Optimizes LLM prompts for high-accuracy text classification through a systematic, evaluation-driven workflow.
The Sociology Prompt Optimizer is a specialized Claude Code skill designed for researchers performing complex text classification tasks such as sentiment analysis, topic modeling, and stance detection. It guides users through a structured 'reflective evolution' process, moving from initial task definition to deployment-ready prompts for batch API processing. By prioritizing clear instructions over few-shot examples and emphasizing rigorous error diagnosis, it helps researchers avoid local optima and achieve the high-precision results necessary for academic publications in Python or R environments.
Características Principales
01Independent optimization tracks for multi-dimensional classification
02Automated version control through integrated git history tracking
03Phase Memo generation for transparent research methodology documentation
04Multi-phase systematic workflow from task definition to deployment
053 GitHub stars
06Reflective evolution for evidence-based prompt iteration and error diagnosis
Casos de Uso
01Classifying large-scale sociological datasets for sentiment, topic, or framing
02Refining prompt performance to meet specific Macro-F1 score targets
03Developing academic codebooks and label definitions via exploratory data immersion
