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.
| Name | Description |
|---|---|
| eval-dataset-creator | Creates evaluation datasets and documentation for AI agent testing using interview-driven data curation |
| 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 planning agent with 6-phase conversational workflow. Guides planning against NIST AI RMF 1.0 as the default evaluation framework. Prepares 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 |
| Name | Description |
|---|---|
| rai-capture | Initiate responsible AI assessment planning from existing knowledge using the RAI Planner agent in capture mode |
| rai-plan-from-prd | Initiate responsible AI assessment planning from PRD/BRD artifacts using the RAI Planner agent in from-prd mode |
| rai-plan-from-security-plan | Initiate responsible AI assessment planning 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 |
| 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 review summary, 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, 6-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-risk-classification | Risk classification screening for Phase 2: prohibited uses gate, risk indicator assessment, and depth tier assignment |
| 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: NIST AI RMF 1.0 trustworthiness characteristics, subcategory mappings, and framework isolation architecture |
| 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. |