model-dos

Enforce token limits, throttling, and timeouts on LLM API endpoints.

20|Updated Feb 27, 2026
One-click install
npx skills add https://github.com/thejefflarson/soundcheck --skill model-dos
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: model-dos
Source: https://github.com/thejefflarson/soundcheck/tree/main/.claude/skills/model-dos
Command: npx skills add https://github.com/thejefflarson/soundcheck --skill model-dos

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Protects LLM endpoints from resource exhaustion by enforcing input caps, per-user throttling, and strict timeouts.

Core Features & Use Cases

  • Input length caps and token limits to bound prompts before they reach the model.
  • Per-identifier throttling to prevent abuse from a single user or API key.
  • Explicit inference deadlines to ensure timely responses even when upstreams misbehave.
  • Applicable to any service exposing an LLM API, including chatbots and multi-turn workflows.

Quick Start

Configure your LLM endpoint to enforce max tokens, rate limits, and request timeouts for incoming prompts.

Frequently Asked Questions about model-dos

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I prevent LLM API denial of service from unbounded prompts?▼

Prevent LLM DoS by enforcing input length caps, max token limits, and strict request timeouts to block resource exhaustion before prompts reach the model.

What is per-identifier throttling for LLM endpoints?▼

Per-identifier throttling limits API requests from a single user or key, preventing chat handlers and multi-turn workflows from triggering resource exhaustion through rapid abusive calls.

How do I cap prompt history in multi-turn chatbot workflows?▼

Cap prompt history by bounding conversation length inputs before they reach the model, ensuring inferences remain bounded and timely even when upstream calls misbehave.

Does this rate-limiting approach work with any LLM service?▼

Yes, this approach applies to any service exposing an LLM API. It enforces max tokens, input caps, and timeouts across chat handlers and multi-turn workflows.

Why do my LLM endpoints hang during slow upstream calls?▼

Endpoints hang because upstream calls lack explicit inference deadlines. Enforcing strict request timeouts guarantees timely responses and prevents resource exhaustion from slow upstreams.

When should I use prompt capping for API security?▼

Use prompt capping when exposing LLM APIs to prevent resource exhaustion. It bounds recursive prompts, enforces token limits, and applies per-user throttling to stop abuse.