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# Data Science
Data specification generation, Jupyter notebooks, and Streamlit dashboards
> [!CAUTION]
> This collection includes RAI (Responsible AI) agents and prompts that are **assistive tools only**. They do not replace qualified responsible AI review, ethics board oversight, or established organizational RAI governance processes. All AI-generated RAI assessments, impact analyses, and recommendations **must** be reviewed and validated by qualified professionals before use. AI outputs may contain inaccuracies, miss critical risk categories, or produce recommendations that are incomplete or inappropriate for your context.
## Overview
Generate data specifications, Jupyter notebooks, and Streamlit dashboards from natural language descriptions. Evaluate AI-powered data systems against Responsible AI standards. This collection includes specialized agents for data science workflows in Python and RAI assessment.
> [!CAUTION]
> The RAI agents and prompts in this collection are **assistive tools only**. They do not replace qualified human review, organizational RAI review boards, or regulatory compliance programs. All AI-generated RAI artifacts **must** be reviewed and validated by qualified professionals before use. AI outputs may contain inaccuracies, miss critical risks, or produce recommendations that are incomplete or inappropriate for your context.
<!-- BEGIN AUTO-GENERATED ARTIFACTS -->
### Chat Agents
| Name | Description |
|------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **gen-data-spec** | Generate comprehensive data dictionaries, machine-readable data profiles, and objective summaries for downstream analysis (EDA notebooks, dashboards) through guided discovery |
| **gen-jupyter-notebook** | Create structured exploratory data analysis Jupyter notebooks from available data sources and generated data dictionaries |
| **gen-streamlit-dashboard** | Develop a multi-page Streamlit dashboard |
| **rai-planner** | Responsible AI assessment agent with 5-phase conversational workflow. Evaluates AI systems against Microsoft RAI Standard v2 and NIST AI RMF 1.0. Produces RAI security model, impact assessment, control surface catalog, and dual-format backlog handoff. |
| **researcher-subagent** | Research subagent using search tools, read tools, fetch web page, github repo, and mcp tools |
| **test-streamlit-dashboard** | Automated testing for Streamlit dashboards using Playwright with issue tracking and reporting |
### Prompts
| Name | Description |
|---------------------------------|------------------------------------------------------------------------------------------------------------------------------------------|
| **rai-capture** | Initiate a responsible AI assessment from existing knowledge using the RAI Planner agent in capture mode |
| **rai-plan-from-prd** | Initiate a responsible AI assessment from PRD/BRD artifacts using the RAI Planner agent in from-prd mode |
| **rai-plan-from-security-plan** | Initiate a responsible AI assessment from a completed Security Plan using the RAI Planner agent in from-security-plan mode (recommended) |
| **synth-data-generate** | Generate comprehensive synthetic data for any specified subject with realistic patterns and relationships |
### Instructions
| Name | Description |
|----------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| **coding-standards/python-script** | Instructions for Python scripting implementation |
| **coding-standards/uv-projects** | Create and manage Python virtual environments using uv commands |
| **rai-planning/rai-backlog-handoff** | RAI review and backlog handoff for Phase 6: review rubric, RAI scorecard, dual-format backlog generation |
| **rai-planning/rai-capture-coaching** | Exploration-first questioning techniques for RAI capture mode adapted from Design Thinking research methods |
| **rai-planning/rai-identity** | RAI Planner identity, 5-phase orchestration, state management, and session recovery |
| **rai-planning/rai-impact-assessment** | RAI impact assessment for Phase 5: control surface taxonomy, evidence register, tradeoff documentation, and work item generation |
| **rai-planning/rai-security-model** | RAI security model analysis for Phase 4: AI STRIDE extensions, dual threat IDs, ML STRIDE matrix, and security model merge protocol |
| **rai-planning/rai-standards** | Embedded RAI standards for Phase 3: Microsoft RAI Standard v2 principles and NIST AI RMF subcategory mappings |
| **shared/hve-core-location** | Important: hve-core is the repository containing this instruction file; Guidance: if a referenced prompt, instructions, agent, or script is missing in the current directory, fall back to this hve-core location by walking up this file's directory tree. |
<!-- END AUTO-GENERATED ARTIFACTS -->
## Install
```bash
copilot plugin install data-science@hve-core
```
## Agents
| Agent | Description |
|--------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| gen-data-spec | Generate comprehensive data dictionaries, machine-readable data profiles, and objective summaries for downstream analysis (EDA notebooks, dashboards) through guided discovery |
| gen-jupyter-notebook | Create structured exploratory data analysis Jupyter notebooks from available data sources and generated data dictionaries |
| gen-streamlit-dashboard | Develop a multi-page Streamlit dashboard |
| test-streamlit-dashboard | Automated testing for Streamlit dashboards using Playwright with issue tracking and reporting |
| rai-planner | Responsible AI assessment agent with 5-phase conversational workflow. Evaluates AI systems against Microsoft RAI Standard v2 and NIST AI RMF 1.0. Produces RAI security model, impact assessment, control surface catalog, and dual-format backlog handoff. - Brought to you by microsoft/hve-core |
| researcher-subagent | Research subagent using search tools, read tools, fetch web page, github repo, and mcp tools |
## Commands
| Command | Description |
|-----------------------------|------------------------------------------------------------------------------------------------------------------------------------------|
| rai-capture | Initiate a responsible AI assessment from existing knowledge using the RAI Planner agent in capture mode |
| rai-plan-from-prd | Initiate a responsible AI assessment from PRD/BRD artifacts using the RAI Planner agent in from-prd mode |
| rai-plan-from-security-plan | Initiate a responsible AI assessment from a completed Security Plan using the RAI Planner agent in from-security-plan mode (recommended) |
| synth-data-generate | Generate comprehensive synthetic data for any specified subject with realistic patterns and relationships |
## Instructions
| Instruction | Description |
|------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| python-script.instructions | Instructions for Python scripting implementation - Brought to you by microsoft/hve-core |
| uv-projects.instructions | Create and manage Python virtual environments using uv commands |
| rai-backlog-handoff.instructions | RAI review and backlog handoff for Phase 6: review rubric, RAI scorecard, dual-format backlog generation |
| rai-identity.instructions | RAI Planner identity, 5-phase orchestration, state management, and session recovery - Brought to you by microsoft/hve-core |
| rai-impact-assessment.instructions | RAI impact assessment for Phase 5: control surface taxonomy, evidence register, tradeoff documentation, and work item generation |
| rai-security-model.instructions | RAI security model analysis for Phase 4: AI STRIDE extensions, dual threat IDs, ML STRIDE matrix, and security model merge protocol |
| rai-standards.instructions | Embedded RAI standards for Phase 3: Microsoft RAI Standard v2 principles and NIST AI RMF subcategory mappings |
| rai-capture-coaching.instructions | Exploration-first questioning techniques for RAI capture mode adapted from Design Thinking research methods - Brought to you by microsoft/hve-core |
| hve-core-location.instructions | Important: hve-core is the repository containing this instruction file; Guidance: if a referenced prompt, instructions, agent, or script is missing in the current directory, fall back to this hve-core location by walking up this file's directory tree. |
---
> Source: [microsoft/hve-core](https://github.com/microsoft/hve-core)microsoft/hve-core
Publicmirrored fromhttps://github.com/microsoft/hve-coreAvailable
plugins/data-science/README.md
97lines · modepreview