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llm-integration

Integrates Llama.cpp and Ollama models securely for offline inference workflows.

3|1|Updated Dec 3, 2025
Search Tags:#llm#structured output#ollama#gguf#llama.cpp#local inference#prompt injection defense
One-click install
npx skills add https://github.com/Probably-Group/Dev-AID --skill llm-integration-probably-group
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-integration
Source: https://github.com/Probably-Group/Dev-AID/tree/main/.dev-aid/skills/expert/llm-integration
Command: npx skills add https://github.com/Probably-Group/Dev-AID --skill llm-integration-probably-group

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill prevents unsafe and unreliable local LLM integrations by enforcing prompt-injection defenses, structured output validation, and strict resource limits when running llama.cpp, Ollama, and GGUF models.

Core Features & Use Cases

  • Prompt Injection & Output Safety Guardrails: Uses delimiter/role separation patterns and validates model outputs before any downstream use.
  • Offline Local Model Integration: Supports deploying local inference for privacy-preserving, self-hosted AI coding and research workflows.
  • Constrained Structured Outputs: Prefer schema-guaranteed JSON generation (e.g., Ollama format / vLLM guided_json) to avoid fragile parse-and-retry flows.

Quick Start

Configure your local LLM (llama.cpp or Ollama) and apply these rules to generate structured, validated outputs for your offline AI pipeline.

Frequently Asked Questions about llm-integration

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

FAQPage Schema
How do I prevent prompt injection when running local LLM inference?▼

To prevent prompt injection during local LLM inference, apply delimiter and role separation patterns to isolate untrusted inputs, and enforce schema-validated outputs before any downstream use.

How do I get structured JSON output from Ollama or llama.cpp?▼

To get structured JSON output from Ollama or llama.cpp, use schema-guaranteed generation methods like Ollama's `format` parameter, which avoids fragile parse-and-retry flows by constraining the model's output.

Can I run offline local LLMs for code generation without internet access?▼

Yes, you can run offline local LLMs for deterministic code-generation support using GGUF models with llama.cpp or Ollama in self-hosted development workflows without requiring internet access.

What's the best way to enforce resource limits on self-hosted LLM workflows?▼

The best way to enforce resource limits on self-hosted LLM workflows is to apply bounded token, cost, and resource controls, alongside strict secrets handling and input delimiterization.

Does local LLM integration work with GGUF models for privacy-preserving research?▼

Yes, local LLM integration works with GGUF models for privacy-preserving research by supporting offline inference deployments that keep data self-hosted and secure within your own infrastructure.

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