%
- Negative: 25%
- Top negative theme: "pricing"
- One viral tweet (15K RT): "Just discovered [Company] raised prices 40%"

Output:

## Quick Sentiment Analysis

### Alert Level: ORANGE

---

### Summary

| Metric | Value | Status |
|--------|-------|--------|
| Volume | 450 (+30%) | Elevated |
| Sentiment | 40% positive | Below target |
| Negative | 25% | Above normal |
| Viral Content | Yes | Action needed |

---

### Key Finding

**Pricing is dominating the conversation**

The viral tweet about 40% price increase is:
- Driving 30% volume spike
- Shifting sentiment negative
- Spreading misinformation (if price increase was different)

---

### Immediate Actions

| Action | Owner | Timeline |
|--------|-------|----------|
| Verify price increase details | Product/Biz | Immediate |
| Draft response to viral tweet | Comms | 2 hours |
| Prepare pricing FAQ | Support | Today |
| Monitor for escalation | Social | Ongoing |

---

### Response Options

**If price increase was less than 40%:**

Hi [Name], thanks for the feedback. Just to clarify—our recent update increased [specific] pricing by [X%], not 40%. Here's what changed and why: [link]

Happy to discuss if you have questions.


**If price increase was accurate:**

Hi [Name], we hear you. We know pricing changes are never easy to absorb. Here's why we made this decision and what you're getting in return: [link]

Our team is available if you'd like to discuss your account.


---

### Escalation Watch

Monitor for:
- Influencer pile-on
- Media pickup
- Support ticket spike
- Churn mentions

Skill Boundaries

What This Skill Does Well

  • Designing monitoring strategies
  • Creating keyword frameworks
  • Analyzing sentiment patterns
  • Structuring alert systems

What This Skill Cannot Do

  • Access social platforms
  • Monitor in real-time
  • Automatically respond
  • Know your specific tools

Iteration Guide

Follow-up Prompts:

  • "Create response templates for [scenario]"
  • "How should we handle [specific mention]?"
  • "Design a competitor monitoring dashboard"
  • "What should we track for [campaign]?"

References

  • Sprout Social Listening Guide
  • Brandwatch Analytics Methodology
  • Hootsuite Social Listening
  • Meltwater Media Intelligence

Related Skills

  • crisis-detector - Early warning escalation
  • response-coordinator - Crisis response
  • reputation-recovery - Post-crisis rebuild

Skill Metadata

  • Domain: Crisis / Marketing
  • Complexity: Intermediate
  • Mode: cyborg
  • Time to Value: 1-2 hours for strategy
  • Prerequisites: Platform access, brand context

GitHub リポジトリ

guia-matthieu/clawfu-skills
パス: skills/crisis/social-listening
0
ai-skillsanthropicclaude-codeclaude-skillsmarketingmcp-server

関連スキル

llamaguard

その他

LlamaGuardは、暴力やヘイトスピーチなど6つの安全性カテゴリーにおいて、LLMの入力と出力をモデレートするMetaの70-80億パラメータモデルです。94〜95%の精度を提供し、vLLM、Hugging Face、Amazon SageMakerを使用してデプロイ可能です。このスキルを使用して、AIアプリケーションにコンテンツフィルタリングと安全策を簡単に統合できます。

スキルを見る

cost-optimization

その他

このClaudeスキルは、リソースの適正サイジング、タグ付け戦略、支出分析を通じて、開発者がクラウドコストを最適化することを支援します。AWS、Azure、GCPにわたるクラウド支出の削減とコストガバナンスの実施のためのフレームワークを提供します。インフラコストの分析、リソースの適正サイジング、または予算制約への対応が必要な際にご利用ください。

スキルを見る

quantizing-models-bitsandbytes

その他

このスキルは、bitsandbytesを使用してLLMを8ビットまたは4ビット精度に量子化し、精度の低下を最小限に抑えつつ50〜75%のメモリ削減を実現します。限られたGPUメモリでより大規模なモデルを実行したり、推論を高速化するのに理想的で、INT8、NF4、FP4などのフォーマットをサポートしています。HuggingFace Transformersと統合され、QLoRAトレーニングや8ビットオプティマイザーを可能にします。

スキルを見る

dispatching-parallel-agents

その他

このClaudeスキルは、複数のエージェントを配備し、3つ以上の独立した問題を並行して調査・修正します。共有状態や依存関係がなく解決可能な、無関係な障害が発生するシナリオ向けに設計されています。中核となる機能は並列問題解決であり、効率を最大化するために独立した問題領域ごとに1つのエージェントを割り当てます。

スキルを見る