Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add guia-matthieu/clawfu-skills --skill sentiment-analyzergit clone --depth 1 https://github.com/guia-matthieu/clawfu-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/guia-matthieu/clawfu-skills/sentiment-analyzer)<a href="https://agentmods.dev/skills/guia-matthieu/clawfu-skills/sentiment-analyzer"><img src="https://agentmods.dev/badge/skills/guia-matthieu/clawfu-skills/sentiment-analyzer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/guia-matthieu/clawfu-skills/sentiment-analyzer"><img src="https://agentmods.dev/badge/skills/guia-matthieu/clawfu-skills/sentiment-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.00848 |
| Opus 5 | $0.00018 | $0.00424 |
| Sonnet 5 | $0.00007 | $0.00170 |
| Haiku 4.5 | $0.00004 | $0.00085 |
Grade A, and why
sentiment-analyzer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 127 lines · 37 tokens per session scan A f9dac6442951
sentiment-analyzer is a skill published in the GitHub repository guia-matthieu/clawfu-skills (149 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 848 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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