Feat ds agent

[C-Neisinger]

Pull Request

Description

This pull request introduces a new agent specification for data science workflows, derived from Matt Dotsons hve-data-science extension, outlining the role, capabilities, methodology, and boundaries for a specialized data science agent. The specification provides detailed instructions, best practices, and guidelines for reproducible and rigorous data analysis, statistical modeling, and machine learning, while clearly stating what tasks are within and outside the agent's scope.

Data Science Agent Specification:

• Added .github/agents/data-science.agent.md defining the agent's role, objectives, and instructions for conducting data science analyses, including exploratory data analysis, statistical modeling, and machine learning workflows.

Core Capabilities & Methodology:

• Enumerated the agent's core capabilities such as EDA, hypothesis testing, model development, workflow management, and research, with explicit instructions for documentation and reproducibility.
• Provided methodological frameworks for decision-making, scientific rigor, reproducibility, ethical practice, and computational efficiency.

Boundaries & Communication:

• Detailed red flags and boundaries to prevent scope creep, ensuring the agent does not perform non-data-science tasks, production ML deployment, or infrastructure decisions.
• Outlined communication style, ideal inputs, and expected outputs to guide user interaction and deliverables.

Related Issue(s)

Type of Change

Select all that apply:

Code & Documentation:

• Bug fix (non-breaking change fixing an issue)
• [X ] 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:

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

Other:

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

Sample Prompts (for AI Artifact Contributions)

Exploratory Data Analysis:
"Analyze the fish.csv dataset to understand the relationship between body measurements and weight. Profile all columns, check for data quality issues, and identify which features are most predictive of weight.

Classification Task:
"Build a classification model for the [YOURDATA] dataset. Start with EDA to understand class distributions, then train and evaluate at least two different classifiers. Report precision, recall, and F1 scores per class."

Model Diagnostics:
"My regression model for predicting fish weight has high training accuracy but poor test performance. Diagnose potential overfitting, check residual patterns, and recommend improvements."

User Request:

Execution Flow:

Output Artifacts:

---.ipynb

# Fish Species Classification Analysis

This notebook analyzes a fish dataset containing physical measurements of different fish species and builds machine learning classifiers to predict species based on these measurements.

## Dataset Overview
- **Species**: Target variable with fish species names
- **Weight**: Fish weight in grams
- **Length1**: First length measurement
- **Length2**: Second length measurement  
- **Length3**: Third length measurement
- **Height**: Fish height
- **Width**: Fish width

## Objective
Create accurate classification models to predict fish species based on physical measurements.
# Data manipulation and analysis
import pandas as pd
import numpy as np

# Visualization
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('default')
sns.set_palette("husl")

# Machine learning
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, StratifiedKFold
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score
from sklearn.pipeline import Pipeline
...

---

Success Indicators:
*.ipynb created with reproducible code and visualizations. Users should closely validate all generated code and artifacts.

For detailed contribution requirements, see:

• Common Standards: docs/contributing/ai-artifacts-common.md - Shared standards for XML blocks, markdown quality, RFC 2119, validation, and testing
• Agents: docs/contributing/custom-agents.md - Agent configurations with tools and behavior patterns
• Prompts: docs/contributing/prompts.md - Workflow-specific guidance with template variables
• Instructions: docs/contributing/instructions.md - Technology-specific standards with glob patterns
• Skills: docs/contributing/skills.md - Task execution utilities with cross-platform scripts

Testing

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
• Link validation: npm run lint:md-links
• PowerShell analysis: npm run lint:ps

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

Additional Notes

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@microsoft-github-policy-service agree

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

Contributor License Agreement

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[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 76.19%. Comparing base (4bb857d) to head (4106cea).
⚠️ Report is 30 commits behind head on main.

Additional details and impacted files

@@             Coverage Diff             @@
##             main     #471       +/-   ##
===========================================
+ Coverage   52.44%   76.19%   +23.74%     
===========================================
  Files          17       20        +3     
  Lines        3110     3503      +393     
===========================================
+ Hits         1631     2669     +1038     
+ Misses       1479      834      -645     

Flag	Coverage Δ
pester	76.19% <ø> (+23.74%)	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.
see 20 files 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.