Design pricing strategy and packaging — tiers, value metrics, enterprise pricing, freemium design, and pricing page copy. Use when asked to "design our pricing", "should we change our price", "how do we package the product", or "what should we charge enterprise".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
B2B proposal generator — takes deal context (ICP, pain, pricing tier, timeline) and produces a complete proposal document with executive summary, problem statement, solution, pricing table, implementation timeline, ROI case, and next steps. Use when asked to "write a proposal", "draft our deck for this deal", "build a proposal for this customer", or "generate a proposal".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Revenue reconnaissance — audit current sales pipeline, deal patterns, ICP definition, and revenue motion to understand what's working and where the constraint is. Use when asked to "audit our sales", "where is revenue stuck", "what's our pipeline state", "before designing a playbook".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when asked to structure a landing page, design page layout for conversion, or plan landing page information architecture. Examples: "landing page structure for SaaS", "conversion-optimized layout"
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when asked about UX patterns, interaction best practices, form design, navigation patterns, or loading states. Examples: "best practice for form validation", "navigation pattern for dashboard", "loading state UX"
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Hi-fi interactive HTML prototype — single-file, double-click-to-open, Playwright-verified. Use when asked to "make it clickable", "build a prototype", "hi-fi mockup", "interactive demo", "iOS prototype", "app mockup", "ui demo", or "prototype this". Goes beyond wireframes into visually finished, state-driven HTML with device frames and real images. Not production code — a design artifact for testing flows and pitching ideas.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
UI and UX reconnaissance — scan existing frontend routes, components, navigation, and flows to understand the current UX state before designing. Use when asked to "understand the current UI", "what UX patterns exist", "map the navigation", "what screens exist", or before starting any flow or wireframe work.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Usability review — evaluate an existing flow or UI against usability heuristics, flag friction points, and recommend fixes. Use when asked to "review the UX", "usability audit", "what's wrong with this flow", "UX feedback", "critique this design", or "why are users dropping off here".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design a drift monitoring system for a production ML model. Use when asked to "monitor this model in production", "detect data drift", or "set up ML monitoring".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit existing ML monitoring — find gaps in drift coverage and missing alerts. Use when asked to "audit our ML monitoring", "are our models monitored", or "find drift coverage gaps".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Feedback synthesis — cluster support tickets, NPS verbatims, app store reviews, and churn surveys by theme, separate signal from noise, and produce an actionable insight report. Use when asked to "synthesize this feedback", "analyze support tickets", "what are users complaining about", "NPS analysis", "churn feedback synthesis", or "what's the feedback telling us".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Run a user interview — produce an interview guide and synthesize the output into an actionable insight report. Use when asked to "run a user interview", "synthesize these interview notes", "what do users actually want", "build a persona from this feedback", "find the JTBD in these transcripts", or "analyze this interview data".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Jobs-to-Be-Done analysis — given a product, user descriptions, transcripts, or tickets, produce a JTBD job map with switching forces analysis and opportunity ranking. Use when asked to "find the JTBD", "what jobs are users hiring us for", "job mapping", "what are users really trying to do", "JTBD framework", or "why are users switching".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
User segmentation and persona creation from mixed data sources — analytics, CRM, support tickets, reviews, or any combination. Use when asked to "build personas", "who are our users", "segment our users", "create user profiles", "define user archetypes", or "who is the target user".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit embedding infrastructure — model drift, index freshness, query latency, coverage gaps. Use when asked to "audit our embeddings", "is our index stale", or "find embedding coverage gaps".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Optimize similarity search — ANN index tuning, hybrid search, reranking, query expansion. Use when asked to "improve similarity search", "tune the ANN index", or "add hybrid search".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze A/B test results — statistical significance, practical significance, and segmentation. Use when asked to "analyze our A/B test", "is this result significant", or "read these experiment results".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit existing experimentation infrastructure and past experiments for methodology issues. Use when asked to "audit our experiments", "is our experimentation sound", or "review past test methodology".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Analyze LLM eval results — score breakdowns by category, regression detection vs baseline, failure clustering. Use when asked to "analyze our eval results", "did the model regress", or "cluster the eval failures".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design an LLM eval — task schema, scoring rubric, dataset composition, and pass/fail thresholds. Use when asked to "design an LLM eval", "write a scoring rubric", or "how do we measure this model".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit existing eval coverage — gaps, metric validity, benchmark leakage, dataset freshness. Use when asked "what eval coverage do we have", "audit our evals", or "check for benchmark leakage".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and implement a feature engineering pipeline for a ML problem. Use when asked to "engineer features for this model", "what features should we build", or "design feature transformations".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design embedding pipelines — model selection, batching, normalization, index refresh strategy. Use when asked to "design an embedding pipeline", "which embedding model should we use", or "how should we batch embeddings".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Audit cloud spend — identify waste, rightsizing opportunities, and reservation gaps. Use when asked "our cloud bill is too high", "find cloud waste", or "where can we rightsize".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.