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.github/copilot-instructions.md

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+# Instructions for GitHub Copilot
+
+Welcome to the laygo data processing library project! Your main goal is to help write clean, consistent, and well-tested Python code. Please adhere strictly to the following principles and conventions.
+
+## 1. General Coding Style & Principles
+* Language Version: All code should be compatible with Python 3.12+. This means you should use modern features like the | union operator for type hints and match statements where appropriate.
+
+* Formatting: Strictly follow the PEP 8 style guide. Use a linter like ruff or flake8 to enforce this.
+
+* Type Hinting: This is mandatory.
+
+* Provide type hints for all function/method arguments and return values.
+
+* Use modern type unions: int | None is preferred over Union[int, None].
+
+* Use types from collections.abc (e.g., Iterable, Callable, Iterator) for abstract collections.
+
+* Utilize the existing type aliases defined at the top of the file (e.g., PipelineFunction, InternalTransformer) for consistency.
+
+* Docstrings: All public classes, methods, and functions must have Google-style docstrings.
+
+* Include a brief one-line summary.
+
+* Provide a more detailed explanation if necessary.
+
+* Use Args:, Returns:, and Raises: sections to document parameters, return values, and exceptions.
+
+Example Docstring Template:
+
+```py
+def my_method(self, parameter_a: str, parameter_b: int | None = None) -> bool:
+    """A brief summary of what this method does.
+
+    A more detailed explanation of the method's behavior, its purpose,
+    and any important side effects or notes for the user.
+
+    Args:
+        parameter_a: Description of the first parameter.
+        parameter_b: Description of the optional second parameter.
+
+    Returns:
+        A description of the return value, explaining what True or False means.
+
+    Raises:
+        ValueError: If parameter_a has an invalid format.
+    """
+    # ... implementation ...
+```
+
+* When checking code, do not worry about whitespaces. There is a formatter in place that will handle that for you.
+
+* Don't add obvious comments. For example, avoid comments like # This is a loop or # Increment i by 1. Instead, focus on explaining why something is done, not what is done.
+
+* Avoid using comments to disable code. If a piece of code is not needed, it should be removed entirely. Use version control to track changes instead.
+
+* `# type: ignore` comments are ok only if there are no other options. For example, you know that the underlying code works correctly, but it's just a limitation of python in play.
+
+## 2. Naming Conventions
+* Consistency in naming is crucial for readability.
+
+* Functions & Methods: Use snake_case (e.g., build_chunk_generator, short_circuit).
+
+* Variables: Use snake_case (e.g., chunk_size, prev_transformer, loop_transformer).
+
+* Classes: Use PascalCase (e.g., Transformer, ErrorHandler, PipelineContext).
+
+* Constants: Use UPPER_SNAKE_CASE (e.g., DEFAULT_CHUNK_SIZE).
+
+* Internal Methods/Attributes: Prefix with a single underscore _ (e.g., _pipe).
+
+* Descriptiveness:
+
+  * Functions used for filtering should be named predicate.
+
+  * Functions passed to loop should be named condition.
+
+  * Transformers passed into methods like loop or tap should be named loop_transformer or tapped_transformer.
+
+## 3. Transformer Class Specifics
+* Chainability: Every pipeline operation (map, filter, loop, etc.) must return self to allow for method chaining.
+
+* Immutability of Logic: Operations should not modify the Transformer instance in place but rather compose a new self.transformer function by wrapping the previous one. The _pipe method is the primary mechanism for this.
+
+* Context Awareness: When adding a new method that accepts a function (like map or filter), always check if that function is "context-aware" using the is_context_aware helper. Provide a separate execution path for both context-aware and non-aware functions.
+
+* Overloading: For methods that can accept multiple distinct types (like tap accepting a Callable or a Transformer), use the @overload decorator to provide clear type hints for each signature.
+
+## 4. Writing and Adding Tests
+
+* All new functionality must be accompanied by comprehensive tests using pytest.
+
+* File Location: Tests for laygo/transformers/transformer.py are located in tests/test_transformer.py.
+
+* Test Organization:
+
+  * Group related tests into classes. The class name should follow the pattern Test<FeatureGroup>, for example: TestTransformerBasics, TestTransformerOperations, TestTransformerContextSupport, TestTransformerErrorHandling
+
+  * When adding tests for a new method, add them to the most relevant existing test class. If the method introduces a new category of functionality, create a new Test... class for it.
+
+* Test Naming: Test methods must be descriptive and follow the pattern test_<method>_<scenario>. test_map_simple_transformation, test_loop_with_max_iterations, test_filter_with_empty_list, test_catch_with_context_aware_error
+
+* Test Structure (Arrange-Act-Assert):
+
+  * Arrange: Set up all necessary data, including input lists, PipelineContext objects, and the Transformer instance itself.
+
+  * Act: Execute the transformer on the data. The result should usually be materialized into a list, e.g., result = list(transformer(data)).
+
+  * Assert: Check that the output is correct. If the operation has side effects (like in tap or loop), assert that the side effects are also correct.
+
+* Coverage for New Methods: When adding tests for a new method (e.g., a hypothetical my_new_op), ensure you cover:
+
+  * Basic functionality (the "happy path").
+
+  * Context-aware version of the functionality.
+
+  * Edge cases, such as an empty input list ([]), a list with a single element, and a case where the operation results in an empty list.
+
+* Interaction with other operations in a chain.
+
+* Behavior with different chunk sizes to ensure chunking does not affect the outcome.
+
+## 5. Documentation
+* Whenever you're adding new functionality, make sure you create documentation in the wiki folder and link it in the Home.md file.
+
+* Do not go overboard with examples. The goal is to give a clear understanding of how to use the new functionality, not to provide exhaustive examples.
+
+## 6. Examples
+* There should be an examples folder in the root of the repository.
+
+* The folder should contain example scripts with clear names that indicate their purpose, such as example_basic_pipeline.py, example_context_aware_operations.py, and example_error_handling.py.
+
+* Each example script should include a brief comment at the top explaining what the script demonstrates.
+
+* The examples should be runnable as standalone scripts, meaning they should not rely on any external setup or configuration.