# Rice Scoring And Mvp Synthesis > Scores multiple solution directions with the RICE model (Reach, Impact, Confidence, Effort) to select an MVP, and turns the choice into an MVP definition, a one-sentence positioning statement, and three 'why we win' claims. - Skill: `pilot2service/rice-scoring-and-mvp-synthesis` (Agent Skill) - Install (CLI): `npx skillmds@latest add pilot2service/rice-scoring-and-mvp-synthesis` - Raw SKILL.md: https://api.skillmd.com/api/skills/pilot2service/rice-scoring-and-mvp-synthesis/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: Pilot2Service (https://skillmd.com/u/pilot2service) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/pilot2service/rice-scoring-and-mvp-synthesis --- # RICE Scoring and MVP Synthesis ## Purpose Turn a comparison of multiple solution alternatives into an objective, justified MVP choice using the RICE model, and translate that choice directly into a usable strategy: what the MVP exactly does, how it's positioned in one sentence, and why exactly this one wins. This is a bridge skill between solution ideation (many alternatives) and writing the PRD (one chosen direction). ## Based on - The RICE prioritization model (Reach, Impact, Confidence, Effort) — a generally known product-prioritization framework, not the owner's own. - The methodology of an external "AI-first SaaS Product" workshop, applied by the owner to one own case — see `../../references/ai-first-saas-workshop-source.md` and the worked example `../../cases/ai-decision-coach-mvp-case.md`, sections 6–7. **Note:** applied only once so far — not broadly validated. ## Method (draft — to be filled in further) 1. **Score each solution direction on four criteria (1–5):** - **Reach** — how many users this would touch. - **Impact** — how significant the effect is for the user it touches. - **Confidence** — how sure you are that your Reach/Impact/Effort estimates hold up (high = strong evidence, low = a guess). - **Effort (inverted: 5 = easiest/lowest effort, 1 = hardest/highest effort)** — note the inversion: in this model a high Effort score means LOW build cost, so all four criteria sum in the same direction (higher = better MVP candidate). 2. **Calculate the total RICE score** (max 20 across the four criteria, or scale as needed) for each direction and rank them. 3. **Briefly justify the score for each criterion** — don't leave scores unexplained. In particular, tie the Effort estimate concretely to the existing tech stack/data/tools (what already exists vs. what needs to be built from scratch). 4. **Choose the MVP with the highest RICE score**, UNLESS a specific strategic reason favors another (e.g. a higher-Effort direction is the only one that proves a genuine differentiator, not just table-stake value). If you deviate from the highest score, explicitly justify why. 5. **Write the MVP definition (2-3 sentences).** Combine the chosen AI wedge (differentiator need) and the essential table-stake needs into one concise description of what the MVP does and for whom. 6. **Sketch the MVP flow concisely** (5-8 steps): user input → AI synthesis/scoring → decision engine/logic → AI output(s) → next-step plan → (optional) communication support → (optional) path to deeper tools. 7. **Write a one-sentence positioning statement.** Format: "[Product] gives [target customer] [core benefit] through [distinctive mechanism]." Test: could this sentence describe any competitor? If yes, it isn't specific enough yet. 8. **Write 3 "why we win" claims.** Each claim ties one strength (differentiator need, existing data/tool, unique approach) to a concrete competitive advantage — not general claims ("we're better") but justified reasons. 9. Carry the MVP definition, flow, positioning statement, and "why we win" claims into `../ai-buildable-prd-writing/SKILL.md` as the basis for the PRD. ## What this skill does NOT do - Does not make the final MVP choice for you in a fully mechanical way — the RICE score supports the decision, it isn't an automatic rule; a strategic deviation from the highest score is allowed if justified. - Does not assess financial viability or unit economics — only relative prioritization between solution alternatives. See `../../../../business-case-and-analysis/skills/roi-npv-sensitivity-model/SKILL.md` for deeper financial modeling once the MVP is chosen. - Does not replace `../../../opportunity-recognition/skills/opportunity-evaluation- and-judgment/SKILL.md` — this is a narrower, faster choice between 2-3 already-identified solution directions, not a full assessment of an opportunity from scratch. ## Refinement notes This skill has so far been applied to one case (the owner's own case). As you apply it to more businesses, add: - your own rules of thumb for when it's worth deviating from the highest RICE score - concrete examples of positioning statements and "why we win" claims from other cases in the `../../cases/` folder Once this section has been filled in with multiple cases, raise `skills_index.json`'s `maturity` field to `validated` (see `../../../../meta/maturity_levels.md`). ## Continue from here - Preceding skill in this pack: `../ai-differentiator-solution-ideation/SKILL.md` — produces the three alternatives scored here. - Next skill in this pack: `../tiny-core-identification-and-feature-freeze/SKILL.md` — once a direction is chosen here, isolates the one interaction that is its actual reason to exist and blocks feature creep before the PRD is written. - Next skill in this pack: `../ai-buildable-prd-writing/SKILL.md` — writes the PRD for the chosen MVP. - Related skill in this pack, if the chosen MVP is a conversational/ agentic product: `../ai-native-conversational-os-design/SKILL.md`. - Worked example: `../../cases/ai-decision-coach-mvp-case.md`, sections 6–7. - The pack's shared guardrails: `../../CLAUDE.md` ## References - `../../references/ai-first-saas-workshop-source.md` — source information - `../../cases/ai-decision-coach-mvp-case.md` — worked example - `../../CLAUDE.md` — the pack's shared guardrails