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.
git 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/commands/nwave-ai/nwave/diverge)<a href="https://agentmods.dev/commands/nwave-ai/nwave/diverge"><img src="https://agentmods.dev/badge/commands/nwave-ai/nwave/diverge/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/commands/nwave-ai/nwave/diverge"><img src="https://agentmods.dev/badge/commands/nwave-ai/nwave/diverge.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00026 | $0.01918 |
| Opus 5.5 | $0.00010 | $0.00767 |
| Sonnet 5.5 | $0.00005 | $0.00384 |
| Haiku 4.5 | $0.00003 | $0.00192 |
Grade A, and why
diverge 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 — 173 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NW-DIVERGER: Structured Divergent Thinking Before Convergence
Wave: DIVERGE (between DISCOVER and DISCUSS, optional) | Agent: Flux (nw-diverger) | Command: /nw-diverger
Overview
Execute DIVERGE wave through Flux's 4-phase workflow: JTBD analysis|competitive research|structured brainstorming|taste-filtered evaluation. Transforms a validated problem into 3-5 concrete, taste-scored design directions so DISCUSS can converge on one with confidence.
DIVERGE is optional. Brownfield features with a clear direction may skip it (see skip checklist in design spec). New products and pivot decisions benefit most from structured divergence.
Interactive Decision Points
Decision 1: Work Type
Question: What type of work is this? Options:
- New product -- no prior solution exists, full divergence needed
- Brownfield feature -- existing product, exploring approach alternatives
- Pivot / redesign -- existing feature being reconsidered from scratch
- Other -- user provides custom context
Decision 2: Research Depth
Question: How deep should competitive research go? Options:
- Lightweight -- 3 competitors, known market
- Comprehensive -- 5+ competitors including non-obvious alternatives
- Deep-dive -- cross-category research, adjacent markets, academic references
Prior Wave Consultation
Before beginning DIVERGE work, read SSOT and prior wave artifacts:
- SSOT (if
docs/product/exists):docs/product/jobs.yaml-- validated jobs and opportunity scoresdocs/product/vision.md-- product vision and strategic context
- Project context:
docs/project-brief.md|docs/stakeholders.yaml(if available) - DISCOVER artifacts: Read
docs/feature/{feature-id}/discover/(if present)wave-decisions.md-- validated assumptions and key decisionsproblem-validation.md-- customer evidence grounding the problem
If docs/product/ does not exist, this is the first wave using the SSOT model. DIVERGE will create it (bootstrap docs/product/jobs.yaml with the validated job).
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 · 173 lines · 26 tokens per session scan A c384361923d1
diverge is a command published in the GitHub repository nWave-ai/nWave (621 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 1,918 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.
Other commands, from other repositories
strategize
Develop a complete UX strategy for a product or feature area.
discover
Run a full user research cycle — persona creation, empathy mapping, and journey mapping for a product or feature.
svg-create
Author or clean up an SVG asset — icon, illustration, mascot pose, logo mark — so it scales from viewBox, recolors from tokens, animates without a rewrite, is optimized, and has an accessible name.
agentation-fix
Session-2 fix loop for Agentation — read structured annotations from the dev overlay and apply targeted UI fixes.
design-bench
Run the ABC benchmark — reference URL → design-blind brief → render arms A/B/C → blind judge → scorecard with win-rates over n runs.
work-flow-feature
Complete workflow for developing a new feature, from exploration to merge.