Coordinate seven AI agents to analyze data, generate code, and verify results.
npx skills add https://github.com/Token-Eater/skills-marketplace --skill ds-star
Please help me install this Agent Skill. Skill: ds-star Source: https://github.com/Token-Eater/skills-marketplace/tree/main/skills/ds-star Command: npx skills add https://github.com/Token-Eater/skills-marketplace --skill ds-star
DS-STAR automates and orchestrates multi-agent data science workflows to handle data analysis, coding, verification, and presentation within Claude Code, reducing manual overhead and enabling reproducible research.
Invoke the ds-star skill to analyze your data with seven specialized agents and return a structured final result.
High-intent search queries and answers about installing and using this skill.
Multi-agent data science workflows in Claude Code are automated by coordinating seven specialized AI agents to handle data analysis, code generation, verification, and final presentation. DS-STAR routes tasks to cost-efficient models and saves artifacts for reproducible research.
Data analysis costs are optimized by routing specific tasks to different Claude models like Haiku, Sonnet, and Opus. DS-STAR assigns code generation and verification steps to the most cost-efficient model available while maintaining robust results.
Reproducibility for exploratory data analysis and data cleaning pipelines is ensured by saving artifacts and supporting resume functionality. DS-STAR stores intermediate results to allow iterative refinement and verification across multiple data formats.
Python and pandas are required to run automated data analysis agents in the Claude Code environment. DS-STAR relies on these dependencies to execute generated code, verify results, and handle structured data formats.
Generated code results in automated data analysis are validated through a dedicated Verifier and Debugger agent. DS-STAR includes optional debug steps to check executable code outputs and ensure robust, structured final answers.
Multi-agent data analysis fails to produce reproducible results without artifact management because intermediate outputs are lost. DS-STAR prevents this by saving artifacts during data cleaning and code generation to support resume and iterative refinement.
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