shinpr/nautilus · Archived

product-principles

Defines 4 Risks confidence thresholds, OST hierarchy levels, Knowledge Pyramid tiers, and state design requirements. Use when evaluating user stories, setting confidence scores, referencing OST levels, scoping MVP, or determining validation sufficiency.

First seen Jul 22, 2026

Installation

$ npx skills add shinpr/nautilus --skill product-principles

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Repository health

Stars 3
License LICENSE
Default branch main
Open issues 0
Status Archived

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 6,691 B
  • docs SUMMARY.md 279 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 1 installs

SKILL.md

Product Management Principles

Core Philosophy

  1. Hypothesis Until Proven: Every assumption is a hypothesis until validated with evidence. Treat unvalidated ideas as hypotheses, not facts
  2. Value Traceability: Preserve the links needed to connect a decision or implementation back to its supporting outcome and evidence
  3. Proportionate Validation: Use cost x risk x reversibility to determine sufficient confidence
  4. Proportionate Artifacts: Keep durable decisions in repo artifacts when a downstream consumer will reuse them; no-change and reuse are valid outcomes

Opportunity Solution Tree (OST) Hierarchy

Use this hierarchy to distinguish outcomes, opportunities, solutions, assumptions, and experiments when those distinctions affect the current decision:

Outcome
  ├── Product Outcome (team-controllable product goals)
  │     NSM connects Product Outcome ↔ Business Outcome
  └── Business Outcome (business results Product Outcome contributes to)

Product Outcome
  └── Opportunity (user problems, needs, desires)
        └── Solution (approaches to address the opportunity = feature candidates)
              └── Assumption (premises underlying the solution = hypotheses)
                    └── Experiment (methods to validate the hypothesis)

Level Definitions

Level Granularity Artifact Description
Business Outcome Largest docs/product/vision.md Business results the product contributes to
Product Outcome Large docs/product/vision.md Team-controllable product goals
Opportunity Large docs/discovery/opportunities/ User problems, needs, desires
Solution Medium PRD (docs/prd/) Feature candidates addressing an Opportunity
Assumption Small docs/discovery/hypotheses/ Premises underlying a Solution
User Story Smallest Within PRD Minimum unit of value with sufficient evidence for its material risks

4 Risks (Authoritative Definition)

A user story is the minimum unit of value. Consider all four risks and gather enough evidence for the dimensions that can change the delivery decision:

  • Value — Will users use/buy this? Does it solve their problem?
  • Usability — Can users figure out how to use it? Does the UX work?
  • Feasibility — Can we build it technically? Is the effort realistic?
  • Viability — Does it work as a business? Can we explain why we're building it?

Confidence Meter (Authoritative Definition)

Track confidence per risk dimension (0-10):

Score Meaning Typical Evidence
0-2 Gut feeling / no evidence Assumption only
3-4 Structured evaluation Expert review, competitive analysis, scoring
5-7 Data-backed Analytics, surveys, interview patterns
8-10 Tested and confirmed Prototype validation, A/B test, beta results

Threshold by Cost x Risk x Reversibility

Condition Confidence Needed Evidence Level
Low-cost, reversible (feature flag, gradual rollout) 3-4 Structured evaluation
Medium cost 5-7 Data
High-cost, irreversible (platform change, pricing change) 8+ Test results

PRDs show current confidence and remaining risks at the smallest scope that changes a delivery decision. Keep shared evidence and decisions at feature scope.

Knowledge Pyramid (Authoritative Definition)

Knowledge is organized in three tiers to manage context as hypotheses accumulate:

Tier Scope Location Loading
Tier 1 Distilled product principles docs/product/learnings.md When durable learning can change the current decision
Tier 2 Opportunity-level learnings Each Opportunity file's "Tier 2 Learnings" section When working on that Opportunity
Tier 3 Individual hypothesis files docs/discovery/hypotheses/ On demand

Tier 1 learnings are corroborated patterns that remain useful across the contexts where they will guide decisions.

Distillation criteria (enforced by knowledge-distiller):

  • Independent corroboration: Independent evidence supports the learning across its intended decision scope
  • Context coverage: Supporting contexts match the scope where the learning will guide decisions
  • Contradiction handling: Conflicting evidence is retained with the conditions that explain its decision effect
  • Freshness: Revalidate when source age or changed conditions can alter a current decision

State Design (Authoritative Definition)

For each user-facing interaction, define the states that can occur and affect its acceptance. The categories below are a reference set, not a required checklist:

State Description
Loading Data is being fetched/processed — show progress indicator
Empty No data exists yet — guide user to first action
Error Something went wrong — explain what happened, offer recovery
Partial Some data available, some not — show available, indicate missing
Success Normal state with data — primary design focus

PRDs and prototypes cover the states needed to define or validate the current interaction. Record an exclusion only when its absence could obscure the acceptance or validation decision.

Key Principles for Daily Decisions

  • 3+ Solutions Test: Use the ability to identify meaningfully different Solutions as a diagnostic for whether an Opportunity is framed too narrowly. A failed diagnostic is a framing signal, not an obligation to manufacture alternatives. See references/opportunity-template.md for Opportunity file structure
  • Business Outcome Traceability: Preserve the connection to business outcomes while using NSM to balance metric pressure
  • Design is a Perspective, Not a Phase: Design thinking applies across all processes — discovery, validation, definition, delivery, and reflection
  • Cycle, Not Phases: Discovery → Validation → Definition → Delivery → Reflection is a continuous cycle. Start from anywhere
  • MVP Scoping: When transitioning validated hypotheses to PRD, use references/mvp-definition.md for prioritization (MoSCoW/RICE) and scope reduction techniques