القائمة
Exploratory data analysis for data scientists — delegates to ds-eda-analyst agent. Use when profiling datasets, visualizing distributions, checking correlations, or detecting missing data and outliers.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Create a pull request with conventional commits and a structured description. Supports GitHub and Azure DevOps — platform is auto-detected from the git remote. Use when the user wants to open a PR, commit changes, or submit work for review.
Create a pull request with conventional commits and a structured description. Supports GitHub and Azure DevOps — platform is auto-detected from the git remote. Use when the user wants to open a PR, commit changes, or submit work for review.
Analyze meeting transcripts — extract decisions, action items, and create SSOT documents. Use when processing standup notes, stakeholder reviews, or sprint meetings.
Save valuable insights from the current session to storage. Use when you've discovered decisions, patterns, or gotchas worth remembering across sessions.
Generate comprehensive, production-ready README.md by analyzing codebase with explorer + documenter agents. Use when creating or updating project documentation.
Sync project context to copilot-instructions.md by analyzing codebase patterns and conventions. Use after adding agents, commands, or significant architecture changes.
| name | eda |
| description | Exploratory data analysis for data scientists — delegates to ds-eda-analyst agent. Use when profiling datasets, visualizing distributions, checking correlations, or detecting missing data and outliers. |
Profile and explore a dataset to understand structure, distributions, and relationships
/ds-eda <dataset-or-description>
/ds-eda data/customers.csv
/ds-eda "Explore churn dataset — check missing values, distributions, and class balance"
/ds-eda "Correlation analysis between features and loan default"
/ds-eda notebooks/raw_data_exploration.ipynb
pandas and data-visualization domains| Agent | Role |
|---|---|
ds-eda-analyst | Primary — profiling, distributions, correlations, outliers |
ds-statistician | Escalation — when statistical significance tests are needed |
ds-feature-engineer | Escalation — when missing data requires Pipeline imputation |
pandas — DataFrame profiling, groupby, reshapingdata-visualization — EDA charts, heatmaps, distribution plotsstatistical-analysis — normality tests, outlier detectionThe agent generates a profiling report with visualizations, null-handling recommendations, and a baseline model summary.