gpt-lab

Agent Building

Benchmark and compare small GPTs for task-specific inference. Tests base, fine-tuned, and prompted models against shared eval datasets. Finds minimum viable model, compares fine-tuned vs prompted, and generates reports.

QUICK START

How to use this skill

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Prompt to paste

I want to install this Agent Skill for this project in Codex.

Source SKILL.md: https://github.com/majiayu000/claude-skill-registry/blob/HEAD/skills/ai-ml/gpt-lab/SKILL.md

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First, summarize what it does, its dependencies, license status if identifiable, and any risks. Show the exact files you propose to add under .agents/skills/gpt-lab/. Do not write files or run scripts until I approve.

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Copying this prompt does not install or run the skill. Review third-party files before use. Codex skill guide

GPT Lab

Benchmark and compare small GPTs trained by /create-gpt against prompted alternatives. Answers the key question: "Is fine-tuning worth it for this task?"

Quick Start

cd .pi/skills/gpt-lab

# Benchmark multiple models on a task
./run.sh benchmark --task qra-validator --models "qwen2.5-0.5b,qwen2.5-1.5b"

# Find the smallest model meeting a threshold
./run.sh find-minimum --task qra-validator --threshold 0.85

# Compare fine-tuned vs prompted
./run.sh compare --task qra-validator \
  --finetuned ../create-gpt/models/qra-validator/model.gguf \
  --prompted deepseek-v3.2

# Profile a single model
./run.sh profile --model ../create-gpt/models/qra-validator/model.gguf --samples 100

# Generate report
./run.sh report --task qra-validator --format markdown

Commands

./run.sh benchmark --task NAME --models "model1,model2,..."
./run.sh compare --task NAME --finetuned PATH --prompted MODEL_NAME
./run.sh find-minimum --task NAME --threshold FLOAT
./run.sh profile --model PATH --samples N
./run.sh report --task NAME [--format markdown|json]
./run.sh history --task NAME

Fine-Tuned vs Prompted Verdict

accuracy delta < -5%                          → NOT_WORTH_IT
accuracy delta >= -2% AND speedup >= 5x       → WORTH_IT
otherwise                                     → MARGINAL

Integration

/create-gpt: Trains the models that this skill benchmarks
/scillm: Provides prompted baseline via Chutes API
/prompt-lab: find-minimum pattern adapted from this skill
/classifier-lab: Benchmark engine pattern adapted from this skill