shinpr/claude-code-discover · 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 Jun 16, 2026

Installation

$ npx skills add shinpr/claude-code-discover --skill product-principles

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Repository health

Stars 6
License LICENSE
Default branch main
Open issues 0
Status Archived

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents claude-code

Package contents

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  • skill md SKILL.md 7,323 B
  • docs SUMMARY.md 279 B

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  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: From hypothesis to validation to user story to PRD to implementation to test — maintain traceability across the entire chain
  3. Feedback Accumulation: Preserve conclusions and evidence that can change a current or plausible future decision, including invalidated hypotheses
  4. Validated Enough: Use cost x risk x reversibility to determine the sufficient confidence threshold
  5. Artifacts Over Notes: Intermediate analysis (market research, competitive landscape, brainstorming) is conducted within the conversation and synthesized into the designated artifact. Each recipe defines its output locations

Opportunity Solution Tree (OST) Hierarchy

All product work follows this hierarchy:

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 all 4 Risks validated

4 Risks (Authoritative Definition)

A user story is the minimum unit of value. All four risks must be sufficiently validated:

  • 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 Calibration Guide Typical Evidence
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 each user story's current confidence and remaining risks. The table calibrates judgment rather than imposing a numeric gate; the required evidence follows cost, risk, reversibility, and the decision at stake.

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 Always (via this skill)
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 validated patterns supported by independent evidence across the contexts in which the principle claims to hold. Treat them as established only within those recorded conditions.

Distillation criteria (enforced by knowledge-distiller):

  • Independent evidence: Promotion requires more than one retelling of the same observation; evidence independence and quality matter more than a fixed count
  • Context coverage: Evidence must cover the segments or conditions named by the proposed learning; additional segment research requires a current decision it can change
  • Contradiction handling: Conflicting evidence remains recorded with its conditions
  • Freshness tags: Tier 1 learnings record last-validated; re-check when a changed environment or current decision makes staleness consequential, not merely because a timer elapsed

State Design (Authoritative Definition)

For each user-facing interaction, specify the states whose occurrence or recovery changes observable behavior or verification. Use these standard states as a coverage prompt, not a required five-row output:

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 specify behavior for each applicable state. Record not_applicable only when omitting a plausibly relevant standard state would leave the downstream behavior ambiguous. Prototypes implement the states needed to distinguish the hypothesis's success, failure, or inconclusive criteria.

Key Principles for Daily Decisions

  • Solution-Framing Check: Seek meaningfully different solution classes to test whether an Opportunity is solution-shaped. Stop when further alternatives cannot change that framing judgment. See references/opportunity-template.md for Opportunity file structure
  • Outcome Connection: Preserve the connection to business outcomes while using NSM to balance pure metric-chasing 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 a PRD or blueprint, use references/mvp-definition.md for the inclusion boundary, explicit exclusions, observable proof, and scope reduction. Use an optional ranking aid only when direct boundary analysis leaves credible candidates tied