Atlas / Skills / nwave-ai / Nw Discovery Methodology

Nw Discovery MethodologySAFE

skills/nwave-ai/nw-discovery-methodology

AI agents that guide you from idea to working code, with you in control at every step.

Verdict
SAFE
Grade
B
Trust score
89 /100
Version
—
Hosts
—
License
MIT
Stars
617
01

Overview

AI agents that guide you from idea to working code, with you in control at every step.

Read from source at commit 1a864ec81cdeOBSERVED · 2026-10-14
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02

What it tells the agent

The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.

---
name: nw-discovery-methodology
description: Question-first approach to understanding user journeys. Load when starting a new journey design or when the discovery phase needs deepening.
user-invocable: false
disable-model-invocation: true
---

# Discovery Methodology

Discover journeys through deep questioning before any sketching. The sketch is proof of understanding, not the starting point.

## Session Flow

### Phase 1: Goal Discovery (First 5-10 minutes)
Focus: What is the user trying to accomplish?

Questions:
- "What's the ultimate goal you're trying to achieve?"
- "What triggers this journey? When does a user start this?"
- "How will you know when you've succeeded?"
- "What's the happy path in your mind?"

#### Draft Sketch (after first 3 answers)

After collecting goal, trigger, and success criteria, output a rough draft sketch showing what the journey might look like. Mark unknowns with `???`. Purpose: make value visible immediately so the user sees progress, not just questions.

```
[Trigger: ???] → [Step 1: ???] → [Step 2: ???] → [Goal: {stated goal}]
  Feels: ???       Sees: ???       Sees: ???       Feels: {success criteria}
  Artifacts: ???   Artifacts: ???  Artifacts: ???
```

This is a working hypothesis, not a commitment. Update it after each phase as understanding deepens. The sketch gives the user something concrete to react to — "no, step 2 happens before step 1" is more productive than abstract discussion.

### Phase 2: Mental Model (10-20 minutes)
Focus: What does the user EXPECT to see?

Questions:
- "Walk me through step by step -- what do you type, what do you see?"
- "At this step, what information appears on screen?"
- "What would you need to see to feel confident?"
- "Where do you think this data comes from?"
- "What's your mental model of how this works?"

### Phase 3: Emotional Journey (5-10 minutes)
Focus: How should the user FEEL?

Questions:
- "How should you feel at the start? Anxious? Curious? Confident?"
- "What's the emotional arc -- where's the peak tension?"
- "How should you feel when it's done?"
- "Where might you feel frustrated or lost?"
- "What would make this feel satisfying vs frustrating?"

### Phase 4: Shared Artifacts (5-10 minutes)
Focus: What data is shared across steps?

Questions:
- "What information appears in multiple places?"
- "Where does the version number come from? Where is it shown?"
- "If this path changes, what else breaks?"
- "Who owns this piece of data?"
- "What paths or URLs are reused across steps?"

### Phase 5: Error Paths (5 minutes)
Focus: What could go wrong?

Questions:
- "What's the most likely failure?"
- "What should the user see when it fails?"
- "How does the user recover?"
- "What would a helpful error message look like?"

### Phase 6: Integration Points
Focus: How do steps connect?

Questions:
- "What did the previous step produce that this step needs?"
- "What does this step produce that the next step needs?"
- "Are there any hidden dependencies between steps?"
- "What external systems or files does this touch?"

### Phase 7: CLI UX Specifics
Focus: What commands and output does the user expect?

Questions:
- "What command would you naturally type for this?"
- "What flags or options do you expect?"
- "How verbose should the output be by default?"
- "Should there be a --dry-run option?"

## Question Format

Use AskUserQuestion with structured options:
- 2-4 concrete options based on design methodology
- Options represent real design alternatives
- Include an "Other" option for open-ended input
- Each option includes its design implication

## Sketch Readiness Criteria

Ready to sketch ONLY when all can be answered:
- Complete happy path described (no "and then something happens")
- Each step has expected output defined
- Emotional arc is explicit and coherent
- Shared artifacts identified with sources
- At least major error paths acknowledged

If ANY criterion is unclear -- ask more questions.

## When to Question

Always ask first when:
- User requests a sketch -- ask before sketching
- User describes a feature -- ask to understand journey context
- User mentions a command -- ask what they expect to see

Continue asking until:
- User can describe complete happy path without gaps
- All shared artifacts identified with sources
- Emotional arc is explicit and coherent
- Error paths are at least acknowledged

## Anti-Patterns

- Jumping to sketching before understanding mental model
- Assuming you know what the user expects
- Filling gaps with your own assumptions
- Skipping emotional journey questions
- Ignoring shared artifact tracking

Instead:
- Ask one more question when uncertain
- Reflect back understanding for user validation
- Make the user articulate their mental model explicitly
- Map emotional states at every step
- Document every ${variable} and its source
03

Trust audit

SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.

LayerWhat it checksResult
L0Provenance & inventoryPASS
L1Static analysis of the codeNA
L2Instruction surface (what it tells the agent)PASS
L3Class-specific surfacePASS
L4Behavioural (sandbox)SKIPPED

What the source does

Filesystem
none-observed
Network
none-observed
Shell
none-observed
Dependencies
pinned
Secrets in source
none-found

Findings (0)

No findings outside the package's declared scope.

Gates applied: no_behavioural_pass.

Audited 2026-10-14 · audit v0.4.1 · source sha 1a864ec81cdefull audit observations/trust-audit/skill/nwave-ai__nw-discovery-methodology.json · Report an issue / request a re-scan
04

Audit history

Every audit this skill has had.

DateSourceVerdictGradeScoreChange
2026-10-141a864ec81cdeSAFEB89first audit
05

Questions

What does the Nw Discovery Methodology skill do?

AI agents that guide you from idea to working code, with you in control at every step.

Is Nw Discovery Methodology safe to install?

The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.

What can Nw Discovery Methodology access on my machine?

The audit observed no filesystem, network or shell use at all in its source.

How current is this page?

The grade is for one exact copy of the source (1a864ec81cde), read on 2026-10-14. The repository is watched, and a new audit runs when it changes — this is the first audit.

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