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-leanux-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-leanux-methodology)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-leanux-methodology"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-leanux-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-leanux-methodology"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-leanux-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 | $0.01979 |
| Opus 5.5 | $0.00015 | $0.00792 |
| Sonnet 5.5 | $0.00007 | $0.00396 |
| Haiku 4.5 | $0.00004 | $0.00198 |
Grade A, and why
nw-leanux-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 — 210 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LeanUX Backlog Management Methodology
"A backlog is not a todo list. It's a collection of validated hypotheses waiting to become working software."
Story States
| State | Meaning | Entry Criteria |
|---|---|---|
| Draft | Idea captured, not validated | Has problem statement |
| Ready | Validated, has UAT, ready to build | All DoR items complete |
| In Progress | Actively being built | UAT test written (RED) |
| In Review | Code complete, awaiting review | All tests green |
| Done | Merged, deployed, validated | UAT passes in production |
| Blocked | Cannot proceed | Blocker documented |
Story Sizing Criteria
Right-Sized
Completable in 1-3 days | 3-7 UAT scenarios | Delivers demonstrable value | Explainable in 2 minutes
Oversized Indicators
7 UAT scenarios | >3 days effort | Multiple distinct user outcomes | Cannot demonstrate in single session
Splitting Strategy
Split by user outcome, not technical layer. Each resulting story delivers independently demonstrable value.
Example: "User Management" (20 scenarios) splits into:
- "Quick Login" (5 scenarios) -- returning customer on trusted device
- "New Registration" (4 scenarios) -- first-time customer sign-up
- "Password Recovery" (3 scenarios) -- customer who forgot credentials
- "Profile Settings" (4 scenarios) -- customer updating preferences
Definition of Ready (DoR) - Hard Gate
Stories pass ALL 8 items before proceeding to DESIGN wave.
Checklist with Pass/Fail Examples
1. Problem statement clear and in domain language
- Pass: "Maria wastes 30 seconds typing credentials on every visit"
- Fail: "Users need authentication"
2. User/persona identified with specific characteristics
- Pass: "Returning customer (2+ orders) on trusted personal device"
- Fail: "User" or "Customer"
3. At least 3 domain examples with real data
- Pass: "Maria on her MacBook, last login 5 days ago, goes directly to dashboard"
- Fail: "User logs in successfully"
4. UAT scenarios in Given/When/Then (3-7 scenarios)
- Pass: "Given Maria authenticated 5 days ago on 'MacBook-Home'..."
- Fail: "Test login functionality"
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 · 210 lines · 37 tokens per session scan A 1ad4f2bb74a2
nw-leanux-methodology is a skill published in the GitHub repository nWave-ai/nWave (624 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 1,979 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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