dspy-bootstrap-fewshot

This skill should be used when the user asks to "bootstrap few-shot examples", "generate demonstrations", "use BootstrapFewShot", "optimize with limited data", "create training demos automatically", mentions "teacher model for few-shot", "10-50 training examples", or wants automatic demonstration generation for a DSPy program without extensive compute.

Zur Installation springen

Repository: majiayu000/claude-skill-registry
Letzte Quellaktivität: 23. Juni 2026 um 12:15
Erkannte Sprache von SKILL.md: Englisch
Sterne: 543
Forks: 85

Installationsoptionen

Standardmäßig ist der Prompt ausgewählt, der zuerst die Quelle prüft. Sie können zu einem direkten Befehl wechseln oder eine lokale Kopie herunterladen.

Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.

Ein direkter Befehl überspringt den Prüf-Prompt. Prüfen Sie die Quelle, bevor Sie ihn ausführen.

npx skills add https://github.com/majiayu000/claude-skill-registry --skill dspy-bootstrap-fewshot

Der Befehl bleibt in einer Zeile. Scrollen Sie horizontal, um ihn vor dem Kopieren vollständig zu prüfen.

Sie bevorzugen eine lokale Kopie? Laden Sie die Dateien herunter, die SkillsMP derzeit vorliegen.

Quelldateien prüfen

Lesen Sie SKILL.md und alle von SkillsMP angezeigten Begleitdateien, bevor Sie sich für eine Installation entscheiden.

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SKILL.md

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Zurück zur Suche Zurück zur Installation

Input	Type	Description
`program`	`dspy.Module`	Your DSPy program to optimize
`trainset`	`list[dspy.Example]`	Training examples
`metric`	`callable`	Evaluation function
`metric_threshold`	`float`	Numerical threshold for accepting demos (optional)
`max_bootstrapped_demos`	`int`	Max teacher-generated demos (default: 4)
`max_labeled_demos`	`int`	Max direct labeled demos (default: 16)
`max_rounds`	`int`	Max bootstrapping attempts per example (default: 1)
`teacher_settings`	`dict`	Configuration for teacher model (optional)

import dspy
from dspy.teleprompt import BootstrapFewShot

# Configure LMs
dspy.configure(lm=dspy.LM("openai/gpt-4o-mini"))

class QA(dspy.Module):
    def __init__(self):
        self.generate = dspy.ChainOfThought("question -> answer")
    
    def forward(self, question):
        return self.generate(question=question)

def validate_answer(example, pred, trace=None):
    return example.answer.lower() in pred.answer.lower()

optimizer = BootstrapFewShot(
    metric=validate_answer,
    max_bootstrapped_demos=4,
    max_labeled_demos=4,
    teacher_settings={'lm': dspy.LM("openai/gpt-4o")}
)

compiled_qa = optimizer.compile(QA(), trainset=trainset)

# Use optimized program
result = compiled_qa(question="What is photosynthesis?")

# Save for production (state-only, recommended)
compiled_qa.save("qa_optimized.json", save_program=False)

import dspy
from dspy.teleprompt import BootstrapFewShot
from dspy.evaluate import Evaluate
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class ProductionQA(dspy.Module):
    def __init__(self):
        self.cot = dspy.ChainOfThought("question -> answer")
    
    def forward(self, question: str):
        try:
            return self.cot(question=question)
        except Exception as e:
            logger.error(f"Generation failed: {e}")
            return dspy.Prediction(answer="Unable to answer")

def robust_metric(example, pred, trace=None):
    if not pred.answer or pred.answer == "Unable to answer":
        return 0.0
    return float(example.answer.lower() in pred.answer.lower())

def optimize_with_bootstrap(trainset, devset):
    """Full optimization pipeline with validation."""
    
    # Baseline
    baseline = ProductionQA()
    evaluator = Evaluate(devset=devset, metric=robust_metric, num_threads=4)
    baseline_score = evaluator(baseline)
    logger.info(f"Baseline: {baseline_score:.2%}")
    
    # Optimize
    optimizer = BootstrapFewShot(
        metric=robust_metric,
        max_bootstrapped_demos=4,
        max_labeled_demos=4
    )
    
    compiled = optimizer.compile(baseline, trainset=trainset)
    optimized_score = evaluator(compiled)
    logger.info(f"Optimized: {optimized_score:.2%}")
    
    if optimized_score > baseline_score:
        compiled.save("production_qa.json", save_program=False)
        return compiled
    
    logger.warning("Optimization didn't improve; keeping baseline")
    return baseline

name	dspy-bootstrap-fewshot
version	1.0.0
dspy-compatibility	3.1.2
description	This skill should be used when the user asks to "bootstrap few-shot examples", "generate demonstrations", "use BootstrapFewShot", "optimize with limited data", "create training demos automatically", mentions "teacher model for few-shot", "10-50 training examples", or wants automatic demonstration generation for a DSPy program without extensive compute.
allowed-tools	["Read","Write","Glob","Grep"]

name	dspy-bootstrap-fewshot
version	1.0.0
dspy-compatibility	3.1.2
description	This skill should be used when the user asks to "bootstrap few-shot examples", "generate demonstrations", "use BootstrapFewShot", "optimize with limited data", "create training demos automatically", mentions "teacher model for few-shot", "10-50 training examples", or wants automatic demonstration generation for a DSPy program without extensive compute.
allowed-tools	["Read","Write","Glob","Grep"]

dspy-bootstrap-fewshot

Installationsoptionen

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