sentiment-analyzer — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited sentiment-analyzer (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Analyze sentiment in customer feedback using transformer models - understand what your customers really feel at scale.
| 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 |
pip install transformers torch pandas click
# Or for lighter CPU-only version:
pip install textblob vaderSentiment pandas clickpython scripts/main.py analyze "This product exceeded my expectations!"
python scripts/main.py analyze "The service was terrible and slow."python scripts/main.py batch reviews.csv --column text
python scripts/main.py batch feedback.csv --column comment --output results.csvpython scripts/main.py report reviews.csv --column text --output sentiment-report.html# 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# 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)| 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 |
category: analytics
subcategory: nlp
dependencies: [transformers, torch, pandas]
difficulty: intermediate
time_saved: 6+ hours/week~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.