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-sd-frameworkgit 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-sd-framework)<a href="https://agentmods.dev/skills/nwave-ai/nwave/nw-sd-framework"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-sd-framework/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-sd-framework"><img src="https://agentmods.dev/badge/skills/nwave-ai/nwave/nw-sd-framework.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 | $0.00022 | $0.01316 |
| Opus 5.5 | $0.00009 | $0.00526 |
| Sonnet 5.5 | $0.00004 | $0.00263 |
| Haiku 4.5 | $0.00002 | $0.00132 |
Grade A, and why
nw-sd-framework 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Design Framework
The 4-Step Process
Every system design follows this structure. Skipping steps is the top mistake.
Step 1: Understand the Problem and Establish Design Scope (3-10 min)
Narrow an impossibly broad question into a tractable problem.
Ask about: users and scale | most important features | read/write ratio | non-functional requirements (latency, availability, consistency) | existing infrastructure | special constraints (mobile-first, offline, regulatory)
Produce: functional requirements (3-5 bullets) | non-functional requirements (scale, latency, availability, consistency model) | capacity estimation (QPS, storage, bandwidth)
Red flags if skipped: designing a system nobody asked for | over-engineering for imaginary scale | missing critical constraints (GDPR, real-time)
Step 2: Propose High-Level Design and Get Buy-In (10-15 min)
Sketch the big picture. Validate before diving deep.
Do: draw architecture diagram (clients, servers, databases, caches, queues) | define API contract (REST/GraphQL/gRPC -- key endpoints) | design data model (entities, relationships, access patterns) | walk through 1-2 core use cases end-to-end | get buy-in: "Does this make sense before I go deeper?"
API patterns: RESTful for CRUD-heavy | GraphQL for flexible client queries | gRPC for internal service-to-service | WebSocket/SSE for real-time
Data model: SQL vs NoSQL based on access patterns, not hype | denormalization trade-offs | partitioning key selection (directly impacts scalability)
Step 3: Design Deep Dive (10-25 min)
Go deep on 2-3 components.
Choose: most technically challenging | most interesting trade-offs | bottleneck components (highest load, most failure-prone)
Depth means: specific algorithms (consistent hashing, Bloom filters) | failure modes and handling | scaling strategy per component | data flow with edge cases | monitoring and operational concerns
Step 4: Wrap Up (3-5 min)
Cover: summarize design in 2-3 sentences | identify known bottlenecks | what you'd improve with more time | operational concerns (monitoring, alerting, deployment) | future enhancements
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 · 126 lines · 22 tokens per session scan A 2bad0995eb0e
nw-sd-framework is a skill published in the GitHub repository nWave-ai/nWave (620 stars, last pushed 1mo ago), licensed MIT. It adds 22 tokens to every session and 1,316 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 2026-09-06.
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