「aso」の検索結果

When the user wants to A/B test App Store product page elements to improve conversion rate. Also use when the user mentions "A/B test", "product page optimization", "test my screenshots", "test my icon", "conversion rate optimization", "CPP", or "custom product pages". For screenshot design, see screenshot-optimization. For metadata optimization, see metadata-optimization.

日本語の概要は準備中です。原文の説明を表示しています。

When the user wants to design or optimize the funnel that takes web visitors into installing and onboarding the app — including smart app banners, web-to-app deep links, deferred deep links, web onboarding (Stripe-paid web flow before app install), QR codes, "open in app" CTAs, and the trade-off between paying on web vs in-app. Use when the user mentions "web to app", "smart app banner", "Stripe before app", "web paywall before install", "Branch web SDK", "web funnel for app", "AppsFlyer OneLink web", "Universal Links", "App Links", "QR code to app", "open in app", "deferred deep link from web", or "should I sell on web first then push to app". For pure in-app onboarding, see onboarding-optimization. For deep link infra, see attribution-setup.

日本語の概要は準備中です。原文の説明を表示しています。

When the user wants to improve their app's onboarding experience, increase activation rate, reduce Day 1 drop-off, or optimize the first-run flow. Use when the user mentions "onboarding", "first-run", "activation", "tutorial", "day 1 retention", "new user flow", "permission prompts", "sign-up conversion", "onboarding funnel", or "users dropping off early". For overall retention strategy, see retention-optimization. For paywall placement, see monetization-strategy.

日本語の概要は準備中です。原文の説明を表示しています。

When the user wants to monitor competitor apps on an ongoing basis — tracking metadata changes, keyword shifts, screenshot updates, rating trends, or new features. Use when the user mentions "competitor monitoring", "track competitors", "competitor alert", "competitor changed their title", "watch a competitor app", "competitor weekly report", "competitive intelligence", or "what changed in competitor's listing". For a one-time deep competitive analysis, see competitor-analysis. For market-wide chart movements, see market-movers.

日本語の概要は準備中です。原文の説明を表示しています。

When the user wants to set up, interpret, or improve their app analytics and tracking. Also use when the user mentions "analytics", "tracking", "metrics", "KPIs", "App Store Connect analytics", "install tracking", "funnel", "attribution", or "how is my app performing". For A/B testing, see ab-test-store-listing. For retention metrics, see retention-optimization.

日本語の概要は準備中です。原文の説明を表示しています。

When the user wants to optimize their subscription business end-to-end — from trial start through renewal, cancellation, and win-back. Use when the user mentions "subscription lifecycle", "trial conversion", "churn", "cancellation", "win-back", "lapsed subscribers", "dunning", "billing retry", "grace period", "renewal rate", "subscriber LTV", or "resubscribe". For paywall design and pricing strategy, see monetization-strategy. For subscription analytics dashboards, see app-analytics.

日本語の概要は準備中です。原文の説明を表示しています。

Add file-based observability, tracing, and run analysis to LLM/agent experiments — per-turn transcripts, tool calls and results, reasoning traces, token/cost accounting, per-episode artifacts, plus terminal viewers and a run-comparison report. Platform-agnostic: one small adapter covers OpenAI, Gemini/Vertex, Anthropic, LiteLLM, Tinker, vLLM, or any custom inference stack, and the same tooling then works unchanged. Use this skill whenever the user wants observability, tracing, logging, or run tracking for model experiments; asks why an agent failed a task or wants to replay/debug/diff an episode; wants to compare models, arms, or ablations; wants to instrument a new folder, repo, or platform ("add observability here", "do the same for Tinker/Gemini/OpenAI"); wants to import or tie in existing experiment results so old runs show up alongside new ones; or wants eval runner scripts where many models share one protocol. Reach for this even when they only say "log what the model is doing", "I can't tell what happened in this run", or "set up traces for this experiment".

日本語の概要は準備中です。原文の説明を表示しています。

Judges whether material for children under six is developmentally plausible — what is typical at each age, why pre-K is not a thinner kindergarten, and which frameworks apply where Common Core does not. Use this to design or review pre-K material, decide whether a task is reasonable for an age, choose the right framework for a pre-K alignment claim, or push back on material that pushes school-age expectations downward.

日本語の概要は準備中です。原文の説明を表示しています。

Conducts and responds to code review — reviewing a change for correctness, design, and risk, and evaluating review feedback received on your own work. Use this before merging, when asked to review a diff or pull request, when review feedback has arrived and needs acting on, or when feedback seems wrong and needs a reasoned response rather than compliance.

日本語の概要は準備中です。原文の説明を表示しています。

Owns the organization itself: org design, hiring, performance, compensation, development, culture, and employee relations. Use this to design or restructure a team, plan hiring, write a role or leveling definition, handle a performance or compensation question, diagnose why a team is underperforming for non-technical reasons, or assess the people risk in a plan. Also use for onboarding design and retention of key staff.

日本語の概要は準備中です。原文の説明を表示しています。

Designs how an organization is structured — reporting lines, team boundaries, spans and layers, role definition, and workforce planning against the strategy. Use this to structure a new team, restructure an existing one, resolve unclear ownership between teams, plan headcount, or diagnose why a team underperforms for structural rather than individual reasons.

日本語の概要は準備中です。原文の説明を表示しています。

Takes something built and gets it into the market — tiering the launch to match what it actually warrants, sequencing internal readiness before external announcement, preparing sales and support to answer the questions it creates, choosing the date for a reason, and measuring adoption rather than announcement reach. Use this to plan a launch, right-size one that is consuming more than it deserves, work out why a released feature nobody uses was launched loudly, or run the weeks after launch day.

日本語の概要は準備中です。原文の説明を表示しています。

Generate medical referral letters with patient summary, reason for referral, key findings, and requested next steps; use when transferring care or communicating with another clinician or department.

日本語の概要は準備中です。原文の説明を表示しています。

Use when creating radiology educational quizzes, preparing board exam questions, or studying medical imaging cases. Generates interactive quizzes with X-ray, CT, MRI, and ultrasound images for medical education.

日本語の概要は準備中です。原文の説明を表示しています。

Write comprehensive literature reviews for medical imaging AI research. Use when writing survey papers, systematic reviews, or literature analyses on topics like segmentation, detection, classification in CT, MRI, X-ray, ultrasound, or pathology imaging. Triggers on requests f...

日本語の概要は準備中です。原文の説明を表示しています。

Compares multiple study-route options for the same biomedical research question and recommends one primary plan, while explicitly explaining why alternative routes are secondary, premature, weaker, or dependency-heavy. Always use this skill when the user already has a reasonably defined question but is unsure which main study route should anchor the project. Focus on plan comparison, route selection, dependency awareness, and primary-plan justification rather than full protocol drafting.

日本語の概要は準備中です。原文の説明を表示しています。

Evidence-based medical knowledge and research mentor grounded in the Bian Que tradition. Covers clinical reasoning, diagnostic thinking (望闻问切), pharmacology, pathology, differential diagnosis, medical literature appraisal, and the philosophy of early intervention. Trigger whenever users ask about medicine, clinical science, drugs, disease mechanisms, diagnosis, lab interpretation, treatment comparison, or health sciences. Even without explicit research framing, trigger on any topic touching disease, therapeutics, or clinical decision-making. Part of the AIPOCH Medical Research Skill Hub.

日本語の概要は準備中です。原文の説明を表示しています。

Use when evaluating a decision through Charlie Munger-style mental models: inversion, incentives, quality filters, multidisciplinary reasoning, and concentrated judgment.

日本語の概要は準備中です。原文の説明を表示しています。

Provides database transaction concurrency-control background. Use when reasoning about transactions, commits and aborts, serializability, isolation anomalies, conflicts, dependencies, and protocol families such as locking, timestamp ordering, OCC, MVCC, SSI, TicToc, hybrid protocols.

日本語の概要は準備中です。原文の説明を表示しています。

Induce visual patterns from examples — few-shot visual reasoning, grid-based pattern recognition, and ARC-style inductive inference.

日本語の概要は準備中です。原文の説明を表示しています。

LangChain ReAct agent implementation with tool binding for reasoning and action loops

日本語の概要は準備中です。原文の説明を表示しています。

Chain-of-thought and step-by-step reasoning prompts for complex problem solving

日本語の概要は準備中です。原文の説明を表示しています。

SONA self-optimizing neural architecture with ReasoningBank trajectory learning, EWC++ anti-forgetting, and reinforcement learning feedback loops.

日本語の概要は準備中です。原文の説明を表示しています。

Configure which models pstack uses per role and at what reasoning budget. Detects your available models and writes an always-applied rule that overrides the skill defaults. Use for /setup-pstack, "configure pstack models", "pstack budget", or changing pstack's model choices.

日本語の概要は準備中です。原文の説明を表示しています。