Skillzwave Logo
Skillzwave

senior-data-scientist

579 stars 112 forks
0
F

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.

CommandsAgentsMarketplace
#Data Scientist#claude-ai#Model deployment#production patterns#claudecode-subagents#claude-ai-skills#model#advanced

Third-Party Agent Skill: Review the code before installing. Agent skills execute in your AI assistant's environment and can access your files. Learn more about security

Installation for Agentic Skill

View all platforms →
skilz install alirezarezvani/claude-skills/senior-data-scientist
skilz install alirezarezvani/claude-skills/senior-data-scientist --agent opencode
skilz install alirezarezvani/claude-skills/senior-data-scientist --agent codex
skilz install alirezarezvani/claude-skills/senior-data-scientist --agent gemini

First time? Install Skilz: pip install skilz

Works with 22+ AI coding assistants

Cursor, Aider, Copilot, Windsurf, Qwen, Kimi, and more...

View All Agents
Download Agent Skill ZIP

Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/alirezarezvani/claude-skills
2. Copy the agent skill directory:
cp -r claude-skills/engineering-team/senior-data-scientist ~/.claude/skills/
View on GitHub

Need detailed installation help? Check our platform-specific guides:

Claude Desktop Guide Claude Code Guide Troubleshooting

Related Agentic Skills

automating-mail

by SpillwaveSolutions

Automates Apple Mail via JXA with AppleScript dictionary discovery. Use when asked to "automate email", "send mail via script", "JXA Mail automatio...

100
Marketplace

automating-reminders

by SpillwaveSolutions

Automates Apple Reminders using JavaScript for Automation (JXA). Use when asked to "create reminders programmatically", "automate reminder lists", ...

100
Marketplace

mastering-postgresql

by SpillwaveSolutions

PostgreSQL development for Python with full-text search (tsvector, tsquery, BM25 via pg_search), vector similarity (pgvector with HNSW/IVFFlat), JS...

100
Marketplace

automating-contacts

by SpillwaveSolutions

Automates macOS Contacts via JXA with AppleScript dictionary discovery. Use when asked to "automate contacts", "JXA contacts automation", "macOS ad...

99
Marketplace

Agentic Skill Details

Owner
alirezarezvani (GitHub)
Repository
claude-skills
Stars
579
Forks
112
Type
Other
Meta-Domain
Primary Domain
Market Score
0
Agentic Skill Grades →

Agent Skill Grade

F
Score: 35/100 Click to see breakdown

Score Breakdown

Spec Compliance
12/15
PDA Architecture
8/30
Ease of Use
7/25
Writing Style
3/10
Utility
4/20
Modifiers: +1

Areas to Improve

  • Identical Reference Files
  • Fictional Quick Start Scripts
  • Marketing Over Instruction

Recommendations

  • Focus on improving Pda (currently 8/30)
  • Focus on improving Ease Of Use (currently 7/25)
  • Focus on improving Writing Style (currently 3/10)

Graded: 2026-01-24

Developer Feedback

I just looked through senior-data-scientist and noticed the spec is pretty minimal—almost feels like it's still finding its voice. Given the 35/100, I'm curious what the original vision was before we started refining it.

Links:

The TL;DR

You're at 35/100, solidly F territory. This is based on Anthropic's skill evaluation rubric across five pillars. Your strongest area is Spec Compliance (12/15)—the YAML structure is valid and the naming convention is correct. But you're getting hit hard on Utility (4/20) and Progressive Disclosure (8/30), which are the backbone of a functional skill.

What's Working Well

  • Clean metadata structure: Your YAML frontmatter is valid with required fields properly formatted—this is the foundation everything else builds on.
  • Follows naming conventions: hyphen-case format is correct and consistent with the skill ecosystem standards.
  • Reference architecture attempted: You're thinking layered—having separate files for statistical methods, experiment design, and feature engineering shows you understand the intent of Progressive Disclosure.

The Big One: Identical Reference Files Kill Utility

Here's the thing—all three reference files (statistical_methods_advanced.md, experiment_design_frameworks.md, feature_engineering_patterns.md) contain the identical boilerplate content. Same "Production-First Design" section, same "Pattern 1: Distributed Processing," everything copy-pasted.

Why this matters: The whole point of layered references is to provide progressively deeper, specialized knowledge. Right now they're just repetition, so a user gets zero additional value from clicking through.

The fix: Make these actually di...

AI-Detected Topics

Extracted using NLP analysis

Data Scientist claude-ai Model deployment production patterns claudecode-subagents claude-ai-skills model advanced claude-skills Senior Data

Report Security Issue

Found a security vulnerability in this agent skill?