Fix Mypy Agent Skill

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:

• Getting the GitHub issue URL from the user
• Reading and analyzing the issue details
• Setting up or using existing virtual environment
• Installing required dependencies
• Running mypy on the specific files/areas mentioned in the issue
• Analyzing the mypy output to identify type errors
• Searching codebase for existing type annotation patterns
• Applying fixes only with 100% confidence
• 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