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-workflowgit 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-workflow)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-discovery-workflow"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-discovery-workflow/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-workflow"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-discovery-workflow.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 | $0.00023 | $0.01037 |
| Opus 5.5 | $0.00009 | $0.00415 |
| Sonnet 5.5 | $0.00005 | $0.00207 |
| Haiku 4.5 | $0.00002 | $0.00104 |
Grade A, and why
nw-discovery-workflow 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Discovery Workflow
4-Phase Overview
PHASE 1 PHASE 2 PHASE 3 PHASE 4
Problem Validation Opportunity Mapping Solution Testing Market Viability
| | | |
v v v v
"Is this real?" "Which matters?" "Does it work?" "Viable business?"
Phase Details
Phase 1: Problem Validation
Duration: 1-2 weeks | Min interviews: 5 | Techniques: Mom Test, Job Mapping Core question: Is this a real problem worth solving?
Phase 2: Opportunity Mapping
Duration: 1-2 weeks | Min interviews: 10 cumulative | Techniques: OST, Opportunity Algorithm Core question: Which problems matter most?
Phase 3: Solution Testing
Duration: 2-4 weeks | Min interviews: 5 per iteration | Techniques: hypothesis testing, prototypes Core question: Does our solution actually work?
Phase 4: Market Viability
Duration: 2-4 weeks | Min interviews: 5 + stakeholders | Techniques: Lean Canvas, 4 Big Risks Core question: Can we build a viable business?
Decision Gates
G1: Problem to Opportunity
Proceed: 5+ confirm pain + willingness to pay | Pivot: problem differs from expected | Kill: <20% confirm
G2: Opportunity to Solution
Proceed: top 2-3 score >8 (max 20) | Pivot: new opportunities discovered | Kill: all low-value Scoring: Score = Importance + Max(0, Importance - Satisfaction). Each 1-10. >8 = high importance with satisfaction gap.
G3: Solution to Viability
Proceed: >80% task completion, usability validated | Pivot: needs refinement | Kill: fundamental blocks
G4: Viability to Build
Proceed: all 4 risks addressed, model validated | Pivot: model needs adjustment | Kill: no viable model
Success Metrics
Phase 1: Problem Validation
| Metric | Target |
|---|---|
| Problem confirmation | >60% (3+ of 5) |
| Frequency | Weekly+ |
| Current spending | >$0 on workarounds |
| Emotional intensity | Frustration evident |
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 · 116 lines · 23 tokens per session scan A 8b851baf6d0c
nw-discovery-workflow is a skill published in the GitHub repository nWave-ai/nWave (620 stars, last pushed 1mo ago), licensed MIT. It adds 23 tokens to every session and 1,037 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.
Other skills, from other repositories
engineering-workflow
A Chinese-language workflow for reliable software development. It describes exploring the problem, making a plan, using TDD (test-driven development, where tests are written before the code), debugging methodically, reviewing code, and verifying the result.
tdd
Use only when the user explicitly asks for TDD, a failing test, or a regression test, OR when the bug has an obvious cheap local test target. Skip when the test path is unclear, expensive, integration-heavy, or not requested.
moai-run
Implement SPEC requirements using DDD/TDD methodology.
test-driven-development
Strict RED-GREEN-REFACTOR cycle enforcement. Tests are never skipped or deferred. Run mode only, never watch mode. Exit code evidence mandatory.
tdd-enforcement
Red-Green-Refactor TDD methodology with mandatory failing tests, minimal implementation, quality refactoring, and 80% coverage gating.
story-development
Implement user stories with test-driven development methodology.