Mom Test questioning toolkit, JTBD analysis, interview conduct, assumption testing framework, and hypothesis design
npx skills add https://github.com/nWave-ai/nWave --skill nw-interviewing-techniques
Understand if problem is real and worth solving.
Map the job-to-be-done and desired outcomes.
Challenge beliefs and uncover truth.
Distinguish interest from commitment.
Understand impact and urgency.
Do: Ask about past specifics | Open, non-directive questions | Seek commitment not praise | Keep informal | 80% listening | Talk about their life first
Avoid: Future behavior questions | Leading questions | Accepting compliments as validation | Talking >20% | Mentioning idea before understanding problem | Formal settings
Challenge when: belief without evidence | prediction about future | negative feedback dismissed | skipping to solution | single data point relied on
Tone: curious and supportive, not confrontational -- goal is truth-seeking.
| Category | Core Question |
|----------|--------------|
| Value | Will customers want this? |
| Usability | Can customers use this? |
| Feasibility | Can we build this? |
| Viability | Does this work for our business? |
| Factor | Weight | Low (1) | Medium (2) | High (3) |
|--------|--------|---------|------------|----------|
| Impact if wrong | 3 | Minor adjustment | Significant rework | Solution fails |
| Uncertainty | 2 | Have data | Mixed signals | Speculation |
| Ease of testing | 1 | Days, low cost | Weeks, moderate | Months, high cost |
Risk Score = (Impact x 3) + (Uncertainty x 2) + (Ease x 1)
| Priority | Score | Action |
|----------|-------|--------|
| Test first | > 12 | Immediate |
| Test soon | 8-12 | Schedule |
| Test later | < 8 | Backlog |
We believe [doing X] for [user type] will achieve [outcome].
We will know this is TRUE when we see [measurable signal].
We will know this is FALSE when we see [counter-signal or absence of signal].
| Category | Methods |
|----------|---------|
| Value | Landing page, Fake door, Mom Test interviews |
| Usability | Prototype testing, 5-second tests, Task completion |
| Feasibility | Spike, Technical prototype, Expert review |
| Viability | Lean Canvas review, Stakeholder interviews |
| Result | Criteria | Action |
|--------|----------|--------|
| Proven | >80% meet success criteria | Proceed |
| Disproven | <20% meet criteria | Pivot or kill |
| Inconclusive | 20-80% | Increase sample, try different method, segment |
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take nwave-ai/nw-interviewing-techniques from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.