system-prompt-creator

Solid

Create high-quality, model-aware system prompts for any LLM (Claude, GPT, Gemini, open-source, etc.). Use this skill whenever the user wants to create, write, build, design, draft, or improve a system prompt, system instructions, or custom instructions for any AI model. Also trigger when the user asks about prompt engineering, prompt design, prompt optimization, or wants to define behavior for an AI assistant, chatbot, agent, or any LLM-powered application — even if they don't explicitly say "system prompt". Covers all use cases including chatbots, agentic systems, tool-use workflows, content generation, data extraction, code assistants, and multi-turn conversations.

AI & Automation 72 stars 29 forks Updated 4 days ago MIT

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Quality Score: 84/100

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Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# System Prompt Creator Build effective system prompts for any LLM using a structured, research-backed process derived from the official prompting guides of Claude (Anthropic), GPT-5 (OpenAI), and Gemini (Google). This skill guides you through interview, analysis, structuring, drafting, and optimization — producing system prompts that are clear, well-structured, and tailored to the target model. ## Workflow Follow these 5 steps in order. Each step builds on the previous. ### Step 1: Interview — Gather Requirements Before writing anything, collect these essentials from the user: 1. **Target model** — Which LLM? (Claude, GPT, Gemini, open-source, or model-agnostic) 2. **Use case** — What will the AI do? (chatbot, agent, content generator, data extractor, code assistant, etc.) 3. **Persona** — Who should the AI be? (role, expertise level, personality traits) 4. **Audience** — Who interacts with it? (developers, end users, internal team, customers) 5. **Input types** — What will users send? (free text, code, documents, images, structured data) 6. **Output format** — What should responses look like? (prose, JSON, markdown, code, tables) 7. **Tone & style** — Formal, casual, technical, friendly? Concise or detailed? 8. **Constraints** — What must it NOT do? (safety rules, topic limits, action restrictions) 9. **Tools & capabilities** — Does it have tool access? (APIs, file system, web search, code execution) 10. **Examples** — Can the user provide examples of good/bad respon...

Details

Author
tronghieu
Repository
tronghieu/agent-skills
Created
6 months ago
Last Updated
4 days ago
Language
Python
License
MIT

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