data-scientist

by code-yeongyu 68k Other Updated Aug 17, 2026
Data ⚠ No license
data-scientist skill by code-yeongyu
data-scientist — Data skill by code-yeongyu

data-scientist is an open-source data skill for Claude Code and compatible agents, published by code-yeongyu. Its author describes it as: “Expert data processing specialist with intelligent DuckDB/Polars selection for maximum performance. Always includes numpy, never uses pandas, runs everything through uv. Triggers: 'analyze the data', 'analyze this fil…”. The project has 68k stars on GitHub and ships custom license terms, so review them before reuse. Add it to your setup with `git clone https://github.com/code-yeongyu/oh-my-openagent ~/.claude/skills/data-scientist`.

What data-scientist does

Performance-obsessed data scientist with expertise in: - Intelligent tool selection: DuckDB vs Polars based on operation characteristics - Zero-copy data interchange via Apache Arrow - Memory-efficient processing for datasets exceeding RAM - SQL and DataFrame API mastery for analytical workloads

Installation

Add data-scientist to your agent with:

git clone https://github.com/code-yeongyu/oh-my-openagent ~/.claude/skills/data-scientist

Always review a skill's source before installing it. This command comes from the skill's public repository; the linked repo is the source of truth for exact setup steps.

What's inside

The SKILL.md for data-scientist is organised into these sections:

  • Role & Expertise
  • Environment Setup
  • Core Principles
  • ABSOLUTE RULES
  • Standard Package Pattern
  • Tool Selection Logic
  • Decision Tree (Apply in Order)
  • Quick Reference
  • Essential Patterns
  • DuckDB Direct File Query
  • Polars Lazy Evaluation
  • Zero-Copy DuckDB → Polars

When to use it

Reach for data-scientist when you want data help from your agent without writing the same instructions every session. Load the skill and the agent picks it up automatically for relevant tasks.

Strengths

  • Ships in code-yeongyu/oh-my-openagent, an established project with 67,982 GitHub stars
  • Actively maintained (recent commits)

Things to check

  • No license declared — confirm terms with the author before reuse

Topics

aiai-agentsanthropicchatgptclaudeclaude-skillscodexcursorgeminiideopenaiopencodeorchestrationtuitypescript

Frequently asked questions

What does data-scientist do?
Expert data processing specialist with intelligent DuckDB/Polars selection for maximum performance. Always includes numpy, never uses pandas, runs everything through uv. Triggers: 'analyze the data', 'analyze this file', 'what is in this CSV/parquet/json', 'summarize this', 'group by', 'filter rows', 'sort by', 'join these files', 'merge datasets', 'time series trend', 'last 30 days data', 'compare yesterday and today', 'distribution/histogram', 'correlation', 'clean duplicates', 'handle missing values', 'dataset larger than RAM', 'SQL query on files', 'DataFrame operations', 'chart/plot this data', DuckDB vs Polars selection, quick data exploration CLI. NOT for plain text/code inspection, configs, or tiny inline math.
How do I install data-scientist?
Run git clone https://github.com/code-yeongyu/oh-my-openagent ~/.claude/skills/data-scientist in your agent, then reload your skills. Review the source at https://github.com/code-yeongyu/oh-my-openagent before installing.
Is data-scientist free to use?
data-scientist is publicly available on GitHub, but code-yeongyu has not declared a standard open-source license (Other). Confirm the terms with the author before reusing it.
Where does data-scientist come from?
data-scientist ships inside code-yeongyu/oh-my-openagent, a repository that contains 46 catalogued skills in total. The repository's 67,982 GitHub stars apply to that whole collection, not to this skill on its own.

Related skills

More Data →

Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.

39k wshobson MIT

Anthropic's method for training harmless AI through self-improvement. Two-phase approach - supervised learning with self-critique/revision, then RLAIF (RL from AI Feedback). Use for safety alignment, reducing harmful outputs without human labels. Powers Claude's safety system.

Expert guidance for fine-tuning LLMs with Axolotl - YAML configs, 100+ models, LoRA/QLoRA, DPO/KTO/ORPO/GRPO, multimodal support

GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.

Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.