Agent skill · writing content · sickn33
llm-prompt-optimizer
Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
Why this skill is useful
Provides systematic frameworks and techniques for optimizing prompts that improve output quality and reduce hallucinations, which the AI may not generate on its own.
What it needs
About 4k tokens when loaded. Last updated 2026-08-07. 44,571 stars on the source repository.
What this skill does
LLM Prompt Optimizer Overview This skill transforms weak, vague, or inconsistent prompts into precision-engineered instructions that reliably produce high-quality outputs from any LLM (Claude, Gemini, GPT-4, Llama, etc.). It applies systematic prompt engineering frameworks — from zero-shot to few-shot, chain-of-thought, and structured output patterns. When to Use This Skill Use when a prompt returns inconsistent, vague, or hallucinated results Use when you need structured/JSON output from an LLM reliably Use when designing system prompts for AI agents or chatbots Use when you want to reduce token usage without sacrificing quality Use when implementing chain-of-thought reasoning for complex tasks Use when prompts work on one model but fail on another Step-by-Step Guide 1. Diagnose the Weak Prompt Before optimizing, identify which problem pattern applies: Problem Symptom Fix --------- --------- ----- Too vague Generic, unhelpful answers Add role + context + constraints No structure Unformatted, hard-to-parse output Specify output format explicitly Hallucination Confident wrong answers Add "say I don't know if unsure" Inconsistent Different answers each run Add few-shot examples Too long Verbose, padded responses Add length constraints 2. Apply the RSCIT Framework Every optimized prompt should have: R — Role: Who is the AI in this interaction? S — Situation: What context does it need? C — Constraints: What are the rules and limits? I — Instructions: What exactly should it do? T — Template: What should the output look like? Before (weak prompt): After (optimized prompt): 3. Chain-of-Thought (CoT) Pattern For reasoning tasks, instruct the model to think step-by-step: 4. Few-Shot Examples Pattern Provide 2-3 examples to establish the pattern: 5. Structured JSON Output Pattern 6. Reduce Hallucination Pattern 7. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills sickn33/llm-prompt-optimizer--3d1273