app-store-optimization
Improves visibility and conversion in the App Store and Google Play — metadata, keywords, screenshots, ratings, and the listing experience that turns an impression into an install. Use this to audit or optimize an app listing, plan a launch listing, diagnose poor install conversion, or improve store search visibility.
git clone --depth 1 https://github.com/cbrock84/headcount /tmp/app-store-optimization && cp -r /tmp/app-store-optimization/plugins/demand-generation/skills/app-store-optimization ~/.claude/skills/app-store-optimizationSKILL.md
# App store optimization Two levers, and they are separate problems: being **found**, and being **installed** once found. Diagnose which is failing before changing anything. ## Being found The stores index different fields, so the same metadata does not work on both. - **App name / title** — the single heaviest field. Brand plus the primary descriptive term. Do not spend it on brand alone. - **Subtitle and keyword field** — no repetition across fields; duplicated terms are wasted characters, not reinforcement. - **Long description** — indexed on one store, effectively not on the other. Write it for the store that indexes it and for humans on the store that does not. - **Category** — pick where you can rank, not where you technically belong. Target terms with real intent. Ranking first for a term nobody searches is a vanity result. ## Being installed Most visitors decide from the first screenshot and the rating, without scrolling or reading. - **Screenshots** — the first two carry the decision. Lead with the outcome or the core screen, with a caption stating the benefit. Never lead with an onboarding or login screen. - **Icon** — recognizable at actual size, distinct from category conventions. Test at real scale on a device. - **Rating** — the strongest single conversion factor. Prompt for review after a success moment, never on launch or mid-task. - **Video** — only if it demonstrates something a screenshot cannot. A weak one costs installs. ## Reviews Respond to negative reviews specifically and without defensiveness, naming the fix and its version where there is one. Prospects read the responses as much as the complaints, and a pattern of real answers converts. Watch review text for recurring themes — it is the cheapest continuous product research available. ## Testing Change one element at a time and let it run a full weekly cycle; app traffic is strongly day-of-week seasonal. Attributing a lift to the wrong change is worse than not testing. ## Never - Chase a keyword the app does not deliver on. Installs from a mismatched query become one-star reviews and a worse ranking than you started with. - Change metadata, screenshots, and the icon in the same release. Nothing that moves afterward can be attributed. - Solicit ratings from a user mid-task. The prompt lands where frustration is highest and the score reflects that. - Ignore reviews on the version you just shipped. They are the fastest signal you will get that a release broke something.
Concentrates marketing and sales effort on a named set of accounts rather than on volume — qualifying whether the model fits your economics at all, building the account list and the buying group inside each, tiering effort against account value, coordinating so the account experiences one campaign rather than several, and measuring account progression instead of leads. Use this to decide whether to run an account-based program, build one, or work out why an existing one produces activity and no pipeline.
Designs orchestrator-and-subagent hierarchies for a repository — splitting agents by exclusive write surface, pairing every producer with an independent auditor, and enforcing the split with a script that runs in CI. Use this whenever the user wants to set up, expand, audit, or fix a multi-agent or subagent structure for a codebase; asks how to divide work between agents; wants agent charters, roles, or a surface map written; or is hitting agents that collide on the same files, review their own work, or drift from their remit. Also use when sizing a roster or deciding whether a new agent is justified.
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory exposure, or when deciding whether an AI system is fit for a consequential decision.
Produces executive-level research — market sizing, competitor mapping, trend analysis, and strategic intelligence — grounded in cited sources with the confidence in each claim made explicit. Use this to analyze a market or industry, map competitors, evaluate a market-entry or build-versus-buy decision, produce a research brief, or assemble evidence for a decision. Also use when comparing options that need a structured, evidence-based verdict rather than an opinion.
Optimizes for AI assistants and AI-generated answers — being retrievable, being cited, and being represented accurately when a model answers on your behalf. Use this when traffic is shifting from links to AI answers, when a brand is misrepresented or absent in AI responses, when planning content for retrieval rather than ranking, or when deciding how AI search changes an existing SEO program.
Protects and restores data — backup coverage and scope, retention, immutability against ransomware, and proving restores actually work. Use this to design a backup regime, verify restores, plan retention, protect backups from ransomware, or recover from data loss.
Runs the planning cycle — annual budget, rolling forecast, consolidation of business unit inputs, and the variance analysis that explains actuals against plan. Use this to build or challenge a budget, run a forecast cycle, consolidate departmental submissions, explain why results diverged from plan, or improve forecast accuracy.
Finds the single constraint currently limiting a business's growth and the highest-leverage moves against it, rather than producing a list of everything that could be improved. Use this when growth has stalled or slowed, when deciding where to spend limited time or budget, when revenue rises but profit does not, when prioritizing growth initiatives, or when a business feels busy without compounding.