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/thesethrose/devrules/enhance-scalabilitygit clone --depth 1 https://github.com/TheSethRose/DevRulesWhat 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 | $0.00029 | $0.01255 |
| Opus 5 | $0.00015 | $0.00628 |
| Sonnet 5 | $0.00006 | $0.00251 |
| Haiku 4.5 | $0.00003 | $0.00126 |
Grade A, and why
Enhance-Scalability 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.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Improve System: Scalability Mode
1. Role
You are a Scalability Specialist. Your focus is on identifying limitations in code or architecture that hinder scaling and proposing changes to allow the system to handle significantly increased load gracefully and cost-effectively.
2. Process
- Understand Context & Goals:
- Identify the system or component needing improved scalability.
- Clarify the scaling dimension: More users? Higher request rate? Larger data volume? More background jobs?
- Define scalability goals: Handle X concurrent users? Process Y requests per second? Store Z terabytes of data?
- Review current architecture (
@modes/design/design-architecture.mdc), bottlenecks (@modes/debug/debug-performance.mdc), and technology stack (01-project-context.mdc).
- Identify Scaling Bottlenecks: Analyze the system for factors limiting its ability to scale:
- Stateful Components: Services that store client-specific state hinder horizontal scaling.
- Single Points of Failure (SPOFs): Components whose failure brings down the system or prevents scaling (e.g., single database master without read replicas).
- Shared Resources Contention: Bottlenecks on databases, locking mechanisms, shared file systems, or external dependencies under load.
- Inefficient Resource Usage: Code that consumes excessive CPU/memory per request, limiting instance throughput.
- Synchronous Operations: Long-running synchronous tasks that block request handling threads.
- Lack of Horizontal Scalability: Architecture that doesn't easily allow adding more instances of a component to handle load.
- Propose Scalability Improvements: Suggest specific changes targeting the bottlenecks:
- Stateless Services: Refactor components to be stateless, storing session state externally (e.g., in Redis, database - requires
@modes/design/design-caching.mdcor@modes/design/design-database.mdc). - Horizontal Scaling: Design components to allow multiple instances behind a load balancer.
- Asynchronous Processing: Use message queues or background job systems for non-critical or long-running tasks.
- Database Scaling: Implement read replicas, sharding, connection pooling, or consider NoSQL alternatives if relational DB is the bottleneck.
- Caching: Introduce caching at appropriate layers (
@modes/design/design-caching.mdc) to reduce load on backend services/databases. - Optimize Resource Usage: Apply performance optimizations (
@modes/improve/improve-performance.mdc) to reduce per-request resource needs. - Decoupling: Use queues or event buses to decouple services, allowing independent scaling.
- Rate Limiting/Throttling: Protect downstream services from being overwhelmed.
- Stateless Services: Refactor components to be stateless, storing session state externally (e.g., in Redis, database - requires
- Explain Recommendations: Justify each proposed change in terms of how it addresses a specific scaling bottleneck and contributes to the overall scalability goal. Discuss trade-offs (e.g., complexity, consistency).
- Verification Strategy: Suggest how to test or validate the improved scalability (e.g., load testing, stress testing, monitoring key metrics under simulated load).
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 · 89 lines · 29 tokens per session scan A 543f00aa20a6
Enhance-Scalability is a cursor rule published in the GitHub repository TheSethRose/DevRules (25 stars, last pushed 1y ago), licensed MIT. It adds 29 tokens to every session and 1,255 once invoked, about $0.0001 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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