programming
Python and R programming for data analysis, automation, and reproducible analytics
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install benchflow-ai-skillsbench-programming
Repository
Skill path: registry/terminal_bench_2.0/full_batch_reviewed/terminal_bench_2_0_rstan-to-pystan/environment/skills/programming
Python and R programming for data analysis, automation, and reproducible analytics
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Full Stack, Data / AI.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: benchflow-ai.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install programming into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/benchflow-ai/SkillsBench before adding programming to shared team environments
- Use programming for development workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: programming
description: Python and R programming for data analysis, automation, and reproducible analytics
version: "2.0.0"
sasmp_version: "2.0.0"
bonded_agent: 05-programming-expert
bond_type: PRIMARY_BOND
# Skill Configuration
config:
atomic: true
retry_enabled: true
max_retries: 3
backoff_strategy: exponential
code_execution: sandboxed
# Parameter Validation
parameters:
language_focus:
type: string
required: true
enum: [python, r, both]
default: python
skill_level:
type: string
required: true
enum: [beginner, intermediate, advanced, expert]
default: beginner
focus_area:
type: string
required: false
enum: [fundamentals, pandas, visualization, automation, ml, all]
default: all
# Observability
observability:
logging_level: info
metrics: [code_execution_success, test_pass_rate, performance_score]
---
# Programming for Data Analytics Skill
## Overview
Master Python and R programming for data analysis, from basic syntax to advanced data manipulation and automation.
## Core Topics
### Python for Data Analysis
- Python fundamentals and syntax
- Pandas for data manipulation
- NumPy for numerical computing
- Data cleaning and preprocessing
### R for Statistics
- R fundamentals and tidyverse
- dplyr for data transformation
- ggplot2 for visualization
- Statistical modeling in R
### Data Wrangling
- Reading various file formats (CSV, JSON, Excel, Parquet)
- Handling missing data
- Data type conversions
- Merging and reshaping datasets
### Automation & Reproducibility
- Jupyter notebooks and R Markdown
- Script automation and scheduling
- Version control with Git
- Environment management (conda, venv)
## Learning Objectives
- Write efficient Python code for data analysis
- Use R for statistical computing
- Automate repetitive data tasks
- Create reproducible analysis workflows
## Error Handling
| Error Type | Cause | Recovery |
|------------|-------|----------|
| ImportError | Missing package | pip/conda install package |
| SyntaxError | Invalid code | Check syntax, use linter |
| MemoryError | Data too large | Use chunking or dask |
| TypeError | Wrong data type | Explicit type conversion |
| FileNotFoundError | Missing file | Verify path, check permissions |
## Related Skills
- databases-sql (for data extraction)
- statistics (for statistical programming)
- advanced (for machine learning implementation)