Prioritize assumptions using an Impact × Risk matrix and suggest experiments for each. Use when triaging a list of assumptions, deciding what to test first, or applying the assumption prioritization canvas.
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Triage assumptions using an Impact × Risk matrix and suggest targeted experiments.
You are helping prioritize assumptions for $ARGUMENTS.
If the user provides files with assumptions or research data, read them first.
ICE works well for assumption prioritization: Impact (Opportunity Score × # Customers) × Confidence (1–10) × Ease (1–10). Opportunity Score = Importance × (1 − Satisfaction), normalized to 0–1 (Dan Olsen). RICE splits Impact into Reach × Impact separately: (R × I × C) / E. See the prioritization-frameworks skill for full formulas and templates.
The user will provide a list of assumptions to prioritize. Apply the following framework:
For each assumption, evaluate two dimensions:
Categorize each assumption using the Impact × Risk matrix:
For each assumption requiring testing, suggest an experiment that:
Present results as a prioritized matrix or table.
Think step by step. Save as markdown if the output is substantial.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations. Use when evaluating experiment results, checking if a test reached significance, interpreting split test data, or deciding whether to ship a variant.
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。
Analyze and prioritize a list of feature requests by theme, strategic alignment, impact, effort, and risk. Use when reviewing customer feature requests, triaging a backlog, or making prioritization decisions.
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 an Ansoff Matrix analysis mapping growth strategies across market penetration, market development, product development, and diversification. Use when considering growth options, planning market expansion, or evaluating strategic growth paths.
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。
Identify the first beachhead market segment for a product launch. Evaluates segments against burning pain, willingness to pay, winnable market share, and referral potential. Use when choosing a first market, targeting an initial customer segment, or planning market entry 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。
Design experiments to test assumptions for an existing product — prototypes, A/B tests, spikes, and other low-effort validation methods. Use when validating assumptions, testing feature ideas cheaply, or planning product experiments.
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 lean startup experiments (pretotypes) for a new product. Creates XYZ hypotheses and suggests low-effort validation methods like landing pages, explainer videos, and pre-orders. Use when validating a new product idea, creating pretotypes, or testing market demand.
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。