「fea」の検索結果

Running closed and open betas that produce real signal. Beta participant selection, structured feedback collection, beta-to-GA decision criteria, and the difference between soft-launch (no structure, no signal), kitchen-sink (everyone in, no actionable feedback), and structured beta (calibrated cohort, intentional feedback loops, clear graduation criteria). Triggers on beta program, alpha test, beta cohort, beta participant, beta feedback, beta to GA decision, design partner, early access program, closed beta, open beta, RC release. Also triggers when a feature is approaching launch and the team needs structured pre-GA validation, when prior betas produced noise rather than signal, or when the team has soft-launched before but wants more structured feedback this time.

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。

A platform decision framework for experimentation. When to use Statsig vs PostHog vs GrowthBook vs Optimizely vs Amplitude vs Eppo vs Kameleoon. How to migrate between them. How to coordinate when multi-platform is genuinely warranted. The decisions that compound for years and the ones you can defer. Triggers on which experimentation platform, choose Statsig vs PostHog, evaluate experimentation tools, switch experimentation platform, migrate from Optimizely, consolidate experimentation tools, multi-platform experimentation, experimentation platform decision, ab test platform selection, feature flag platform vs experiment platform, warehouse-native experiments, vendor lock-in experimentation. Also triggers when a team is asking about cost, governance, or migration cost across experimentation tools, or when an evaluation is starting.

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。

Playbook for designing SaaS and startup products that convert, retain, and monetize — landing pages & CRO, checkout & forms, onboarding/activation, churn reduction, pricing psychology, dashboards, behavioral-science tactics, feature discipline, positioning/ICP, go-to-market, and AI-era differentiation. Use when advising on product design, UX, conversion, landing pages, checkout, onboarding, trials, pricing, retention/churn, growth, or positioning for a startup or SaaS. Distilled from product designer Richard (@richardrx). Not for use on gambling, betting, or casino products.

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。

Compare feasible approaches to a customer problem and recommend a path with costs, constraints and evidence. Use for an architecture or delivery decision, not implementation.

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。

Assess a new customer request against agreed scope, trade-offs and ownership. Use when an engagement expands or a custom feature needs a commitment decision.

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。

Compare competing customer use cases by value, feasibility and evidence. Use when several problems compete for delivery capacity.

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。

Evaluate code (especially LLM-generated) for structural simplicity using Rich Hickey's "Simple Made Easy" framework. Use this skill whenever reviewing a PR, diff, or code snippet for accidental complexity — particularly when the code was generated by an AI coding assistant and line-by-line review isn't feasible. Also use when the user asks about complecting, simplicity vs. easiness, structural coupling, or concept deduplication. Trigger on phrases like "is this simple", "does this complect", "review for complexity", "structural analysis", or any reference to Hickey, Simple Made Easy, or grey-box review.

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。

Guides collaborative requirements discovery before implementation. Creates task directory, seeds PRD, asks high-value questions one at a time, researches technical choices, and converges on MVP scope. Use when requirements are unclear, there are multiple valid approaches, or the user describes a new feature or complex task.

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。

Productboard expert for workspace setup, Insight-to-Feature triage, Driver scoring, Releases, Roadmap views, and REST API automation. Use for Productboard administration, prioritization workflows, and programmatic API operations.

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 persuasive marketing copy for homepages, landing, pricing, feature, and product pages using headline formulas, CTA frameworks, and objection handling. Use when writing page copy, improving headlines, crafting CTAs, or conversion copy.

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。

Product manager toolkit covering RICE prioritization, customer interview analysis, PRDs, and discovery frameworks. Use for feature prioritization, user research synthesis, requirement documentation, or product 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。

Product analytics for instrumenting products, defining metrics, and building retention funnels. Use when designing a metric tree, instrumenting a feature, auditing instrumentation, defining a North Star, or building an analytics roadmap.

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 and score candidate brand, product, and domain names for memorability and pronounceability. Use when naming a new product, company, or feature, brainstorming brand names, or finding an available .com.

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。

Run development from an executable specification with traceable requirement IDs and merge-time coverage gates. Use when starting a greenfield feature, reviving a stale spec, or gating merges on requirement coverage.

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ML engineering skill for productionizing models, building MLOps pipelines, and integrating LLMs. Covers model deployment, feature stores, drift monitoring, RAG systems, and cost 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。

Design relational schemas from requirements with normalization, migrations, ERDs, RLS policies, and indexes for PostgreSQL, MySQL, and SQLite. Use when designing new features, reviewing schemas, or adding multi-tenancy.

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。

Data science across machine learning, statistical modeling, and experimentation. Use when selecting ML algorithms, engineering features, designing A/B tests, evaluating model performance, or building predictive pipelines.

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。

In-app paywall and upgrade screen optimization covering feature gate design, usage limit UX, trial expiration flows, upgrade trigger timing, save offer strategy, and ethical monetization patterns.

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。

Analyzes RFP responses for coverage gaps, builds competitive feature matrices, and plans proof-of-concept engagements for pre-sales engineering

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。

Systematic competitor analysis covering product teardowns, 12-dimension scoring rubric, feature comparison matrices, SWOT analysis, pricing model deconstruction, UX audits, and strategic action plans with stakeholder presentation templates.

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。

Molecular data processing, featurization, and cheminformatics workflows

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。

Systematically QA test a web application with browser-based bug fixing. Four modes: diff-aware (automatic on feature branches), full (systematic exploration), quick (30-second smoke test), regression (compare against baseline). Atomic bug-fix workflow with regression testing. Health scoring across 8 categories.

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 production-grade ML pipeline infrastructure with experiment tracking, Kubeflow/Airflow DAGs, feature store schemas, model registries, and automated retraining workflows.

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。

Writes, debugs, and refactors JavaScript code using modern ES2023+ features, async/await patterns, ESM module systems, and Node.js APIs.

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。