ai-kickoff

name	ai-kickoff
description	Scaffold a new AI feature powered by DSPy. Use when adding AI to your app, starting a new AI project, building an AI-powered feature, setting up a DSPy program from scratch, or bootstrapping an LLM-powered backend. Also used for DSPy quickstart, DSPy hello world, first DSPy program, getting started with DSPy, new to AI development, add AI to existing Python app, AI feature from zero to working, scaffold AI project structure, best practices for AI project setup, where do I even begin with LLMs, AI boilerplate code, starter template for AI features, bootstrap AI backend, simple AI project template, how to structure an AI codebase, AI mvp in a day, proof of concept AI feature, DSPy project structure best practices.
disable-model-invocation	true

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.
• Non-LLM AI — this is for LLM-powered features (classification, extraction, generation, Q&A). For traditional ML, use scikit-learn or similar.

Step 1: Gather requirements

Ask the user:

1. What should the AI do? (sort content, answer questions, extract data, take actions, or describe it)
2. What goes in and what comes out? (e.g., "customer email in, category out" or "question in, answer out")
3. Do you have example data? (if yes, what format — CSV, JSON, database?)
4. Which AI provider? (default: OpenAI — DSPy works with any provider)

Step 2: Generate the project

requirements.txt

dspy>=3.0

Add datasets if loading from HuggingFace. Add provider-specific packages if needed.

data.py

Create dataset loading utilities:

import dspy

def load_data():
    """Load and prepare training/dev data.

    Returns:
        tuple: (trainset, devset) as lists of dspy.Example
    """
    # TODO: Replace with actual data loading
    examples = [
        dspy.Example(input_field="...", output_field="...").with_inputs("input_field"),
    ]

    split = int(0.8 * len(examples))
    return examples[:split], examples[split:]

Adapt field names to match the user's inputs/outputs.

program.py

Create the DSPy module. Choose the right module based on the task:

Task type	Module	When to use
Simple extraction or lookup	dspy.Predict	No reasoning needed, lowest cost
Needs reasoning	dspy.ChainOfThought	Most tasks — default choice
Math or computation	dspy.ProgramOfThought	Counting, dates, calculations
Needs external tools	dspy.ReAct	API calls, web search, database access

import dspy

class MySignature(dspy.Signature):
    """Describe the task here."""
    # Adapt fields to user's task
    input_field: str = dspy.InputField(desc="description")
    output_field: str = dspy.OutputField(desc="description")

class MyProgram(dspy.Module):
    def __init__(self):
        self.predict = dspy.ChainOfThought(MySignature)

    def forward(self, **kwargs):
        return self.predict(**kwargs)

metrics.py

def metric(example, prediction, trace=None):
    """Score how good the AI output is.

    Args:
        example: Expected output (ground truth)
        prediction: What the AI actually produced
        trace: Optional trace for optimization

    Returns:
        float: Score between 0 and 1
    """
    # TODO: Implement task-specific metric
    return prediction.output_field == example.output_field

evaluate.py

import dspy
from dspy.evaluate import Evaluate
from program import MyProgram
from metrics import metric
from data import load_data

# Configure AI provider
lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)

# Load data
_, devset = load_data()

# Test quality
program = MyProgram()
evaluator = Evaluate(devset=devset, metric=metric, num_threads=4, display_progress=True)
score = evaluator(program)
print(f"Score: {score}")

optimize.py

import dspy
from program import MyProgram
from metrics import metric
from data import load_data

# Configure AI provider
lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)

# Load data
trainset, devset = load_data()

# Automatically improve the AI prompts
program = MyProgram()
optimizer = dspy.BootstrapFewShot(metric=metric, max_bootstrapped_demos=4)
optimized = optimizer.compile(program, trainset=trainset)

# Check improvement
from dspy.evaluate import Evaluate
evaluator = Evaluate(devset=devset, metric=metric, num_threads=4, display_progress=True)
score = evaluator(optimized)
print(f"Optimized score: {score}")

# Save
optimized.save("optimized.json")

main.py

import dspy
from program import MyProgram

# Configure AI provider
lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
dspy.configure(lm=lm)

# Load optimized version if available
program = MyProgram()
try:
    program.load("optimized.json")
    print("Loaded optimized program")
except FileNotFoundError:
    print("Running unoptimized program")

# Run
result = program(input_field="test input")
print(result)

Step 2b: Add API serving (if the user wants a web API)

If the user wants to serve their AI as a web API, add these files to the project structure:

$ARGUMENTS/
├── main.py          # Entry point — run your AI feature
├── program.py       # AI logic (DSPy module)
├── server.py        # FastAPI app — routes and startup
├── models.py        # Pydantic request/response schemas
├── config.py        # Environment configuration
├── 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
├── Dockerfile
└── .env.example

server.py

from contextlib import asynccontextmanager
import dspy
from fastapi import FastAPI
from pydantic import BaseModel, Field

from program import MyProgram

@asynccontextmanager
async def lifespan(app: FastAPI):
    lm = dspy.LM("openai/gpt-4o-mini")  # or "anthropic/claude-sonnet-4-5-20250929", etc.
    dspy.configure(lm=lm)
    app.state.program = MyProgram()
    try:
        app.state.program.load("optimized.json")
    except FileNotFoundError:
        pass
    yield

app = FastAPI(title="My AI API", lifespan=lifespan)

class QueryRequest(BaseModel):
    input_field: str = Field(..., min_length=1)

class QueryResponse(BaseModel):
    output_field: str

@app.post("/query", response_model=QueryResponse)
async def query(request: QueryRequest):
    result = app.state.program(input_field=request.input_field)
    return QueryResponse(output_field=result.output_field)

@app.get("/health")
async def health():
    return {: }

Adapt QueryRequest/QueryResponse fields to match the user's inputs/outputs.

Updated requirements.txt

dspy>=3.0
fastapi>=0.100
uvicorn[standard]
pydantic-settings>=2.0

.env.example

AI_MODEL_NAME=openai/gpt-4o-mini
AI_API_KEY=your-api-key-here

Step 3: Explain next steps

After generating the project, tell the user:

1. Fill in data.py with real training data (20+ examples). No real data yet? Use /ai-generating-data to generate synthetic training examples.
2. Run evaluate.py to see how well the AI works now
3. Run optimize.py to automatically improve quality
4. Run main.py to use the AI

Gotchas

• Claude omits .with_inputs() on Example objects. Every dspy.Example used in training must call .with_inputs("field1", "field2") to mark which fields are inputs vs expected outputs. Without this, the optimizer cannot distinguish inputs from labels and silently produces garbage demos.
• Claude generates the project but forgets to adapt field names. The scaffold uses input_field/output_field as placeholders. Claude must rename these to match the user's actual task (e.g., email/category for email classification). Leaving generic names produces a project that runs but confuses the user.
• Claude picks ChainOfThought for everything. For simple extraction or yes/no tasks, dspy.Predict is faster, cheaper, and equally accurate. Only use ChainOfThought when the task genuinely benefits from step-by-step reasoning.
• The metric function returns a boolean but the user needs a float. Claude often writes return prediction.answer == example.answer which returns True/False. DSPy handles booleans fine, but for weighted or partial-credit metrics, return a float between 0.0 and 1.0.
• Claude generates all files at once without checking the directory. Before scaffolding, verify the target directory does not already contain files. Overwriting existing code is destructive and hard to undo.

Additional resources

• For worked examples and complete project patterns, see examples.md
• For API signatures, module selection tables, and quick-reference checklists, see reference.md

Cross-references

Install any skill: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill <name>

• Improving accuracy after scaffolding to measure and optimize quality -- see /ai-improving-accuracy
• Generating data when you have no training examples yet -- see /ai-generating-data
• Serving APIs to put your AI behind web endpoints -- see /ai-serving-apis
• Signatures for defining input/output contracts -- see /dspy-signatures
• ChainOfThought for the default reasoning module -- see /dspy-chain-of-thought
• Install /ai-do if you do not have it — it routes any AI problem to the right skill and is the fastest way to work: npx skills add lebsral/DSPy-Programming-not-prompting-LMs-skills --skill ai-do