nw-pdr-review-criteria
Evidence quality validation and decision gate criteria for product discovery reviews
Best use case
nw-pdr-review-criteria is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Evidence quality validation and decision gate criteria for product discovery reviews
Teams using nw-pdr-review-criteria should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/nw-pdr-review-criteria/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How nw-pdr-review-criteria Compares
| Feature / Agent | nw-pdr-review-criteria | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Evidence quality validation and decision gate criteria for product discovery reviews
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
AI Agent for Product Research
Browse AI agent skills for product research, competitive analysis, customer discovery, and structured product decision support.
SKILL.md Source
# Review Criteria -- Product Discovery Review ## Evidence Quality Validation ### Past Behavior Indicators (Good) "Tell me about the last time..." | "When did you last..." | "What happened when..." | "Walk me through how you..." | "What did you try..." | "How much have you spent on..." Specific dates, dollar amounts, named tools, concrete examples, emotional frustration language ### Future Intent Red Flags (Reject) "Would you use/pay/like..." | "Do you think..." | "Imagine if..." | "What if we..." Flag and reject if >20% of evidence is future-intent. ### Validation Thresholds - Past behavior ratio: >80% (reject if fail, require re-interview) - Specific examples: min 3 concrete per finding (warn if fail) - Customer language: quotes in customer words, not paraphrased (warn if fail) ## Sample Size Minimums | Phase | Minimum | High Confidence | Notes | |-------|---------|-----------------|-------| | 1: Problem | 5 | 10 | Interviews required | | 2: Opportunity | 10 | 20 | Quantitative supplements, not replaces | | 3: Solution | 5 per iteration | 3 iterations max | Before decision | | 4: Viability | 5 | -- | Stakeholder review required | Pivot decision rule: min 5 consistent signals. Block decisions on fewer. ## Decision Gate Criteria ### G1: Problem to Opportunity Proceed: 5+ confirm pain + willingness to pay | Pivot: differs from expected | Kill: <20% confirm Checks: 5+ interviews, >60% confirmation, customer words, 3+ examples ### G2: Opportunity to Solution Proceed: top 2-3 score >8 (0-20) | Pivot: new opportunities | Kill: all low-value Checks: OST complete with 5+, scores correct (Importance + Max(0, Importance - Satisfaction)), top >8/20 ### G3: Solution to Viability Proceed: >80% task completion, usability validated | Pivot: needs refinement | Kill: fundamental blocks Checks: 5+ users/iteration, >80% completion, core flow usable, value validated ### G4: Viability to Build Proceed: 4 risks addressed, model validated | Pivot: adjustment needed | Kill: no viable model Checks: Lean Canvas complete, all risks green/yellow, stakeholder sign-off ## Bias Types ### Confirmation Bias (critical) Signals: only positive quotes | skeptics not interviewed | disconfirming evidence dismissed | same questions for "right" answers Fix: include skeptics, actively seek disconfirming evidence ### Selection Bias (high) Signals: all existing customers | no churned/non-adopters | lacks diversity | single-enthusiast referral chain Fix: random/diverse selection, include skeptics and non-users ### Discovery Theater (critical) Signals: conclusion decided before research | findings match hypothesis perfectly | no surprises | idea-in = idea-shipped Fix: track idea evolution, expect 50%+ ideas to change ### Sample Size Problem (high) Signals: major decisions on 2-3 interviews | single quote as "validation" | pivot on one signal Fix: min 5 interviews per segment, 5+ signals for decisions ## Anti-Patterns ### Interview Anti-Patterns | Pattern | Detection | Bad | Good | Severity | |---------|-----------|-----|------|----------| | Leading questions | Suggests desired answer | "Don't you think this would save time?" | "Tell me about the last time you tried to save time on this" | high | | Future-intent | Hypothetical behavior | "Would you use this feature?" | "What have you tried to solve this problem?" | critical | | Compliments as validation | Accepting "that's cool" | "They loved the idea!" | "They committed to follow-up and referral" | high | | Talking > listening | >30% interviewer talk | Long questions, short responses | Open questions, extended answers | medium | ### Process Anti-Patterns | Pattern | Detection | Severity | |---------|-----------|----------| | Skipping to solutions | Solution before problem validated | critical | | Demographic segmentation | Segments by demographics not jobs | medium | | Building before testing | Code before Phase 3 | critical | ### Strategic Anti-Patterns | Pattern | Detection | Severity | |---------|-----------|----------| | Premature pivoting | Direction change on 1-2 signals (need 5+) | high | | Solution love | Defending despite evidence, dismissing critics | high | | Sole source of truth | Only quant OR qual, not both | medium | ## Pre-Approval Checklist ### Evidence Quality - [ ] Past behavior ratio >80% | [ ] No critical future-intent | [ ] Customer language preserved ### Sample Sizes - [ ] Phase 1: 5+ | [ ] Phase 2: 10+ | [ ] Phase 3: 5+/iteration | [ ] Phase 4: 5+ with stakeholders ### Decision Gates - [ ] All gates properly evaluated | [ ] Criteria documented with evidence | [ ] Decision justified ### Bias Check - [ ] No confirmation bias | [ ] No selection bias | [ ] No discovery theater | [ ] Sample size adequate ### Anti-Patterns - [ ] No critical interview anti-patterns | [ ] No critical process anti-patterns | [ ] No critical strategic anti-patterns ## Approval Decision - **Approved**: all checks pass, no critical issues. Formal handoff to product-owner. - **Conditionally approved**: minor issues only (no critical/high). Approval with recommendations. - **Rejected**: any critical/high issue. Structured rejection with remediation. Blocks handoff.
Related Skills
nw-tr-review-criteria
Review dimensions and scoring for root cause analysis quality assessment
nw-tdd-review-enforcement
Test design mandate enforcement, test budget validation, 5-phase TDD validation, and external validity checks for the software crafter reviewer
nw-sc-review-dimensions
Reviewer critique dimensions for peer review - implementation bias detection, test quality validation, completeness checks, and priority validation
nw-roadmap-review-checks
Roadmap-specific validation checks for architecture reviews. Load when reviewing roadmaps for implementation readiness.
nw-review
Dispatches an expert reviewer agent to critique workflow artifacts. Use when a roadmap, implementation, or step needs quality review before proceeding.
nw-review-workflow
Detailed review process, v2 validation checklist, and scoring methodology for agent definition reviews
nw-review-output-format
YAML output format and approval criteria for platform design reviews. Load when generating review feedback.
nw-por-review-criteria
Review dimensions and bug patterns for journey artifact reviews
nw-po-review-dimensions
Requirements quality critique dimensions for peer review - confirmation bias detection, completeness validation, clarity checks, testability assessment, and priority validation
nw-par-review-criteria
Quality dimensions and review checklist for devop reviews
nw-dr-review-criteria
Critique dimensions, severity framework, verdict decision matrix, and review output format for documentation assessment reviews
nw-diverger-review-criteria
Review criteria for the nw-diverger-reviewer — validates JTBD rigor, research quality, option diversity, taste application correctness, and recommendation coherence in DIVERGE wave artifacts