fix-pylint

name	fix-pylint
description	Automatically fix pylint issues in azure-ai-ml package following Azure SDK Python guidelines and existing code patterns. Expects GitHub issue URL and optional virtual env path in the request. Format "fix pylint issue <issue-url> [using venv <path>] Use when this capability is needed.
metadata	{"author":"Azure"}

Fix Pylint Issues Skill

This skill automatically fixes pylint warnings in the azure-ai-ml package by analyzing existing code patterns and applying fixes with 100% confidence based on GitHub issues.

Scope: Fix only mandatory/blocking issues — warnings that will cause CI to fail. Leave optional/informational warnings as-is.

Overview

Intelligently fixes pylint issues by:

1. Getting the GitHub issue URL from the user
2. Reading and analyzing the issue details
3. Setting up or using existing virtual environment
4. Installing required dependencies
5. Running pylint on the specific files/areas mentioned in the issue
6. Analyzing the pylint output to identify warnings
7. Searching codebase for existing patterns to follow
8. Applying fixes only with 100% confidence
9. Re-running pylint to verify fixes
10. Providing a summary of what was fixed

Running Pylint

Command:

cd sdk/ml/azure-ai-ml
azpysdk pylint .

Note: azpysdk runs at the package level only. To focus on specific files, run the full check and filter the output by file path.

Reference Documentation

• Azure SDK Python Pylint Guidelines
• Official Pylint Documentation
• Azure SDK Python Pylint Guide
• Tool Usage Guide

Fixing Strategy

Step 0: Get GitHub Issue Details

Check if user provided in their request:

• GitHub issue URL (look for https://github.com/Azure/azure-sdk-for-python/issues/... in user's message)
• Virtual environment path (look for phrases like "using venv", "use env", "virtual environment at", or just the venv name)

If GitHub issue URL is missing: Ask: "Please provide the GitHub issue URL for the pylint problems you want to fix."

If virtual environment is missing: Ask: "Do you have an existing virtual environment path, or should I create 'env'?"

Once you have the issue URL: Read the issue to understand which files/modules and specific warnings to fix.

Step 1: CRITICAL - Activate Virtual Environment FIRST

IMMEDIATELY activate the virtual environment before ANY other command:

# Activate the provided virtual environment (e.g., envml, env, venv)
.\<venv-name>\Scripts\Activate.ps1

# If creating new virtual environment:
python -m venv env
.\env\Scripts\Activate.ps1

⚠️ IMPORTANT: ALL subsequent commands MUST run within the activated virtual environment. Never run commands outside the venv.

Step 2: Install Dependencies (within activated venv)

# Navigate to azure-ai-ml directory (within activated venv)
cd sdk/ml/azure-ai-ml

# Install dev dependencies from dev_requirements.txt (within activated venv)
pip install -r dev_requirements.txt

# Install the package in editable mode (within activated venv)
pip install -e .

Step 3: Identify Target Files (within activated venv)

Based on the GitHub issue details, determine which files to check:

Option A - Run pylint on the package and filter output:

# Ensure you're in azure-ai-ml directory (within activated venv)
cd sdk/ml/azure-ai-ml

# Run pylint on the full package, then filter output for files from the issue
azpysdk pylint .
# Review output for warnings in the specific files/modules mentioned in the issue

Option B - Check modified files (if no specific target):

git diff --name-only HEAD | Select-String "sdk/ml/azure-ai-ml"
git diff --cached --name-only | Select-String "sdk/ml/azure-ai-ml"

Step 4: Run Pylint (within activated venv)

⚠️ Ensure virtual environment is still activated before running:

# Navigate to azure-ai-ml directory
cd sdk/ml/azure-ai-ml

# Run pylint on the package (within activated venv)
azpysdk pylint .
# Filter output for the specific files/modules from the issue

Step 5: Analyze Warnings

Parse the pylint output to identify:

• Warning type and code (e.g., C0103, W0212, R0913)
• File path and line number
• Specific issue description
• Cross-reference with the GitHub issue to ensure you're fixing the right problems

Step 6: Search for Existing Patterns

Before fixing, search the codebase for how similar issues are handled:

# Example: Search for similar function patterns
grep -r "pattern" sdk/ml/azure-ai-ml/

Use the existing code patterns to ensure consistency.

Step 7: Apply Fixes (ONLY if 100% confident)

Fix only mandatory/blocking issues. Skip optional or informational warnings that do not cause CI failure.

ALLOWED ACTIONS: Fix warnings with 100% confidence Use existing file patterns as reference Follow Azure SDK Python guidelines Make minimal, targeted changes

FORBIDDEN ACTIONS: Fix warnings without complete confidence Create new files for solutions Import non-existent modules Add new dependencies or imports Make unnecessary large changes Change code style without clear reason Delete code without clear justification

Step 8: Verify Fixes

Re-run pylint to ensure:

• The warning is resolved
• No new warnings were introduced
• The code still functions correctly

Step 9: Summary

Provide a summary:

• GitHub issue being addressed
• Number of warnings fixed
• Number of warnings remaining
• Types of fixes applied
• Any warnings that need manual review

Common Pylint Issues and Fixes

Missing Docstrings

Warning: C0111: Missing module/function/class docstring

Fix: Add proper docstring following Azure SDK style:

def function_name(param: str) -> None:
    """Brief description of function.
    
    :param param: Description of parameter
    :type param: str
    :return: None
    """

Too Many Arguments

Warning: R0913: Too many arguments

Fix: Consider grouping related parameters into a configuration object or using keyword-only arguments.

Line Too Long

Warning: C0301: Line too long

Fix: Break line at logical points following PEP 8 style.

Unused Import

Warning: W0611: Unused import

Fix: Remove the unused import statement.

Invalid Name

Warning: C0103: Invalid name

Fix: Rename to follow snake_case for functions/variables, PascalCase for classes.

Example Workflow

# 0. Get issue details
# User provides: https://github.com/Azure/azure-sdk-for-python/issues/12345
# Issue mentions: pylint warnings in azure/ai/ml/operations/job_operations.py

# 1. CRITICAL - Activate virtual environment FIRST
.\<venv-name>\Scripts\Activate.ps1  # Use the venv name provided by user
cd sdk/ml/azure-ai-ml
pip install -r dev_requirements.txt
pip install -e .

# 2. Identify target from issue
$targetFile = "azure/ai/ml/operations/job_operations.py"

# 3. Run pylint on the package and check output for target file
azpysdk pylint .
# Filter output for warnings in $targetFile

# 4. Analyze output and identify fixable issues
# Cross-reference with GitHub issue #12345

# 5. Search for existing patterns in codebase
Get-ChildItem -Recurse azure/ai/ml/ | Select-String "similar_pattern"

# 6. Apply fixes to identified files

# 7. Re-run pylint to verify
azpysdk pylint .

# 8. Report results

Notes

• Always read the existing code to understand patterns before making changes
• Prefer following existing patterns over strict rule compliance
• If unsure about a fix, mark it for manual review
• Some warnings may require architectural changes - don't force fixes
• Test the code after fixing to ensure functionality is preserved

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Source: Azure/azure-sdk-for-python — distributed by TomeVault.