ai-kickoff
Start a New AI Feature
Create a project structure for building an AI-powered feature with DSPy:
$ARGUMENTS/
├── main.py # Entry point — run your AI feature
├── program.py # AI logic (DSPy module)
├── metrics.py # How to measure if the AI is working
├── optimize.py # Make the AI better automatically
├── evaluate.py # Test the AI's quality
├── data.py # Training/test data loading
└── requirements.txt # Dependencies
When NOT to scaffold
- Already have a DSPy codebase — add your feature to the existing project. This skill creates a new project from scratch.
- Exploring or prototyping — if you just want to test an idea, write a single script. Scaffolding adds structure you do not need yet.
More from lebsral/dspy-programming-not-prompting-lms-skills
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51ai-do
Describe your AI problem and get routed to the right skill with a ready-to-use prompt. Use when you are not sure which ai- skill to use, want help picking the right approach, or just want to describe what you need in plain language. Also use this when someone says I want to build an AI that..., how do I make my AI..., or describes any AI/LLM task without naming a specific skill, I need AI but do not know where to start, which AI pattern should I use, what is the best way to add AI to my app, recommend an AI approach, AI feature discovery, too many AI options, overwhelmed by AI frameworks, just tell me what to build, new to DSPy, beginner AI project help, which LLM pattern fits my use case, confused about AI architecture, help me figure out my AI approach.
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Build a conversational AI assistant with memory and state. Use when you need a customer support chatbot, helpdesk bot, onboarding assistant, sales qualification bot, FAQ assistant, or any multi-turn conversational AI. Also used for chatbot remember previous messages, conversational AI keeps forgetting context, build a helpdesk bot that actually works, chatbot drops context after a few turns, Intercom bot alternative, Zendesk AI alternative, build WhatsApp bot, Slack bot with AI, chatbot escalation to human agent, LangChain chatbot but simpler, chatbot for SaaS onboarding flow.
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Measure and improve how well your AI works. Use when AI gives wrong answers, accuracy is bad, responses are unreliable, you need to test AI quality, evaluate your AI, write metrics, benchmark performance, optimize prompts, improve results, or systematically make your AI better. Also used for spent hours tweaking prompts, trial and error prompt engineering is not working, quality plateaued early, stale prompts everywhere in your codebase, my AI is only 60% accurate, how to measure AI quality, AI evaluation framework, benchmark my LLM, prompt optimization not working, systematic way to improve AI, AI accuracy plateaued, DSPy optimizer tutorial, MIPROv2 optimization, how to go from 70% to 90% accuracy.
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