fix-mypy

name	fix-mypy
description	Automatically fix mypy type checking issues in azure-ai-ml package following Azure SDK Python patterns. Expects GitHub issue URL and optional virtual env path in the request. Format "fix mypy issue <issue-url> [using venv <path>]"

Fix MyPy Issues Skill

This skill automatically fixes mypy type checking errors 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 — type errors that will cause CI to fail. Leave optional/informational warnings as-is.

Overview

Intelligently fixes mypy 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 mypy on the specific files/areas mentioned in the issue
6. Analyzing the mypy output to identify type errors
7. Searching codebase for existing type annotation patterns
8. Applying fixes only with 100% confidence
9. Re-running mypy to verify fixes
10. Providing a summary of what was fixed

Running MyPy

Command:

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

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 MyPy Guide
• Tool Usage Guide
• Official MyPy Documentation
• MyPy Common Issues

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 mypy type checking 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 error codes 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 3.10+):
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 .

Important: Use Python 3.10 compatible environment for mypy checks.

Step 3: Identify Target Files (within activated venv)

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

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

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

# Run mypy on the full package, then filter output for files from the issue
azpysdk mypy .
# Review output for errors 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 MyPy (within activated venv)

⚠️ Ensure virtual environment is still activated before running:

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

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

Step 5: Analyze Type Errors

Parse the mypy output to identify:

• Error type and code (e.g., [arg-type], [return-value], [assignment])
• File path and line number
• Specific error description
• Expected vs actual types
• Cross-reference with the GitHub issue to ensure you're fixing the right problems

Step 6: Search for Existing Type Annotation Patterns

Before fixing, search the codebase for how similar types are annotated:

# Example: Search for similar function signatures
grep -r "def similar_function" sdk/ml/azure-ai-ml/ -A 5

# Search for type imports
grep -r "from typing import" sdk/ml/azure-ai-ml/

Use the existing type annotation 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 type errors with 100% confidence Use existing type annotation patterns as reference Follow Azure SDK Python type checking guidelines Add missing type hints Fix incorrect type annotations Make minimal, targeted changes

FORBIDDEN ACTIONS: Fix errors without complete confidence Create new files for solutions Import non-existent types or modules Add new dependencies or imports outside typing module Use # type: ignore without clear justification Change code logic to avoid type errors Delete code without clear justification

Step 8: Verify Fixes

Re-run mypy to ensure:

• The type error is resolved
• No new errors were introduced
• The code still functions correctly

Step 9: Summary

Provide a summary:

• GitHub issue being addressed
• Number of type errors fixed
• Number of errors remaining
• Types of fixes applied (e.g., added type hints, fixed return types)
• Any errors that need manual review

Common MyPy Issues and Fixes

Missing Type Hints

Error: Function is missing a type annotation

Fix: Add proper type hints:

from typing import Optional

def function_name(param: str, optional_param: Optional[int] = None) -> None:
    """Brief description."""
    pass

Argument Type Mismatch

Error: Argument 1 to "function" has incompatible type "X"; expected "Y"

Fix: Ensure the argument matches the expected type or add proper type conversion:

# If expecting str but passing int
value: str = str(int_value)

# Or fix the function signature if the type is incorrect
def function(param: Union[str, int]) -> None:
    pass

Return Type Mismatch

Error: Incompatible return value type (got "X", expected "Y")

Fix: Ensure the return type matches the annotation:

from typing import Optional

def get_value() -> Optional[str]:
    if condition:
        return "value"
    return None  # Not empty string if Optional

Type Annotation with Optional

Error: Item "None" of "Optional[X]" has no attribute "Y"

Fix: Add None check before accessing attributes:

from typing import Optional

def process(value: Optional[str]) -> str:
    if value is None:
        return ""
    return value.upper()

Union Types

Error: Argument has incompatible type

Fix: Use Union for multiple acceptable types:

from typing import Union

def function(param: Union[str, int, None]) -> str:
    if param is None:
        return ""
    return str(param)

List/Dict Type Annotations

Error: Need type annotation for variable

Fix: Add specific type annotations for collections:

from typing import List, Dict, Any

items: List[str] = []
config: Dict[str, Any] = {}

Example Workflow

# 0. Get issue details
# User provides: https://github.com/Azure/azure-sdk-for-python/issues/67890
# Issue mentions: mypy errors in azure/ai/ml/entities/job.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 mypy on the package and check output for target file
azpysdk mypy .
# Filter output for errors in $targetFile

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

# 5. Search for existing type annotation patterns
grep -r "from typing import" azure/ai/ml/ | findstr "operations"

# 6. Apply fixes to identified files

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

# 8. Report results

Notes

• Always read the existing code to understand type annotation patterns before making changes
• Prefer following existing patterns over adding new complex types
• Use Python 3.10+ compatible type hints (use Optional[X] instead of X | None)
• If unsure about a fix, mark it for manual review
• Some errors may require architectural changes - don't force fixes
• Test the code after fixing to ensure functionality is preserved
• Avoid using # type: ignore unless absolutely necessary and document why