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 asodevapp/skills --skill reviews-ratingsgit clone --depth 1 https://github.com/asodevapp/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/asodevapp/skills/reviews-ratings)<a href="https://agentmods.dev/skills/asodevapp/skills/reviews-ratings"><img src="https://agentmods.dev/badge/skills/asodevapp/skills/reviews-ratings/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/asodevapp/skills/reviews-ratings"><img src="https://agentmods.dev/badge/skills/asodevapp/skills/reviews-ratings.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 | $0.00063 | $0.01556 |
| Opus 5 | $0.00032 | $0.00778 |
| Sonnet 5 | $0.00013 | $0.00311 |
| Haiku 4.5 | $0.00006 | $0.00156 |
Grade A, and why
reviews-ratings 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 — 195 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review Management
Use this for review analysis, reputation management, reply drafts, complaint/report drafts, product insight mining, uploaded review CSV analysis, and rating improvement strategy.
MCP workflow
- Use
app-store-connect-mcp. - Single review: read with
get_review_data. - Review list: read with
get_reviews_data. - Use
unansweredOnly,badRatingOnly, andmaxRatingto narrow review lists. - Use project context for tone, support links, known issues, and response policy.
- Analyze review health, themes, response opportunities, and complaint/report eligibility.
- Validate with
validate_ai_companion_datatargetrevieworreviewList. - If asked to apply, fill drafts with
fill_revieworfill_reviews.
Fill fields
Single review:
responseBodycomplaintReasonconcernType
Review list:
reviews: array or map byreviewId, each withresponseBody,complaintReason, and/orconcernType
CSV review analysis
Use this mode when the user uploads or references a CSV with all reviews. CSV analysis does not require MCP unless the user wants to fill replies/complaints back into ASO.dev.
Before analysis:
- Identify the CSV columns. Common columns: review ID, rating, title, body/text, locale/country, app version, date, developer response, response date, device/platform.
- Normalize dates, ratings, locales, versions, and empty values.
- Deduplicate repeated rows by review ID or exact title/body/date.
- Segment by rating, locale/country, app version, date period, answered/unanswered status, and review length.
- If the CSV is large, sample examples for quotes but compute counts/themes across the full file.
Analyze for:
- Sentiment by rating and text: positive, neutral, negative, mixed.
- Themes: bugs/crashes, UX friction, pricing/paywall, onboarding, performance, account/login, localization, support, content quality.
- Feature requests: missing capabilities, repeated "wish it had..." patterns, competitor comparisons.
- Product risk: regressions by app version/date, severe bugs, refund/cancel intent, trust/privacy concerns.
- ASO language: phrases users use to describe value, outcomes, and use cases.
- Support opportunities: unanswered low-star reviews, stale responses, high-impact replies.
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 · 195 lines · 63 tokens per session scan A 124336fed8a6
reviews-ratings is a skill published in the GitHub repository asodevapp/skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 1,556 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 2026-08-31.
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