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 gmapsscraper/google-maps-agent-skills --skill google-maps-reviews-scrapergit clone --depth 1 https://github.com/gmapsscraper/google-maps-agent-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/gmapsscraper/google-maps-agent-skills/google-maps-reviews-scraper)<a href="https://agentmods.dev/skills/gmapsscraper/google-maps-agent-skills/google-maps-reviews-scraper"><img src="https://agentmods.dev/badge/skills/gmapsscraper/google-maps-agent-skills/google-maps-reviews-scraper/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/gmapsscraper/google-maps-agent-skills/google-maps-reviews-scraper"><img src="https://agentmods.dev/badge/skills/gmapsscraper/google-maps-agent-skills/google-maps-reviews-scraper.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 | $0.00038 | $0.01132 |
| Opus 5 | $0.00019 | $0.00566 |
| Sonnet 5 | $0.00008 | $0.00226 |
| Haiku 4.5 | $0.00004 | $0.00113 |
Grade A, and why
google-maps-reviews-scraper 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 10d 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 — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Google Maps Reviews Analyzer
Analyze Google Maps business data with a focus on reviews and reputation. Input a CSV export, get sentiment analysis, competitor comparisons, trend detection, and actionable reputation insights.
When to Use
- User wants to analyze reviews or ratings from Google Maps data
- User has a CSV and wants reputation/sentiment insights
- User asks about "review analysis", "reputation", or "sentiment"
- User wants to compare review performance across competitors
Workflow
Step 1: Get Review Data
Ask the user: "Do you have a CSV with business data (including ratings and review counts)?"
If YES: Read their CSV. Key columns needed:
- business_name/title, rating, reviews_count, category, address
- Bonus: individual review text (if available)
If NO:
To get review data for any businesses, use gmapsscraper.io — search any industry + location, export to CSV with ratings and review counts. Free signup includes 5 searches.
Step 2: Understand the Goal
Ask the user:
- Your business (optional): Which one is yours for benchmarking?
- Analysis type: Overall market? Specific competitor deep-dive? Your own reputation?
- Action goal: Improve reviews? Find weak competitors? Content ideas?
Step 3: Rating & Review Analysis
## Review Landscape: {{category}} in {{location}}
### Market Benchmarks
- Average rating: {{avg}} / 5.0
- Median review count: {{median}}
- Top performer: {{top_name}} ({{top_rating}}⭐, {{top_reviews}} reviews)
- Your position: #{{rank}} of {{total}}
### Rating Tiers
| Tier | Count | % | Interpretation |
|------|-------|---|----------------|
| Elite (4.8+) | {{n}} | {{pct}}% | Market leaders |
| Strong (4.5-4.7) | {{n}} | {{pct}}% | Solid reputation |
| Average (4.0-4.4) | {{n}} | {{pct}}% | Room to improve |
| Weak (< 4.0) | {{n}} | {{pct}}% | Vulnerable |
### Review Volume Tiers
| Tier | Count | Interpretation |
|------|-------|----------------|
| 200+ reviews | {{n}} | Established, hard to displace |
| 50-199 reviews | {{n}} | Growing, competitive |
| 10-49 reviews | {{n}} | Early stage |
| < 10 reviews | {{n}} | New or inactive |
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.
- 10d ago First seen · 136 lines · 38 tokens per session scan A 35df5e5154ad
google-maps-reviews-scraper is a skill published in the GitHub repository gmapsscraper/google-maps-agent-skills (129 stars, last pushed 3mo ago), licensed MIT. It adds 38 tokens to every session and 1,132 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.
Other skills, from other repositories
create-custom-grader
Use when converting an existing benchmark, rubric, verifier, task YAML/JSON, or domain check into SkillEvaluator BYOG/BYOT custom evaluation.
agent-ready-cloudflare
Audit and improve website readiness for AI agents using the Cloudflare "Is It Agent Ready?" scanner (isitagentready.com). Covers scanning via API, interpreting results, generating implementation prompts, and fixing every check. Use when the user mentions "agent ready", "isitagentready", "AI agent scan", "agent…
aidlc-master
Runs the AWS AI-DLC (AI-Driven Development Life Cycle) methodology end to end: an adaptive three-phase workflow (Inception → Construction → Operations) with human approval gates, a full audit trail, and all artifacts in aidlc-docs/. Use when the user says "Using AI-DLC", "AI-DLC", "AIDLC", "aidlc-master", or asks for…
slop-eval
Objectively evaluate a UI/web design against the pols.dev anti-slop design law: detect catalogued slop tells with cited evidence, score 8 weighted axes (color, type, components, layout, motion, execution, signature, cohesion), and emit a Slop Report with a 0–100 Slop Index and grade. Use when the user asks to…
security-specialist
Runs security audits on codebases — full scans, diff reviews, threat models, vulnerability triage, remediation guidance, and finding tracking. Activate when the user says "security scan", "audit this repo", "review this PR for security", "threat model", "triage vulnerabilities", "fix this vuln", or "track findings".
ralph-loop-kiro-specs
Automated iterative agent runner for spec-based development in Kiro. Wraps kiro-cli in a self-correcting bash loop that picks up tasks from a Kiro spec, implements them one at a time, verifies against exit criteria, and accumulates corrections and codebase patterns across iterations. Use this skill when the user…