feat(agents): add data-science.agent.md with role, core capabilities, and methodology documentation

[C-Neisinger]

Pull Request

Description

This PR adds a new Data Science Agent, derived from Matt Dotsons hve-data-science extension, to the repository, providing a specialized Copilot agent for exploratory data analysis, statistical modeling, and machine learning workflows.

• feat(agents): add data-science.agent.md with role, core capabilities, and methodology documentation
  • Defines expert data scientist persona for EDA, hypothesis testing, and ML model development
  • Configures tools for notebook execution (execute/runNotebookCell, read/getNotebookSummary, read/readNotebookCellOutput)
  • Includes handoffs to task-planner and task-implementor agents
• docs(agents): document working principles covering scientific rigor, reproducibility, ethics, and computational efficiency
• docs(agents): outline decision-making framework and pre-recommendation checklist for data analysis tasks
• docs(agents): specify red flags, pitfalls, and explicit boundaries for agent scope

Related Issue(s)

Type of Change

Select all that apply:

Code & Documentation:

• Bug fix (non-breaking change fixing an issue)
• New feature (non-breaking change adding functionality)
• Breaking change (fix or feature causing existing functionality to change)
• Documentation update

Infrastructure & Configuration:

• GitHub Actions workflow
• Linting configuration (markdown, PowerShell, etc.)
• Security configuration
• DevContainer configuration
• Dependency update

AI Artifacts:

• Reviewed contribution with prompt-builder agent and addressed all feedback
• Copilot instructions (.github/instructions/*.instructions.md)
• Copilot prompt (.github/prompts/*.prompt.md)
• Copilot agent (.github/agents/*.agent.md)
• Copilot skill (.github/skills/*/SKILL.md)

Note for AI Artifact Contributors:

• Agents: Research, indexing/referencing other project (using standard VS Code GitHub Copilot/MCP tools), planning, and general implementation agents likely already exist. Review .github/agents/ before creating new ones.
• Skills: Must include both bash and PowerShell scripts. See Skills.
• Model Versions: Only contributions targeting the latest Anthropic and OpenAI models will be accepted. Older model versions (e.g., GPT-3.5, Claude 3) will be rejected.
• See Agents Not Accepted and Model Version Requirements.

Other:

• Script/automation (.ps1, .sh, .py)
• Other (please describe):

Sample Prompts (for AI Artifact Contributions)

User Request:

Analyze this dataset and identify any significant patterns or anomalies in the data.

Execution Flow:

1. Agent loads and inspects the dataset using notebook tools
2. Performs descriptive statistics and data profiling
3. Generates visualizations for pattern identification
4. Documents findings in .copilot-tracking/research/{{YYYY-MM-DD}}-{{analysis-name}}.md
5. Provides actionable recommendations with statistical evidence

Output Artifacts:

• Analysis documentation in .copilot-tracking/research/
• Visualizations and statistical summaries
• Recommendations grounded in data findings

Success Indicators:

• Statistical assumptions validated before applying methods
• Simple baselines established before complex approaches
• Reproducible analysis with documented methodology
• Clear communication of uncertainty and limitations

Testing

• Verified agent frontmatter conforms to agent schema
• Confirmed tool identifiers match repository conventions (edit/editFiles vs edit)
• Validated handoff prompts point to correct slash commands (/task-implement, /task-plan)

Checklist

Required Checks

• Documentation is updated (if applicable)
• Files follow existing naming conventions
• Changes are backwards compatible (if applicable)
• Tests added for new functionality (if applicable)

AI Artifact Contributions

• Used /prompt-analyze to review contribution
• Addressed all feedback from prompt-builder review
• Verified contribution follows common standards and type-specific requirements

Required Automated Checks

The following validation commands must pass before merging:

• Markdown linting: npm run lint:md
• Spell checking: npm run spell-check
• Frontmatter validation: npm run lint:frontmatter
• Skill structure validation: npm run validate:skills
• Link validation: npm run lint:md-links
• PowerShell analysis: npm run lint:ps
• Plugin freshness: npm run plugin:generate

Security Considerations

• This PR does not contain any sensitive or NDA information
• Any new dependencies have been reviewed for security issues
• Security-related scripts follow the principle of least privilege

GHCP Artifact Maturity

File	Type	Maturity	Notes
.github/agents/data-science.agent.md	Agent	✅ stable	All builds

---

🔬 - Generated by Copilot

[github-actions]

Dependency Review

✅ No vulnerabilities or license issues or OpenSSF Scorecard issues found.

Scanned Files

None

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 85.34%. Comparing base (fa116ea) to head (ab6eb1d).

Additional details and impacted files

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- Coverage   85.36%   85.34%   -0.03%     
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  Files          23       23              
  Lines        4475     4475              
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- Hits         3820     3819       -1     
- Misses        655      656       +1     

Flag	Coverage Δ
pester	85.34% <ø> (-0.03%)	⬇️

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see 1 file with indirect coverage changes

🚀 New features to boost your workflow:

• ❄️ Test Analytics: Detect flaky tests, report on failures, and find test suite problems.

[Copilot]

Pull request overview

Adds a new GitHub Copilot custom agent definition to support data science workflows (EDA, statistical modeling, and ML evaluation) within the HVE Core agent library.

Changes:

• Added a new .github/agents/data-science.agent.md agent spec describing responsibilities, methodology, boundaries, and expected outputs.
• Included agent handoffs intended to route to planning and implementation workflows.

[C-Neisinger]

@microsoft-github-policy-service agree company="Microsoft"

[Copilot]

Pull request overview

Copilot reviewed 1 out of 1 changed files in this pull request and generated no new comments.

[WilliamBerryiii]

Hi @C-Neisinger — Thanks for this contribution! A quick note on project conventions: we ask that pull requests be paired with a corresponding backlog issue for tracking purposes. I wasn't able to find an existing issue related to the data-science agent addition.

When you get a chance, could you file an issue describing the feature this PR addresses and link it here using Closes #NNN in the PR body? This helps the team track work across the backlog and ensures issues are automatically closed when PRs merge. Thanks! 🙏