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Analyze shopify app reviews

Skill applora/agent-skills/skills/analyze-shopify-app-reviews

Agent plugins/extensions for Shopify App Store intelligence.

Install
npx -y skills add applora/agent-skills --skill analyze-shopify-app-reviews

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • 27 days oldThe repository was created 27 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
  • 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Analyzes Shopify App Store review sentiment, complaint themes, feature requests, switching triggers, and merchant language using current and historical review data from Applora MCP. Use for voice-of-customer research, product prioritization, churn analysis, support diagnosis, or category-wide sentiment studies.

SKILL.md

2.8 KB, as published. Nobody here has run it

Analyze Shopify App Reviews

Convert verbatim public reviews into traceable product decisions. Do not invent sentiment percentages or themes without reading the underlying review text.

Required connection

Use the Applora MCP server at https://applora.ai/mcp:

  • get_app({ handle, includeChanges?, includeCategoryRanks?, changeTypes?, limit? })
  • get_app_reviews({ handle, rating?, usageDuration?, hasContent?, cursor?, limit? })
  • live_get_app_reviews({ handle, page?, sortBy?, rating?, hasContent?, limit? }) for current Pro verification
  • get_category({ handle, includeRankedApps?, rankedAppsLimit? }) for category-level sampling
  • get_store({ id, appHandle?, maxRating?, limit? }) only when a review's store context matters

Sampling workflow

  1. Define the decision and time sensitivity. A product-priority study needs a different sample from a support incident check.
  2. Fetch the app profile to establish rating and review-count context.
  3. Pull bounded, stratified samples:
    • 1–2 star reviews for failures and churn triggers;
    • 3 star reviews for tradeoffs and missing features;
    • 4–5 star reviews for must-preserve value.
  4. Use hasContent: true. Page only until themes stabilize; state the exact sample size.
  5. For category research, select a transparent set of leading apps and repeat the same sampling rule.
  6. Code each review into concrete themes such as reliability, setup, compatibility, support, billing, missing capability, or outcome achieved.
  7. Count mentions within the sample. Do not present sample frequency as the share of all customers.
  8. Support each important theme with a short representative quote and app/rating context.
  9. Distinguish recent operational incidents from recurring structural problems. Use a live call if freshness matters.

Analysis rules

  • Keep praise, complaint, request, and switching trigger separate.
  • Treat sarcasm and mixed reviews carefully; preserve ambiguity.
  • Never expose private contact fields.
  • Public review-inferred store relationships are signals, not confirmed current installs.
  • Flag selection bias: reviewers are not the full customer base.

Output

Lead with the three most decision-relevant findings. Then provide:

  1. sample and method;
  2. theme frequency within the sample;
  3. representative evidence;
  4. severity versus prevalence;
  5. product, support, and messaging actions;
  6. confidence and limitations.

Prefer a small evidence matrix over a long narrative or raw review dump.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.