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 agentmods add rules/homenshum/nodebenchai/flywheel_continuousgit clone --depth 1 https://github.com/HomenShum/NodeBenchAIWhat 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 | $0.00409 | $0.00409 |
| Opus 5 | $0.00204 | $0.00204 |
| Sonnet 5 | $0.00082 | $0.00082 |
| Haiku 4.5 | $0.00041 | $0.00041 |
Grade A, and why
flywheel_continuous 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 2d 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.
What it actually says
Flywheel Continuous Improvement
When in flywheel mode, operate as a continuous improvement loop.
Protocol
- Launch - Start dev server, capture baseline state, publish a trajectory link in-app within 60 seconds.
- Poll - Every 60 seconds, check logs, console, and visual state.
- Diagnose - At each checkpoint, immediately seek issues. Do a 5-whys root-cause chain before any fix.
- Fix - Apply a minimal targeted fix that makes the failure mode impossible, not just hidden.
- Dogfood - Verify the fix end-to-end in the UI and capture evidence (screenshots + walkthrough video).
- Iterate - Return to step 2. Never ask "should I continue?" - just keep going until verified.
- Adapt - After 3 consecutive failures on the same issue, change strategy (instrumentation, isolation, or rollback).
- Document - Log every iteration: symptom, root cause, fix, verification result, and what to watch next.
Motion Safety (Seizure / Flash Policy)
- Avoid high-contrast flashes and large-area pulses/fades.
- Prefer stable backgrounds and subtle loading states; default to non-animated skeletons for full-viewport surfaces.
- Always honor
prefers-reduced-motionand ship a UI toggle when motion is used.
Anti-patterns
- Band-aids: swallowing errors,
as any, masking with?., retry loops without idempotency. - Declaring done without dogfood artifacts and deterministic verification.
Standards
- Target: industry-standard UI performance + product design polish.
- Every route loads without jank, no layout shift, and fast interaction response.
- All interactive elements have focus rings, aria labels, and keyboard support.
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.
- 2d ago First seen · 33 lines · 409 tokens per session scan A 82b2fd94af59
flywheel_continuous is a cursor rule published in the GitHub repository HomenShum/NodeBenchAI (14 stars, last pushed 18d ago), licensed MIT. It adds 409 tokens to every session, about $0.0020 per session on Opus 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 2026-08-30.
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