Does this skill support multi-dimensional coding?

Yes, it allows for multiple classification dimensions (like sentiment and rhetorical style) to be optimized simultaneously on their own independent tracks.

Do I need a pre-existing codebook to use this skill?

No. The skill includes an 'Exploratory Immersion' path that helps you develop a classification scheme and categories from scratch based on your raw data.

What is reflective evolution in prompt engineering?

Reflective evolution is a process of testing prompts on real data, diagnosing errors, and making targeted, evidence-based fixes rather than making random adjustments.

Can I use this for both Python and R projects?

Yes, the optimizer is designed to support researchers using either Python or R for batch API processing and data analysis.

How does it handle version control?

The skill utilizes git to track progress across phases, automatically committing the state of your prompts to provide a reliable audit trail without manual file renaming.

Sociology Prompt Optimizer

Name: Sociology Prompt Optimizer
Author: nealcaren

bynealcaren

•

数据科学与机器学习

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.

主要功能

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

使用场景

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

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nealcaren/sociology-skillset prompt-optimizer

For use in Claude.ai and ChatGPT

Download Skill