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prompt-snr-optimizer

Optimize AI prompts by increasing signal-to-noise ratio and hardening against injection.

Updated Mar 1, 2026
Search Tags:#security#prompt engineering#llm#ai optimization#prompt injection#text compression
One-click install
npx skills add https://github.com/painter99/windsurf-prejunior-config --skill prompt-snr-optimizer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-snr-optimizer
Source: https://github.com/painter99/windsurf-prejunior-config/tree/main/skills/prompt-snr-optimizer
Command: npx skills add https://github.com/painter99/windsurf-prejunior-config --skill prompt-snr-optimizer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill tackles the issue of overly verbose, inefficient, and potentially insecure AI prompts by systematically refining them to be clearer, more concise, and safer.

Core Features & Use Cases

  • Prompt Compression: Reduces token count while preserving essential meaning and logic.
  • Variable Shielding: Protects placeholders and variables from accidental modification or injection.
  • Security Hardening: Explicitly separates trusted instructions from untrusted data to prevent prompt injection.
  • Use Case: You have a complex system prompt for an AI assistant that is too long and might be vulnerable to injection. Use this Skill to analyze and optimize it, ensuring it's both efficient and secure.

Quick Start

Use the prompt-snr-optimizer skill to analyze the attached prompt text.

Frequently Asked Questions about prompt-snr-optimizer

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

FAQPage Schema
How do I reduce token count in LLM prompts while preserving logic?▼

To reduce token count in LLM prompts, apply text compression and logic dehydration techniques. This removes redundant noise while preserving essential meaning, ensuring system and task prompts remain structurally efficient and parseable.

What is prompt injection and how does variable shielding prevent it?▼

Prompt injection exploits untrusted data to hijack LLM instructions. Variable shielding prevents this by protecting placeholders from accidental modification and explicitly separating trusted system instructions from untrusted task data.

How do I optimize AI prompts for clarity and security?▼

Optimize AI prompts by applying a multi-step pipeline including auto-typology, structural normalization, and injection hardening. This increases the signal-to-noise ratio, ensuring outputs are deterministic, parseable, and safe from malicious inputs.

Can I analyze complex system prompts to check for inefficiency and vulnerabilities?▼

Yes, you can analyze complex system prompts using an optional analysis mode. This evaluates overly verbose or insecure AI prompts, identifying inefficiencies and potential prompt injection risks before applying structural normalization.

What is the best way to harden LLM instructions against untrusted data?▼

The best way to harden LLM instructions is through injection hardening and contract finalization. This explicitly separates trusted instructions from untrusted data, applying attention anchoring to ensure deterministic and secure prompt execution.

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