Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add nWave-ai/nWave --skill nw-design-methodologygit clone --depth 1 https://github.com/nWave-ai/nWaveWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nwave-ai/nwave/nw-design-methodology)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-design-methodology"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-design-methodology/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-design-methodology"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-design-methodology.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00037 | $0.01190 |
| Opus 5 | $0.00018 | $0.00595 |
| Sonnet 5 | $0.00007 | $0.00238 |
| Haiku 4.5 | $0.00004 | $0.00119 |
Grade A, and why
nw-design-methodology scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Design Methodology (Apple LeanUX++)
Design Workflow
PHASE 1 PHASE 2 PHASE 3 PHASE 4
Journey Mapping Emotional Design TUI Prototyping Integration Check
| | | |
v v v v
"What's the flow?" "How should it feel?" "What does it look?" "Does it connect?"
Phase 1: Journey Mapping (1-2 days)
- Techniques: User journey mapping | goal-completion flow | step identification
- Question: "What complete journey is the user trying to accomplish?"
- Output: Journey map with steps, commands, and touchpoints
Phase 2: Emotional Design (1 day)
- Techniques: Emotional arc design | form follows feeling | transition analysis
- Question: "How should the user FEEL at each step?"
- Output: Emotional annotations on journey map
Phase 3: TUI Prototyping (1-3 days)
- Techniques: Progressive fidelity | ASCII mockups | TUI design patterns
- Question: "What does each step look like?"
- Output: TUI mockups for each journey step
Phase 4: Integration Check (1 day)
- Techniques: Shared artifact tracking | horizontal coherence | CLI vocabulary
- Question: "Do all pieces connect properly?"
- Output: Validated journey with integration checkpoints
Journey Schema
schema_version: 1
journey:
name: "{Goal Name}"
goal: "{What user is trying to accomplish}"
persona: "{User persona reference}"
emotional_arc:
start: "{Initial emotional state}"
middle: "{Journey emotional state}"
end: "{Final emotional state}"
steps:
- id: 1
name: "{Step Name}"
command: "{CLI command or action}"
tui_mockup: |
+-- Step N: {Name} -----------------------------------------+
| {ASCII representation of CLI output} |
| ${variable} <-- tracked artifact |
+------------------------------------------------------------+
shared_artifacts:
- name: "{artifact_name}"
source: "{single source of truth file}"
displayed_as: "${variable}"
consumers: ["{list of places this appears}"]
emotional_state:
entry: "{How user feels entering step}"
exit: "{How user feels after step}"
integration_checkpoint: |
{What must be validated before proceeding}
failure_modes:
- "{What can go wrong at this step — used by DISTILL for error scenario generation}"
- "{Another failure scenario}"
gherkin: |
Scenario: {Step description}
Given {precondition}
When {action}
Then {observable outcome}
And shared artifact "${variable}" matches source
integration_validation:
shared_artifact_consistency:
- artifact: "{name}"
must_match_across: [1, 2, 3]
failure_message: "{Integration error description}"
changelog:
- date: "{YYYY-MM-DD}"
feature: "{feature-id}"
change: "{What changed in this update}"
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 156 lines · 37 tokens per session scan A 8ebecd812e33
nw-design-methodology is a skill published in the GitHub repository nWave-ai/nWave (610 stars, last pushed 6d ago), licensed MIT. It adds 37 tokens to every session and 1,190 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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concept-selection
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