Code Cleanup Skill

The Code Cleanup Skill reviews, refactors, and documents code changes in your current branch, ensuring alignment with Pipecat's architecture, coding standards, and example patterns. It focuses on readability, correctness, performance, and consistency, while avoiding breaking changes.

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Skill Overview

This skill analyzes all changes introduced in your branch and performs the following actions:

1. Analyze Branch Changes
   • Review uncommitted changes and outgoing commits
2. Refactor for Readability
   • Improve clarity, naming, structure, and modern Python usage
3. Enhance Performance
   • Identify safe, conservative optimization opportunities
4. Add Documentation
   • Apply Pipecat-style, Google-format docstrings
5. Ensure Pattern Consistency
   • Match existing Pipecat services, pipelines, and examples
6. Validate Examples
   • Ensure examples follow foundational patterns (e.g. 07-interruptible.py)

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Usage

Invoke the skill using any of the following commands:

• "Clean up my branch code"
• "Refactor the changes in my branch"
• "Review and improve my branch code"
• /cleanup

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What This Skill Does

1. Analyze Branch Changes

The skill retrieves all uncommitted changes and outgoing commits to understand:

• New files added
• Modified files
• Code additions and deletions
• Overall scope and intent of changes

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2. Code Refactoring

Readability Improvements

• Replace tuples with named classes or dataclasses
• Improve variable, method, and class naming
• Extract complex logic into well-named helper methods
• Add missing type hints
• Simplify nested or complex conditionals
• Replace deprecated methods and features
• Normalize formatting to match Pipecat style

Performance Enhancements

• Identify inefficient loops or repeated work
• Suggest appropriate data structures
• Optimize async workflows and I/O
• Remove redundant operations

Performance changes are conservative and non-breaking.

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3. Documentation

Documentation follows Google-style docstrings, consistent with Pipecat conventions.

Class Documentation

class ExampleService:
    """Brief one-line description.

    Detailed explanation of the class purpose, responsibilities,
    and important behaviors.

    Supported features:

    - Feature 1
    - Feature 2
    - Feature 3
    """

Method Documentation

def process_data(self, data: str, options: Optional[dict] = None) -> bool:
    """Process incoming data with optional configuration.

    Args:
        data: The input data to process.
        options: Optional configuration dictionary.

    Returns:
        True if processing succeeded, False otherwise.

    Raises:
        ValueError: If data is empty or invalid.
    """

Pydantic Model Parameters

class InputParams(BaseModel):
    """Configuration parameters for the service.

    Parameters:
        timeout: Request timeout in seconds.
        retry_count: Number of retry attempts.
        enable_logging: Whether to enable debug logging.
    """

    timeout: Optional[float] = None
    retry_count: int = 3
    enable_logging: bool = False

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4. Pattern Consistency Checks

Service Classes

• Correct inheritance (TTSService, STTService, LLMService)
• Consistent constructor signatures
• Frame emission patterns
• Metrics support:
  • can_generate_metrics()
  • TTFB and TTFA metrics
  • Usage metrics
• Alignment with similar existing services

Examples

Validated against examples/07-interruptible.py:

• Proper create_transport() usage
• Correct pipeline structure
• Task setup and observers
• Event handler registration
• Runner and bot entrypoint consistency

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5. Specific Implementation Patterns

Service Implementation

class ExampleTTSService(TTSService):
    def __init__(self, *, api_key: Optional[str] = None, **kwargs):
        super().__init__(**kwargs)
        self._api_key = api_key or os.getenv("SERVICE_API_KEY")

    def can_generate_metrics(self) -> bool:
        return True

    async def run_tts(self, text: str) -> AsyncGenerator[Frame, None]:
        try:
            await self.start_ttfb_metrics()
            yield TTSStartedFrame()
            # ... processing ...
            frame = TTSAudioRawFrame(...)
            await self.process_ttfa_metrics(frame)
            yield frame
        finally:
            await self.stop_ttfb_metrics()

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Example Structure Pattern

transport_params = {
    "daily": lambda: DailyParams(...),
    "twilio": lambda: FastAPIWebsocketParams(...),
    "webrtc": lambda: TransportParams(...),
}


async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
    stt = DeepgramSTTService(...)
    tts = SomeTTSService(...)
    llm = OpenAILLMService(...)

    context = LLMContext(messages)
    user_aggregator, assistant_aggregator = LLMContextAggregatorPair(...)

    pipeline = Pipeline([...])
    worker = PipelineWorker(pipeline, params=..., observers=[...])

    runner = WorkerRunner(handle_sigint=runner_args.handle_sigint)

    await runner.add_workers(worker)

    @transport.event_handler("on_client_connected")
    async def on_client_connected(transport, client):
        await worker.queue_frames([LLMRunFrame()])

    @transport.event_handler("on_client_disconnected")
    async def on_client_disconnected(transport, client):
        await runner.cancel()

    await runner.run()


async def bot(runner_args: RunnerArguments):
    """Main bot entry point compatible with Pipecat Cloud."""
    transport = await create_transport(runner_args, transport_params)
    await run_bot(transport, runner_args)

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Execution Flow

1. Fetch uncommitted and outgoing changes
2. Categorize files (services, examples, tests, utilities)
3. Analyze each file:
   • Readability
   • Performance
   • Documentation
   • Pattern consistency
4. Generate actionable recommendations
5. Apply Pipecat standards
6. Run /prose-review branch over the comments and docstrings written above, and fix anything it flags

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Examples

Before: Tuple Usage

def get_audio_info(self) -> Tuple[int, int]:
    return (48000, 1)

After: Named Class

class AudioInfo:
    """Audio configuration information.

    Parameters:
        sample_rate: Sample rate in Hz.
        num_channels: Number of audio channels.
    """

    sample_rate: int
    num_channels: int


def get_audio_info(self) -> AudioInfo:
    return AudioInfo(sample_rate=48000, num_channels=1)

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Before: Missing Documentation

class NewTTSService(TTSService):
    def __init__(self, api_key: str, voice: str):
        self._api_key = api_key
        self._voice = voice

After: Fully Documented

class NewTTSService(TTSService):
    """Text-to-speech service using NewProvider API.

    Streams PCM audio and emits TTSAudioRawFrame frames compatible
    with Pipecat transports.

    Supported features:
    - Text-to-speech synthesis
    - Streaming PCM audio
    - Voice customization
    - TTFB and TTFA metrics
    """

    def __init__(self, *, api_key: str, voice: str, **kwargs):
        """Initialize the NewTTSService.

        Args:
            api_key: API key for authentication.
            voice: Voice identifier to use.
            **kwargs: Additional arguments passed to the parent service.
        """
        super().__init__(**kwargs)
        self._api_key = api_key
        self.set_voice(voice)

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Notes

• Non-breaking improvements only
• Backward compatibility preserved
• Conservative performance changes
• Google-style docstrings
• Pattern checks follow recent Pipecat code