From 2fdb916f9341196f099ad6c01935d9bc43031e4f Mon Sep 17 00:00:00 2001 From: "SnakeEye-sudo (Er. Sangam Krishna)" Date: Wed, 4 Mar 2026 11:55:02 +0530 Subject: [PATCH] feat: add llm-prompt-optimizer skill --- skills/llm-prompt-optimizer/SKILL.md | 182 +++++++++++++++++++++++++++ 1 file changed, 182 insertions(+) create mode 100644 skills/llm-prompt-optimizer/SKILL.md diff --git a/skills/llm-prompt-optimizer/SKILL.md b/skills/llm-prompt-optimizer/SKILL.md new file mode 100644 index 00000000..d9a405d9 --- /dev/null +++ b/skills/llm-prompt-optimizer/SKILL.md @@ -0,0 +1,182 @@ +--- +name: llm-prompt-optimizer +description: "Use when improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage." +risk: low +source: community +date_added: "2026-03-04" +--- + +# 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):** +``` +Explain machine learning. +``` + +**After (optimized prompt):** +``` +You are a senior ML engineer explaining concepts to a junior developer. + +Context: The developer has 1 year of Python experience but no ML background. + +Task: Explain supervised machine learning in simple terms. + +Constraints: +- Use an analogy from everyday life +- Maximum 200 words +- No mathematical formulas +- End with one actionable next step + +Format: Plain prose, no bullet points. +``` + +### 3. Chain-of-Thought (CoT) Pattern + +For reasoning tasks, instruct the model to think step-by-step: + +``` +Solve this problem step by step, showing your work at each stage. +Only provide the final answer after completing all reasoning steps. + +Problem: [your problem here] + +Thinking process: +Step 1: [identify what's given] +Step 2: [identify what's needed] +Step 3: [apply logic or formula] +Step 4: [verify the answer] + +Final Answer: +``` + +### 4. Few-Shot Examples Pattern + +Provide 2-3 examples to establish the pattern: + +``` +Classify the sentiment of customer reviews as POSITIVE, NEGATIVE, or NEUTRAL. + +Examples: +Review: "This product exceeded my expectations!" -> POSITIVE +Review: "It arrived broken and support was useless." -> NEGATIVE +Review: "Product works as described, nothing special." -> NEUTRAL + +Now classify: +Review: "[your review here]" -> +``` + +### 5. Structured JSON Output Pattern + +``` +Extract the following information from the text below and return it as valid JSON only. +Do not include any explanation or markdown — just the raw JSON object. + +Schema: +{ + "name": string, + "email": string | null, + "company": string | null, + "role": string | null +} + +Text: [input text here] +``` + +### 6. Reduce Hallucination Pattern + +``` +Answer the following question based ONLY on the provided context. +If the answer is not contained in the context, respond with exactly: "I don't have enough information to answer this." +Do not make up or infer information not present in the context. + +Context: +[your context here] + +Question: [your question here] +``` + +### 7. Prompt Compression Techniques + +Reduce token count without losing effectiveness: + +``` +# Verbose (expensive) +"Please carefully analyze the following code and provide a detailed explanation of +what it does, how it works, and any potential issues you might find." + +# Compressed (efficient, same quality) +"Analyze this code: explain what it does, how it works, and flag any issues." +``` + +## Best Practices + +- ✅ **Do:** Always specify the output format (JSON, markdown, plain text, bullet list) +- ✅ **Do:** Use delimiters (```, ---) to separate instructions from content +- ✅ **Do:** Test prompts with edge cases (empty input, unusual data) +- ✅ **Do:** Version your system prompts in source control +- ✅ **Do:** Add "think step by step" for math, logic, or multi-step tasks +- ❌ **Don't:** Use negative-only instructions ("don't be verbose") — add positive alternatives +- ❌ **Don't:** Assume the model knows your codebase context — always include it +- ❌ **Don't:** Use the same prompt across different models without testing — they behave differently + +## Prompt Audit Checklist + +Before using a prompt in production: + +- [ ] Does it have a clear role/persona? +- [ ] Is the output format explicitly defined? +- [ ] Are edge cases handled (empty input, ambiguous data)? +- [ ] Is the length appropriate (not too long/short)? +- [ ] Has it been tested on 5+ varied inputs? +- [ ] Is hallucination risk addressed for factual tasks? + +## Troubleshooting + +**Problem:** Model ignores format instructions +**Solution:** Move format instructions to the END of the prompt, after examples. Use strong language: "You MUST return only valid JSON." + +**Problem:** Inconsistent results between runs +**Solution:** Lower the temperature setting (0.0-0.3 for factual tasks). Add more few-shot examples. + +**Problem:** Prompt works in playground but fails in production +**Solution:** Check if system prompt is being sent correctly. Verify token limits aren't being exceeded (use a token counter). + +**Problem:** Output is too long +**Solution:** Add explicit word/sentence limits: "Respond in exactly 3 bullet points, each under 20 words."