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Found 109 Skills
Enable efficient communication between Thai-language users and agents by translating Thai prompts into English in two modes and by preventing Thai text corruption in files. Use when the user writes in Thai, asks for Thai-to-English interpretation, wants token-efficient prompt rewriting, or reports mojibake/replacement-character issues such as U+FFFD in saved files.
Use when prompts produce inconsistent or unreliable outputs, need explicit structure and constraints, require safety guardrails or quality checks, involve multi-step reasoning that needs decomposition, need domain expertise encoding, or when user mentions improving prompts, prompt templates, structured prompts, prompt optimization, reliable AI outputs, or prompt patterns.
프롬프트를 실증 기반 기법으로 분석하고 개선합니다. Few-shot, CoT, XML 구조화, Context Engineering 등 검증된 기법을 적용하여 프롬프트 품질을 높입니다. 프롬프트 개선, prompt 리뷰, 프롬프트 최적화, 프롬프팅 개선 요청 시 사용.
Transforms vague or rough prompts into precise, structured AI instructions. Use when asked to "refine prompt", "improve prompt", "make this prompt better", "promptify", "optimize prompt", "rewrite prompt", "enhance prompt", or "sharpen instructions".
Test, validate, and improve agent instructions (CLAUDE.md, system prompts) using sub-agents as experiment subjects. Measures instruction compliance, context decay, and constraint strength. Use for "test prompt", "validate instructions", "prompt effectiveness", "instruction decay", or when designing robust agent behaviors.
Find prompt and model quality issues using real conversation data, with specific optimization recommendations. Can implement prompt fixes and model switches directly in your codebase.
Create, optimize, and iteratively refine agent prompts and system prompts. Use when asked to "improve a prompt", "optimize a system prompt", "rewrite an agent prompt", "tune prompt wording", "make this prompt more reliable", or "adapt a prompt for OpenAI, Claude, or Gemini". Handles model-specific prompt guidance, prompt markers/tags, eval design, and meta optimization loops for new and existing prompts.
Transforms vague or simple user prompts into high-quality, structured, and high-performance AI instructions using systematic optimization techniques like XML tagging, few-shot examples, and Chain-of-Thought. Use this skill when you need to improve the reliability, accuracy, or formatting of an AI's output.
Distills a jackin❯ roadmap item — plus optional plan files — into a self-contained /goal prompt capped at 4000 characters.
This skill should be used when the user asks to "create a replit prompt", "write a prompt for replit", "optimize for replit agent", "prepare instructions for replit", or mentions building something with Replit Agent. Transforms user requirements into optimized, structured prompts that Replit Agent understands and executes accurately with minimal iterations.
Prompt engineering and optimization for AI/LLMs. Capabilities: transform unclear prompts, reduce token usage, improve structure, add constraints, optimize for specific models, backward-compatible rewrites. Actions: improve, enhance, optimize, refactor, compress prompts. Keywords: prompt engineering, prompt optimization, token efficiency, LLM prompt, AI prompt, clarity, structure, system prompt, user prompt, few-shot, chain-of-thought, instruction tuning, prompt compression, token reduction, prompt rewrite, semantic preservation. Use when: improving unclear prompts, reducing token consumption, optimizing LLM outputs, restructuring verbose requests, creating system prompts, enhancing prompt clarity.
KERNEL-based prompt engineering — transforms vague requests into structured, high-performance prompts optimized for first-try success.