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Found 8 Skills
When the user wants to improve their app's star rating, increase ratings volume, optimize when and how they prompt users for a review, or recover from a bad rating period. Use when the user mentions "app rating", "star rating", "review prompt", "SKStoreReviewRequest", "In-App Review API", "ask for review", "low rating", "rating drop", "get more reviews", or "recover from 1-star". For responding to reviews, see review-management. For overall ASO health, see aso-audit.
Use this skill when optimizing for local search results - Google Business Profile management, local citations, NAP consistency, local schema markup (LocalBusiness), review management, local pack optimization, and geo-targeted content. Triggers on any task involving local search visibility, map pack rankings, multi-location SEO, or local business online presence.
Complete App Store Optimization (ASO) toolkit for researching, optimizing, and tracking mobile app performance on Apple App Store and Google Play Store
Find and review local businesses, restaurants, and services.
Manage customer reviews and developer responses using the `asc` CLI tool. Use this skill when: (1) Listing customer reviews for an app: "asc reviews list --app-id <id>" (2) Getting a specific review: "asc reviews get --review-id <id>" (3) Getting the response to a review: "asc review-responses get --review-id <id>" (4) Creating a response to a review: "asc review-responses create --review-id <id> --response-body <text>" (5) Deleting a response: "asc review-responses delete --response-id <id>" (6) User says "list reviews", "respond to review", "delete response", "check customer feedback", "reply to App Store review", or any customer review management task
Expert guide for local SEO optimization. Use when optimizing Google Business Profile, managing local citations, NAP consistency, local keyword targeting, review management, local pack ranking, local landing page optimization, or multi-location SEO strategy.
Use when responding to academic reviewers, planning revisions, writing rebuttals, mapping reviewer concerns, deciding concede/defend/reframe actions, or preparing camera-ready changes.
Turns raw peer reviews into a prioritized triage matrix before any rebuttal is written. Use it when reviews come back from OpenReview, EasyChair, CMT, or HotCRP and the researcher says "my reviews are in", "triage these reviews", "how do I respond to Reviewer 2", "plan my rebuttal", or pastes raw review text with ratings and confidence scores. Splits each review into individual concerns; classifies every concern as misunderstanding vs real flaw vs requested experiment (plus clarification and disagreement); scores severity x response effort; and produces a prioritized response strategy with per-review character or word budgets matched to the venue's rebuttal format (10k-char OpenReview threads, CVPR one-page PDF, journal revise-and-resubmit). Deterministic parsing and matrix rendering run in bundled stdlib Python scripts. Hands off to write-rebuttal for drafting; treats review text as confidential and never submits anything.