Measure and optimize growth using the AARRR (Pirate Metrics) framework with stage-specific KPIs and funnel analysis
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guia-matthieu/clawfu-skills1502026년 10월 1일 업데이트
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sentiment-analyzer
Analyze sentiment in text using ML models. Use when: analyzing customer reviews; processing NPS feedback; monitoring brand mentions; evaluating campaign responses; categorizing support tickets
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guia-matthieu/clawfu-skills스타 150(GitHub 150 + cccskills 0)
실행 점수 미검증
설치 방법 보기포함된 파일(3)
- SKILL.md3.6 KB
- scripts/main.py9.6 KB
- scripts/requirements.txt119 B
SKILL.md(원문)
설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Sentiment Analyzer
Analyze sentiment in customer feedback using transformer models - understand what your customers really feel at scale.
When to Use This Skill
- Review analysis - Process hundreds of product reviews
- NPS feedback - Categorize open-ended survey responses
- Social listening - Monitor brand sentiment on social media
- Campaign feedback - Evaluate response to marketing campaigns
- Support insights - Categorize support ticket sentiment
What Claude Does vs What You Decide
| Claude Does | You Decide |
|---|---|
| Structures analysis frameworks | Metric definitions |
| Identifies patterns in data | Business interpretation |
| Creates visualization templates | Dashboard design |
| Suggests optimization areas | Action priorities |
| Calculates statistical measures | Decision thresholds |
Dependencies
pip install transformers torch pandas click
# Or for lighter CPU-only version:
pip install textblob vaderSentiment pandas click
Commands
Analyze Text
python scripts/main.py analyze "This product exceeded my expectations!"
python scripts/main.py analyze "The service was terrible and slow."
Batch Analysis
python scripts/main.py batch reviews.csv --column text
python scripts/main.py batch feedback.csv --column comment --output results.csv
Generate Report
python scripts/main.py report reviews.csv --column text --output sentiment-report.html
Examples
Example 1: Analyze Product Reviews
# Process CSV of reviews
python scripts/main.py batch amazon-reviews.csv --column review_text
# Output: amazon-reviews_sentiment.csv
# review_text | sentiment | score | label
# "Absolutely love this!" | positive | 0.95 | Very Positive
# "It's okay, nothing special" | neutral | 0.52 | Neutral
# "Worst purchase ever" | negative | 0.12 | Very Negative
Example 2: NPS Feedback Categorization
# Analyze NPS survey responses
python scripts/main.py report nps-responses.csv --column feedback
# Output: sentiment-report.html
# Summary:
# - Positive: 62% (mainly: product quality, support)
# - Neutral: 23% (mainly: pricing concerns)
# - Negative: 15% (mainly: shipping delays)
Sentiment Categories
| Score Range | Label | Interpretation |
|---|---|---|
| 0.8 - 1.0 | Very Positive | Enthusiastic, recommend |
| 0.6 - 0.8 | Positive | Satisfied, happy |
| 0.4 - 0.6 | Neutral | Mixed or indifferent |
| 0.2 - 0.4 | Negative | Disappointed, frustrated |
| 0.0 - 0.2 | Very Negative | Angry, will churn |
Skill Boundaries
What This Skill Does Well
- Structuring data analysis
- Identifying patterns and trends
- Creating visualization frameworks
- Calculating statistical measures
What This Skill Cannot Do
- Access your actual data
- Replace statistical expertise
- Make business decisions
- Guarantee prediction accuracy
Related Skills
- social-analytics - Get social data to analyze
- content-repurposer - Use insights for content
Skill Metadata
- Mode: centaur
category: analytics
subcategory: nlp
dependencies: [transformers, torch, pandas]
difficulty: intermediate
time_saved: 6+ hours/week
리뷰
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같은 저장소의 스킬
Calculate A/B test statistical significance. Use when: determining if test results are significant; calculating required sample size; estimating test duration; analyzing conversion experiments; making data-driven decisions
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
guia-matthieu/clawfu-skills1502026년 10월 1일 업데이트
Assess customer account health using product usage, support sentiment, payment status, and relationship signals
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
guia-matthieu/clawfu-skills1502026년 10월 1일 업데이트
Analyze paid advertising performance across channels and recommend budget reallocation to maximize ROAS and minimize CAC. Use when: planning quarterly ad budget allocation, diagnosing underperforming ad channels, deciding whether to scale spend on a channel, calculating marginal ROI across Google Ads, Meta, LinkedIn, or TikTok, rebalancing media mix after performance shifts, or setting up a test-and-scale framework for new channels.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
guia-matthieu/clawfu-skills1502026년 10월 1일 업데이트
Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
guia-matthieu/clawfu-skills1502026년 10월 1일 업데이트
Use when analyzing server logs to understand how AI crawlers (GPTBot, ClaudeBot, PerplexityBot) interact with your site. Use when optimizing content placement for LLM retrieval, diagnosing why AI search isn't citing your content, or auditing crawl patterns to find optimization gaps.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
guia-matthieu/clawfu-skills1502026년 10월 1일 업데이트