Holistic Linting: Resolution Workflows

This skill provides systematic resolution procedures for Python linting tools. Sub-agents executing linting resolution tasks MUST follow the appropriate workflow based on the linter reporting issues.

When to Use This Skill

Use this skill when you are a sub-agent assigned to resolve linting issues in a specific file. This skill provides detailed workflows for:

• Ruff (style, import, and code quality checks)
• Mypy (type checking)
• Pyright/Basedpyright (strict type checking)

Do NOT use this skill if you are an orchestrator. Orchestrators should use the holistic-linting-orchestrator skill for delegation workflows.

Common Resolution Methodology

All linter-specific workflows share these common steps. Apply them in order before the linter-specific procedures.

1. Load python3-development Skill

Before implementing any fixes:

Skill(skill: "python3-development:python3-development")

Motivation: Ensures fixes follow Python 3.11+ standards, modern typing patterns, and project conventions.

2. Suppression Gate (MANDATORY)

Before implementing any fix, verify it is a code change — not suppression. Each category below is an immediate STOP:

Inline suppression comments:

• Adding # noqa, # type: ignore, # pyright: ignore, # pylint: disable, or any suppression comment

Configuration-level suppression (equally forbidden):

• Adding a rule to [tool.ruff.lint] ignore = [...] in pyproject.toml
• Adding an entry to [tool.ruff.lint.per-file-ignores]
• Changing [tool.pyright] reportX = "warning" or any other severity downgrade
• Adding disable_error_code = [...] to [tool.mypy]
• Modifying any linter config file (pyproject.toml, ruff.toml, mypy.ini, .flake8, setup.cfg) to reduce the scope, severity, or applicability of a rule

Reason: A pyproject.toml severity downgrade achieves the same silencing effect as # type: ignore but at project scope, affecting all future code. Both are standards degradation. Neither is a code fix.

Deletion-as-resolution (equally forbidden):

• Removing a function, class, method, or test to eliminate the linting error within it
• Deleting lines of code solely because they contain a linting error

If any proposed fix falls into these categories: STOP.

When no code restructuring works (minimum 2 approaches attempted), document the constraint and return UNRESOLVED:

1. What you tried (each approach, minimum 2)
2. Why each approach failed (exact linter error produced)
3. The fundamental constraint (e.g., "ruff cannot evaluate sys.platform branches")

Return this as an UNRESOLVED item in your resolution report. The orchestrator will surface it to the user for a human decision on whether to suppress, reconfigure, or accept the limitation.

3. Check Architectural Context

Examine how this code fits into the broader system:

• What does this function/module do?
• How is it called by other code?
• Are there similar patterns elsewhere in the codebase?

Use Grep to find usage patterns:

uv run rg "function_name" --type py

4. Verify Resolution

After implementing fixes, rerun the appropriate linter on the primary file:

# For Ruff:
uv run ruff check /path/to/file.py

# For Mypy:
uv run mypy /path/to/file.py

# For Pyright/Basedpyright:
uv run pyright /path/to/file.py
# or
uv run basedpyright /path/to/file.py

Verify incidentally modified files: If you touched any file other than the primary target during the fix (e.g., added an import to a utility module, moved a function, updated a type alias), run the linter on those files too:

uv run ruff check /path/to/incidentally/modified/file.py
uv run mypy /path/to/incidentally/modified/file.py

All incidentally modified files must also produce zero errors before resolution is complete.

Record the before and after issue counts in the resolution report header:

**Issues before resolution:** N (from initial linter run)
**Issues after resolution:** 0 (from final linter run — must be 0 for all touched files)
**UNRESOLVED items:** N (must be explicitly listed even if 0)

Pre-existing issues detected: If the initial linter run reveals issues in files you did not touch — see Pre-Existing Issues Protocol. Every detected issue gets recorded. Silence is not permitted.

Ruff Resolution Workflow

When to use: Linting errors with ruff rule codes (E, F, W, B, S, I, UP, C90, N, etc.)

Resolution Process:

1. Research the Rule

   Use ruff's built-in documentation system:

   bashCopy

   ruff rule {RULE_CODE}
   

   Examples:

   bashCopy

   ruff rule F401  # unused-import
   ruff rule E501  # line-too-long
   ruff rule B006  # mutable-default-argument
   

   This command provides:

   • What the rule prevents (design principle)
   • When code violates the rule
   • Example of violating code
   • Example of resolved code
   • Configuration options

2. Read Rule Documentation Output

   The ruff rule output contains critical information:

   • Principle: Why this pattern is problematic
   • Bad Pattern: What code triggers the rule
   • Good Pattern: How to fix it correctly

   Motivation: Understanding the principle prevents similar issues in other locations.

3. Read the Affected Code

   Read the complete file containing the linting error:

   claudeCopy

   Read("/path/to/file.py")
   

   Focus on:

   • The line with the error
   • Surrounding context (5-10 lines before/after)
   • Related function/class definitions

4. Apply Common Methodology

   Follow steps 1-4 in the Common Resolution Methodology section above:

   • Load python3-development skill
   • Pass through Suppression Gate
   • Check Architectural Context
   • Verify Resolution

5. Implement Elegant Fix

   Apply the fix following these principles:

   • Address the root cause, not the symptom
   • Follow modern Python patterns from python3-development skill
   • Maintain or improve code readability
   • Consider performance and maintainability
   • Add comments only if the fix is non-obvious

Mypy Resolution Workflow

When to use: Type checking errors with mypy error codes (attr-defined, arg-type, return-value, etc.)

Resolution Process:

1. Research the Error Code

   Mypy errors contain error codes in brackets like [attr-defined] or [arg-type].

   Look up the error code in locally-cached documentation:

   claudeCopy

   Read("./references/mypy-docs/error_code_list.rst")
   Read("./references/mypy-docs/error_code_list2.rst")
   

   Search for the error code:

   bashCopy

   grep -n "error-code-{CODE}" ./references/mypy-docs/*.rst
   

   Motivation: Mypy error codes map to specific type safety principles. Understanding the principle prevents misunderstanding type relationships.

2. Read Error Code Documentation

   The mypy documentation explains:

   • What type safety principle is violated
   • When this is an error (type violations)
   • When this is NOT an error (valid patterns)
   • Example of error-producing code
   • Example of corrected code

   Key insight: Mypy errors often indicate misunderstanding about what types a function accepts or returns.

3. Trace Type Flow

   Follow the data flow to understand type relationships:

   a. Read the error location:

   claudeCopy

   Read("/path/to/file.py")
   

   b. Identify the type mismatch:

   • What type does mypy think the variable is?
   • What type does mypy expect?
   • Where does the variable get its type?

   c. Trace upstream:

   • Read function signatures
   • Check return type annotations
   • Review variable assignments

   d. Check library type stubs:

   • If the error involves a library, check its type stubs
   • Use python -c "import library; print(library.__file__)" to locate
   • Read .pyi stub files or py.typed marker

4. Apply Common Methodology

   Follow steps 1-4 in the Common Resolution Methodology section above:

   • Load python3-development skill
   • Pass through Suppression Gate
   • Check Architectural Context
   • Verify Resolution

5. Implement Elegant Fix

   Choose the appropriate fix strategy:

   Strategy A: Fix the type annotation (if annotation is wrong)

   pythonCopy

   # Before: Function returns dict but annotated as returning Response
   def get_data() -> Response:
       return {"key": "value"}  # mypy error: Incompatible return value type
   
   # After: Correct annotation to match actual return
   def get_data() -> dict[str, str]:
       return {"key": "value"}
   

   Strategy B: Fix the implementation (if annotation is correct)

   pythonCopy

   # Before: Function should return Response but returns dict
   def get_data() -> Response:
       return {"key": "value"}  # mypy error: Incompatible return value type
   
   # After: Fix implementation to return correct type
   def get_data() -> Response:
       return Response(data={"key": "value"})
   

   Strategy C: Add type narrowing (if type is conditional)

   pythonCopy

   # Before: Mypy can't prove value is not None
   def process(value: str | None) -> str:
       return value.upper()  # mypy error: Item "None" has no attribute "upper"
   
   # After: Add type guard
   def process(value: str | None) -> str:
       if value is None:
           raise ValueError("value cannot be None")
       return value.upper()
   

   Strategy D: Use TypeGuard for complex narrowing

   pythonCopy

   from typing import TypeGuard
   
   def is_valid_response(data: dict[str, Any]) -> TypeGuard[dict[str, str]]:
       return all(isinstance(v, str) for v in data.values())
   
   def process(data: dict[str, Any]) -> dict[str, str]:
       if not is_valid_response(data):
           raise ValueError("Invalid data format")
       return data  # mypy now knows this is dict[str, str]
   

Pyright/Basedpyright Resolution Workflow

When to use: Type checking errors with pyright diagnostic rules (reportGeneralTypeIssues, reportOptionalMemberAccess, reportUnknownVariableType, etc.)

Resolution Process:

1. Research the Diagnostic Rule

   Pyright errors reference diagnostic rule names like reportOptionalMemberAccess or reportGeneralTypeIssues.

   Look up the rule in basedpyright documentation:

   For rule settings and descriptions:

   Use MCP tools for documentation lookup (in order of preference):

   claudeCopy

   # Option 1 (Preferred): Use Ref MCP for high-fidelity documentation
   mcp__Ref__ref_search_documentation(query="basedpyright {RULE_NAME} diagnostic rule configuration")
   # Then read the URL from results:
   mcp__Ref__ref_read_url(url="<exact_url_from_search_results>")
   
   # Option 2: Use exa for code context if Ref doesn't have it
   mcp__exa__get_code_context_exa(query="basedpyright {RULE_NAME} diagnostic rule examples")
   
   # Fallback: Use WebFetch only if MCP tools don't work
   WebFetch(url="https://docs.basedpyright.com/latest/configuration/config-files/",
           prompt="Find documentation for diagnostic rule {RULE_NAME}")
   

   For features and PEP support:

   claudeCopy

   # Option 1 (Preferred): Use Ref MCP for high-fidelity documentation
   mcp__Ref__ref_search_documentation(query="basedpyright Python typing features PEP {RULE_NAME}")
   mcp__Ref__ref_read_url(url="<exact_url_from_search_results>")
   
   # Fallback: Use WebFetch only if MCP tools don't work
   WebFetch(url="https://docs.basedpyright.com/latest/getting_started/features/",
           prompt="Explain what Python typing features and PEPs are covered related to {RULE_NAME}")
   

   Motivation: Pyright is more strict than mypy in many areas. Understanding what the rule enforces helps identify whether the issue is a genuine type safety problem or overly strict checking.

2. Read Diagnostic Rule Documentation

   The basedpyright documentation explains:

   • What type safety issue the rule detects
   • Configuration levels (basic, standard, strict, all)
   • Whether the rule can be disabled per-project
   • Related typing features and PEPs

3. Read the Affected Code

   Read the complete file containing the type error:

   claudeCopy

   Read("/path/to/file.py")
   

   Focus on:

   • The exact line with the error
   • Type annotations in the surrounding function/class
   • Import statements for typing constructs

4. Understand the Type Inference Issue

   Pyright has sophisticated type inference. Common issues:

   Optional member access:

   pythonCopy

   # Error: reportOptionalMemberAccess
   value: str | None = get_value()
   result = value.upper()  # Error: 'value' could be 'None'
   

   Unknown variable type:

   pythonCopy

   # Error: reportUnknownVariableType
   result = some_function()  # some_function has no return type annotation
   

   Type narrowing not recognized:

   pythonCopy

   # Error: pyright doesn't recognize the narrowing
   value: int | str = get_value()
   if type(value) == int:  # Use isinstance() instead
       result = value + 1
   

5. Apply Common Methodology

   Follow steps 1-4 in the Common Resolution Methodology section above:

   • Load python3-development skill
   • Pass through Suppression Gate
   • Check Architectural Context
   • Verify Resolution

6. Implement Elegant Fix

   Choose the appropriate fix strategy:

   Strategy A: Add type narrowing guards

   pythonCopy

   # Before:
   def process(value: str | None) -> str:
       return value.upper()  # reportOptionalMemberAccess
   
   # After:
   def process(value: str | None) -> str:
       if value is None:
           raise ValueError("value is required")
       return value.upper()  # pyright knows value is str here
   

   Strategy B: Add missing type annotations

   pythonCopy

   # Before:
   def fetch_data():  # reportUnknownVariableType on callers
       return {"key": "value"}
   
   # After:
   def fetch_data() -> dict[str, str]:
       return {"key": "value"}
   

   Strategy C: Use assert for type narrowing

   pythonCopy

   # Before:
   value: int | str = get_value()
   result = value + 1  # reportGeneralTypeIssues
   
   # After:
   value: int | str = get_value()
   assert isinstance(value, int), "Expected int"
   result = value + 1  # pyright knows value is int
   

   Strategy D: Use typing.cast for complex cases

   pythonCopy

   from typing import cast
   
   # Before:
   data: dict[str, Any] = get_data()
   name: str = data["name"]  # reportUnknownVariableType
   
   # After (if you've validated data structure):
   from typing import TypedDict
   
   class UserData(TypedDict):
       name: str
       age: int
   
   data = cast(UserData, get_data())
   name: str = data["name"]  # pyright knows this is str
   

   When all strategies fail: Apply the Suppression Gate — document approaches tried and fundamental constraint, then return UNRESOLVED to the orchestrator. The pyproject.toml severity level is a project configuration decision, not a linting resolution action. Config changes require explicit user approval via the UNRESOLVED escalation path, not autonomous agent action.

Integration: Resolution Process with python3-development

All linter resolution workflows integrate with the python3-development skill at the implementation stage. This integration ensures:

1. Modern Python Patterns: Fixes use Python 3.11+ syntax

   • Native generics (list[str] not List[str])
   • Union syntax (str | None not Optional[str])
   • Structural pattern matching where appropriate

2. Idiomatic Code: Solutions follow Python best practices

   • Clear naming conventions
   • Appropriate use of comprehensions
   • Proper exception handling
   • Single Responsibility Principle

3. Type Safety: Type annotations are complete and accurate

   • Precise return types
   • Correct parameter types
   • Proper use of generics and protocols

4. Project Consistency: Fixes align with existing codebase patterns

   • Consistent with project's CLAUDE.md standards
   • Matches existing module organization
   • Follows project-specific conventions

Activation pattern:

[Identify linting issue] → [Research rule] → [Read code] → [Check architecture]
→ [Load python3-development skill] → [Implement elegant fix] → [Verify]

Related Skills

• holistic-linting - Core linting skill with linter detection and resource documentation
• holistic-linting-orchestrator - Orchestrator delegation workflows
• Pre-Existing Issues Protocol - Recording and triage pipeline for issues found outside current task scope