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Agent skill

Senior Data Scientist

by Raidriar7170 in Raidriar7170/hermes-skilleval

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.

MITAuto-check passedData & Analytics

Install Senior Data Scientist

skills CLI
$ npx skills add Raidriar7170/hermes-skilleval --skill senior-data-scientist -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Raidriar7170/hermes-skilleval senior-data-scientist --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Raidriar7170/hermes-skilleval.git skills-src && mkdir -p .claude/skills && cp -r skills-src/artifacts/v0.3/skillsbench-pilot/v0.3-stage2-input-package-candidate-20260701T010000Z/candidate-data/skill-sources/skillsbench__senior-data-scientist .claude/skills/senior-data-scientist && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
senior-data-scientist
GitHub stars
125
Used in
6 other repos
Token cost
~1.4k tokens
SKILL.md length
438 words
Files
1
Skills in repo
8
Repo updated
First seen
Licence
MIT

At a glance

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.

  • Works in 3 steps: Statistical Methods Advanced → Experiment Design Frameworks → Feature Engineering Patterns
  • Designing experiments
  • SKILL.md covers Quick Start, Core Expertise, Tech Stack and Reference Documentation, plus 7 more sections
  • Calls python, kubectl and docker

What it does

Senior Data Scientist is an agent skill from Raidriar7170/hermes-skilleval. 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.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Machine learning, A/B testing and Experimental design. It works with Python, SQL, scikit-learn and NumPy. The repository describes itself as: Verification-gated skill routing and self-improvement harness for Hermes-style agent skills. The licence is MIT.

When your agent uses it

  • Designing experiments
  • Building predictive models
  • Performing causal analysis
  • Driving data-driven decisions

Example prompts

  • “/senior-data-scientist”

Requirements

  • Python 3
  • Docker

Workflow steps

3 steps, taken from the step headings in SKILL.md.

  1. Statistical Methods Advanced
  2. Experiment Design Frameworks
  3. Feature Engineering Patterns

What it can do on your machine

Read from SKILL.md and the folder at commit 8f6a21e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python
    • kubectl
    • docker
    • helm

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use kubectl, docker and helm, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Senior Data Scientist loads about 1.4k tokens when it runs. Until then it costs about 123 tokens; SKILL.md has 438 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~123
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Raidriar7170/hermes-skilleval at commit 8f6a21e, republished under its MIT licence (© Raidriar7170). 438 words, ~1,407 tokens.

Download SKILL.mdSave it as .claude/skills/senior-data-scientist/SKILL.md (or your agent's skills folder).
name
senior-data-scientist
description
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.

Senior Data Scientist

World-class senior data scientist skill for production-grade AI/ML/Data systems.

Quick Start

Main Capabilities
bash
# Core Tool 1
python scripts/experiment_designer.py --input data/ --output results/

# Core Tool 2
python scripts/feature_engineering_pipeline.py --target project/ --analyze

# Core Tool 3
python scripts/model_evaluation_suite.py --config config.yaml --deploy

Core Expertise

This skill covers world-class capabilities in:

  • Advanced production patterns and architectures
  • Scalable system design and implementation
  • Performance optimization at scale
  • MLOps and DataOps best practices
  • Real-time processing and inference
  • Distributed computing frameworks
  • Model deployment and monitoring
  • Security and compliance
  • Cost optimization
  • Team leadership and mentoring

Tech Stack

Languages: Python, SQL, R, Scala, Go ML Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost Data Tools: Spark, Airflow, dbt, Kafka, Databricks LLM Frameworks: LangChain, LlamaIndex, DSPy Deployment: Docker, Kubernetes, AWS/GCP/Azure Monitoring: MLflow, Weights & Biases, Prometheus Databases: PostgreSQL, BigQuery, Snowflake, Pinecone

Reference Documentation

1. Statistical Methods Advanced

Comprehensive guide available in references/statistical_methods_advanced.md covering:

  • Advanced patterns and best practices
  • Production implementation strategies
  • Performance optimization techniques
  • Scalability considerations
  • Security and compliance
  • Real-world case studies
2. Experiment Design Frameworks

Complete workflow documentation in references/experiment_design_frameworks.md including:

  • Step-by-step processes
  • Architecture design patterns
  • Tool integration guides
  • Performance tuning strategies
  • Troubleshooting procedures
3. Feature Engineering Patterns

Technical reference guide in references/feature_engineering_patterns.md with:

  • System design principles
  • Implementation examples
  • Configuration best practices
  • Deployment strategies
  • Monitoring and observability

Production Patterns

Pattern 1: Scalable Data Processing

Enterprise-scale data processing with distributed computing:

  • Horizontal scaling architecture
  • Fault-tolerant design
  • Real-time and batch processing
  • Data quality validation
  • Performance monitoring
Pattern 2: ML Model Deployment

Production ML system with high availability:

  • Model serving with low latency
  • A/B testing infrastructure
  • Feature store integration
  • Model monitoring and drift detection
  • Automated retraining pipelines
Pattern 3: Real-Time Inference

High-throughput inference system:

  • Batching and caching strategies
  • Load balancing
  • Auto-scaling
  • Latency optimization
  • Cost optimization

Best Practices

Show full SKILL.md (181 more words)Show less
Development
  • Test-driven development
  • Code reviews and pair programming
  • Documentation as code
  • Version control everything
  • Continuous integration
Production
  • Monitor everything critical
  • Automate deployments
  • Feature flags for releases
  • Canary deployments
  • Comprehensive logging
Team Leadership
  • Mentor junior engineers
  • Drive technical decisions
  • Establish coding standards
  • Foster learning culture
  • Cross-functional collaboration

Performance Targets

Latency:

  • P50: < 50ms
  • P95: < 100ms
  • P99: < 200ms

Throughput:

  • Requests/second: > 1000
  • Concurrent users: > 10,000

Availability:

  • Uptime: 99.9%
  • Error rate: < 0.1%

Security & Compliance

  • Authentication & authorization
  • Data encryption (at rest & in transit)
  • PII handling and anonymization
  • GDPR/CCPA compliance
  • Regular security audits
  • Vulnerability management

Common Commands

bash
# Development
python -m pytest tests/ -v --cov
python -m black src/
python -m pylint src/

# Training
python scripts/train.py --config prod.yaml
python scripts/evaluate.py --model best.pth

# Deployment
docker build -t service:v1 .
kubectl apply -f k8s/
helm upgrade service ./charts/

# Monitoring
kubectl logs -f deployment/service
python scripts/health_check.py

Resources

  • Advanced Patterns: references/statistical_methods_advanced.md
  • Implementation Guide: references/experiment_design_frameworks.md
  • Technical Reference: references/feature_engineering_patterns.md
  • Automation Scripts: scripts/ directory

Senior-Level Responsibilities

As a world-class senior professional:

  1. Technical Leadership

    • Drive architectural decisions
    • Mentor team members
    • Establish best practices
    • Ensure code quality

  2. Strategic Thinking

    • Align with business goals
    • Evaluate trade-offs
    • Plan for scale
    • Manage technical debt

  3. Collaboration

    • Work across teams
    • Communicate effectively
    • Build consensus
    • Share knowledge

  4. Innovation

    • Stay current with research
    • Experiment with new approaches
    • Contribute to community
    • Drive continuous improvement

  5. Production Excellence

    • Ensure high availability
    • Monitor proactively
    • Optimize performance
    • Respond to incidents

© Raidriar7170, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in artifacts/v0.3/skillsbench-pilot/v0.3-stage2-input-package-candidate-20260701T010000Z/candidate-data/skill-sources/skillsbench__senior-data-scientist of Raidriar7170/hermes-skilleval.

Open the folder on GitHubat commit 8f6a21e

Used in 6 other repositories

We found 10 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 6 other GitHub owners. This page covers the copy in Raidriar7170/hermes-skilleval, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Senior Data Scientist next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Senior Data Scientist compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Senior Data Scientist this skillRaidriar7170/hermes-skilleval1256 repos~1.4kAutomated safety check: PassMIT
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Senior Data Scientistborghei/Claude-Skills881—~1.7kAutomated safety check: PassMIT
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Scikit Learn Machine Learningjaechang-hits/SciAgent-Skills3701 repos~4kAutomated safety check: PassBSD-3-Clause
Light Experiment CodingLight0305/Light-skills641—~2.3kAutomated safety check: PassMIT

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Works with

Categories

Questions about Senior Data Scientist

What does Senior Data Scientist do?

World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Senior Data Scientist is an agent skill from Raidriar7170/hermes-skilleval. World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics.

When should I use Senior Data Scientist?

Senior Data Scientist fits situations like: designing experiments; building predictive models; performing causal analysis; driving data-driven decisions.

How do I install Senior Data Scientist in Claude Code?

Run `npx skills add Raidriar7170/hermes-skilleval --skill senior-data-scientist -a claude-code`. Or copy the skill folder (artifacts/v0.3/skillsbench-pilot/v0.3-stage2-input-package-candidate-20260701T010000Z/candidate-data/skill-sources/skillsbench__senior-data-scientist in Raidriar7170/hermes-skilleval) into .claude/skills/senior-data-scientist in your project. Claude Code loads it when a task matches its description.

How do I install Senior Data Scientist in Codex?

Run `npx skills add Raidriar7170/hermes-skilleval --skill senior-data-scientist -a codex`. Or copy the skill folder (artifacts/v0.3/skillsbench-pilot/v0.3-stage2-input-package-candidate-20260701T010000Z/candidate-data/skill-sources/skillsbench__senior-data-scientist in Raidriar7170/hermes-skilleval) into .agents/skills/senior-data-scientist in your project. Codex loads it when a task matches its description.

Can I use Senior Data Scientist in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Raidriar7170/hermes-skilleval --skill senior-data-scientist -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/senior-data-scientist, .gemini/skills/senior-data-scientist, .github/skills/senior-data-scientist and .opencode/skills/senior-data-scientist in your project.

What does Senior Data Scientist need to run?

Going by SKILL.md and its folder, Senior Data Scientist needs the command-line tools its instructions call (python, kubectl, docker and helm). Our summary lists: Python 3; Docker.

Does Senior Data Scientist access the network?

SKILL.md contains no URLs. Its commands use docker, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Senior Data Scientist safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Senior Data Scientist use?

Senior Data Scientist is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Senior Data Scientist use?

About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Senior Data Scientist?

Skills that share tags, products or a category with Senior Data Scientist: Senior Data Scientist (alirezarezvani/claude-skills, 28k stars), Senior Data Scientist (borghei/Claude-Skills, 881 stars), Machine Learning Trading Strategy (HKUDS/Vibe-Trading, 35k stars) and Scikit Learn Machine Learning (jaechang-hits/SciAgent-Skills, 370 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Senior Data Scientist?

Raidriar7170 (a GitHub user) maintains it in Raidriar7170/hermes-skilleval, which has 125 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 26, 2026.

Source: Raidriar7170/hermes-skilleval on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.