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 ekinciio/saas-growth-marketing-skills --skill review-sentimentgit clone --depth 1 https://github.com/ekinciio/saas-growth-marketing-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/ekinciio/saas-growth-marketing-skills/review-sentiment)<a href="https://agentmods.dev/skills/ekinciio/saas-growth-marketing-skills/review-sentiment"><img src="https://agentmods.dev/badge/skills/ekinciio/saas-growth-marketing-skills/review-sentiment/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/ekinciio/saas-growth-marketing-skills/review-sentiment"><img src="https://agentmods.dev/badge/skills/ekinciio/saas-growth-marketing-skills/review-sentiment.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 | $0.00068 | $0.02026 |
| Opus 5 | $0.00034 | $0.01013 |
| Sonnet 5 | $0.00014 | $0.00405 |
| Haiku 4.5 | $0.00007 | $0.00203 |
Grade A, and why
review-sentiment 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 12d 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 — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Sentiment Analyzer
Analyze customer reviews to extract sentiment, identify themes, surface feature requests and complaints, and generate actionable summaries. Works with reviews from any source - app stores, Google, Yelp, G2, Capterra, or any text-based feedback.
First Run
When a user runs /review-sentiment analyze, ALWAYS display this
guidance before asking for input:
""" 📝 Review Sentiment Analyzer
What I'll need: Paste your customer reviews below - one per line or separated by blank lines. Works with any source: app stores, G2, Capterra, Yelp, Google, support tickets, survey responses.
Minimum: 5 reviews for meaningful patterns Ideal: 20-50 reviews for strong analysis Format: Plain text. Star ratings optional but helpful.
Type "demo" to see analysis on 10 sample reviews first.
What you'll get: → Sentiment breakdown (positive/negative/neutral %) → Theme extraction (UX, pricing, support, bugs, etc.) → Top complaints and praise patterns → Feature requests ranked by frequency → Saved to REVIEW-SENTIMENT-REPORT.md
Paste your reviews below: """
Demo Mode
If the user types "demo", use these 10 sample reviews:
"Love the new dashboard! So much easier to navigate now."
"Terrible customer support. Waited 3 days for a response."
"It's okay. Does what I need but pricing feels high."
"App crashes every time I try to export a PDF. Very frustrating."
"Onboarding was smooth and the docs are great."
"The automation features saved us hours every week."
"Can't believe there's still no dark mode."
"Best tool I've found for small team project management."
"Billing is confusing. Got charged twice last month."
"Fast, reliable, and the API is well documented."
Save the demo report as REVIEW-SENTIMENT-REPORT-DEMO.md.
After showing the summary, ask: "Want to analyze your own reviews now?"
Commands
/review-sentiment analyze - Analyze Provided Reviews
Performs sentiment analysis on a set of review texts. Each review is classified by sentiment and tagged with detected themes.
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.
- 12d ago First seen · 252 lines · 68 tokens per session scan A 887e045df7ba
review-sentiment is a skill published in the GitHub repository ekinciio/saas-growth-marketing-skills (12 stars, last pushed 1mo ago), licensed MIT. It adds 68 tokens to every session and 2,026 once invoked, about $0.0003 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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