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-discovery-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-discovery-methodology)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-discovery-methodology"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-discovery-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-discovery-methodology"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-discovery-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 | $0.00030 | $0.01076 |
| Opus 5.5 | $0.00012 | $0.00430 |
| Sonnet 5.5 | $0.00006 | $0.00215 |
| Haiku 4.5 | $0.00003 | $0.00108 |
Grade A, and why
nw-discovery-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 1mo 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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?
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.
- 1mo ago First seen · 138 lines · 30 tokens per session scan A 9fbc2a5174c6
nw-discovery-methodology is a skill published in the GitHub repository nWave-ai/nWave (620 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 1,076 once invoked, about $0.0001 per session on Opus 5.5. 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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