name: voice-ai-engine-development description: "Voice AI Engine Development workflow skill. Use this skill when the user needs Build real-time conversational AI voice engines using async worker pipelines, streaming transcription, LLM agents, and TTS synthesis with interrupt handling and multi-provider support and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off." version: "0.0.1" category: ai-agents tags: ["voice-ai-engine-development", "build", "real-time", "conversational", "voice", "engines", "using", "async"] complexity: advanced risk: caution tools: ["codex-cli", "claude-code", "cursor", "gemini-cli", "opencode"] source: community author: "sickn33" date_added: "2026-04-15" date_updated: "2026-04-25"

Voice AI Engine Development

Overview

This public intake copy packages plugins/antigravity-awesome-skills-claude/skills/voice-ai-engine-development from https://github.com/sickn33/antigravity-awesome-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Voice AI Engine Development

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Component Implementation Guide, The Interrupt System, Multi-Provider Factory Pattern, WebSocket Integration, Common Pitfalls and Solutions, Production Considerations.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Building real-time voice conversation systems
• Implementing voice assistants or chatbots
• Creating voice-enabled customer service agents
• Developing voice AI applications with interrupt capabilities
• Integrating multiple transcription, LLM, or TTS providers
• Working with streaming audio processing pipelines

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Start with Base Workers: Implement the base worker pattern first
2. Add Transcriber: Choose a provider and implement streaming transcription
3. Add Agent: Implement LLM integration with streaming responses
4. Add Synthesizer: Implement TTS with audio streaming
5. Connect Pipeline: Wire all workers together with queues
6. Add Interrupts: Implement the interrupt system
7. Add WebSocket: Create WebSocket endpoint for client communication

Imported Workflow Notes

Imported: Implementation Workflow

When implementing a voice AI engine:

1. Start with Base Workers: Implement the base worker pattern first
2. Add Transcriber: Choose a provider and implement streaming transcription
3. Add Agent: Implement LLM integration with streaming responses
4. Add Synthesizer: Implement TTS with audio streaming
5. Connect Pipeline: Wire all workers together with queues
6. Add Interrupts: Implement the interrupt system
7. Add WebSocket: Create WebSocket endpoint for client communication
8. Test Components: Unit test each worker in isolation
9. Test Integration: Test the full pipeline end-to-end
10. Add Error Handling: Implement robust error handling and logging
11. Optimize: Add rate limiting, monitoring, and performance optimizations

Imported: Overview

This skill guides you through building production-ready voice AI engines with real-time conversation capabilities. Voice AI engines enable natural, bidirectional conversations between users and AI agents through streaming audio processing, speech-to-text transcription, LLM-powered responses, and text-to-speech synthesis.

The core architecture uses an async queue-based worker pipeline where each component runs independently and communicates via asyncio.Queue objects, enabling concurrent processing, interrupt handling, and real-time streaming at every stage.

Imported: Summary

Building a voice AI engine requires:

• ✅ Async worker pipeline for concurrent processing
• ✅ Queue-based communication between components
• ✅ Streaming at every stage (transcription, LLM, synthesis)
• ✅ Interrupt system for natural conversations
• ✅ Rate limiting for real-time audio playback
• ✅ Multi-provider support for flexibility
• ✅ Proper error handling and graceful shutdown

The key insight: Everything must stream and everything must be interruptible for natural, real-time conversations.

Imported: Component Implementation Guide

1. Transcriber (Audio → Text)

Purpose: Converts incoming audio chunks to text transcriptions

Interface Requirements:

class BaseTranscriber:
    def __init__(self, transcriber_config):
        self.input_queue = asyncio.Queue()   # Audio chunks (bytes)
        self.output_queue = asyncio.Queue()  # Transcriptions
        self.is_muted = False

    def send_audio(self, chunk: bytes):
        """Client calls this to send audio"""
        if not self.is_muted:
            self.input_queue.put_nowait(chunk)
        else:
            # Send silence instead (prevents echo during bot speech)
            self.input_queue.put_nowait(self.create_silent_chunk(len(chunk)))

    def mute(self):
        """Called when bot starts speaking (prevents echo)"""
        self.is_muted = True

    def unmute(self):
        """Called when bot stops speaking"""
        self.is_muted = False

Output Format:

class Transcription:
    message: str          # "Hello, how are you?"
    confidence: float     # 0.95
    is_final: bool        # True = complete sentence, False = partial
    is_interrupt: bool    # Set by TranscriptionsWorker

Supported Providers:

• Deepgram - Fast, accurate, streaming
• AssemblyAI - High accuracy, good for accents
• Azure Speech - Enterprise-grade
• Google Cloud Speech - Multi-language support

Critical Implementation Details:

• Use WebSocket for bidirectional streaming
• Run sender and receiver tasks concurrently with asyncio.gather()
• Mute transcriber when bot speaks to prevent echo/feedback loops
• Handle both final and partial transcriptions

2. Agent (Text → Response)

Purpose: Processes user input and generates conversational responses

Interface Requirements:

class BaseAgent:
    def __init__(self, agent_config):
        self.input_queue = asyncio.Queue()   # TranscriptionAgentInput
        self.output_queue = asyncio.Queue()  # AgentResponse
        self.transcript = None               # Conversation history

    async def generate_response(self, human_input, is_interrupt, conversation_id):
        """Override this - returns AsyncGenerator of responses"""
        raise NotImplementedError

Why Streaming Responses?

• Lower latency: Start speaking as soon as first sentence is ready
• Better interrupts: Can stop mid-response
• Sentence-by-sentence: More natural conversation flow

Supported Providers:

• OpenAI (GPT-4, GPT-3.5) - High quality, fast
• Google Gemini - Multimodal, cost-effective
• Anthropic Claude - Long context, nuanced responses

Critical Implementation Details:

• Maintain conversation history in Transcript object
• Stream responses using AsyncGenerator
• IMPORTANT: Buffer entire LLM response before yielding to synthesizer (prevents audio jumping)
• Handle interrupts by canceling current generation task
• Update conversation history with partial messages on interrupt

3. Synthesizer (Text → Audio)

Purpose: Converts agent text responses to speech audio

Interface Requirements:

class BaseSynthesizer:
    async def create_speech(self, message: BaseMessage, chunk_size: int) -> SynthesisResult:
        """
        Returns a SynthesisResult containing:
        - chunk_generator: AsyncGenerator that yields audio chunks
        - get_message_up_to: Function to get partial text (for interrupts)
        """
        raise NotImplementedError

SynthesisResult Structure:

class SynthesisResult:
    chunk_generator: AsyncGenerator[ChunkResult, None]
    get_message_up_to: Callable[[float], str]  # seconds → partial text

    class ChunkResult:
        chunk: bytes          # Raw PCM audio
        is_last_chunk: bool

Supported Providers:

• ElevenLabs - Most natural voices, streaming
• Azure TTS - Enterprise-grade, many languages
• Google Cloud TTS - Cost-effective, good quality
• Amazon Polly - AWS integration
• Play.ht - Voice cloning

Critical Implementation Details:

• Stream audio chunks as they're generated
• Convert audio to LINEAR16 PCM format (16kHz sample rate)
• Implement get_message_up_to() for interrupt handling
• Handle audio format conversion (MP3 → PCM)

4. Output Device (Audio → Client)

Purpose: Sends synthesized audio back to the client

CRITICAL: Rate Limiting for Interrupts

async def send_speech_to_output(self, message, synthesis_result,
                                stop_event, seconds_per_chunk):
    chunk_idx = 0
    async for chunk_result in synthesis_result.chunk_generator:
        # Check for interrupt
        if stop_event.is_set():
            logger.debug(f"Interrupted after {chunk_idx} chunks")
            message_sent = synthesis_result.get_message_up_to(
                chunk_idx * seconds_per_chunk
            )
            return message_sent, True  # cut_off = True

        start_time = time.time()

        # Send chunk to output device
        self.output_device.consume_nonblocking(chunk_result.chunk)

        # CRITICAL: Wait for chunk to play before sending next one
        # This is what makes interrupts work!
        speech_length = seconds_per_chunk
        processing_time = time.time() - start_time
        await asyncio.sleep(max(speech_length - processing_time, 0))

        chunk_idx += 1

    return message, False  # cut_off = False

Why Rate Limiting? Without rate limiting, all audio chunks would be sent immediately, which would:

• Buffer entire message on client side
• Make interrupts impossible (all audio already sent)
• Cause timing issues

By sending one chunk every N seconds:

• Real-time playback is maintained
• Interrupts can stop mid-sentence
• Natural conversation flow is preserved

Examples

Example 1: Ask for the upstream workflow directly

Use @voice-ai-engine-development to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @voice-ai-engine-development against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @voice-ai-engine-development for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @voice-ai-engine-development using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Decoupling: Workers only know about their input/output queues
• Concurrency: All workers run simultaneously via asyncio
• Backpressure: Queues automatically handle rate differences
• Interruptibility: Everything can be stopped mid-stream
• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.

Imported Operating Notes

Imported: Core Architecture Principles

The Worker Pipeline Pattern

Every voice AI engine follows this pipeline:

Audio In → Transcriber → Agent → Synthesizer → Audio Out
           (Worker 1)   (Worker 2)  (Worker 3)

Key Benefits:

• Decoupling: Workers only know about their input/output queues
• Concurrency: All workers run simultaneously via asyncio
• Backpressure: Queues automatically handle rate differences
• Interruptibility: Everything can be stopped mid-stream

Base Worker Pattern

Every worker follows this pattern:

class BaseWorker:
    def __init__(self, input_queue, output_queue):
        self.input_queue = input_queue   # asyncio.Queue to consume from
        self.output_queue = output_queue # asyncio.Queue to produce to
        self.active = False

    def start(self):
        """Start the worker's processing loop"""
        self.active = True
        asyncio.create_task(self._run_loop())

    async def _run_loop(self):
        """Main processing loop - runs forever until terminated"""
        while self.active:
            item = await self.input_queue.get()  # Block until item arrives
            await self.process(item)              # Process the item

    async def process(self, item):
        """Override this - does the actual work"""
        raise NotImplementedError

    def terminate(self):
        """Stop the worker"""
        self.active = False

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in plugins/antigravity-awesome-skills-claude/skills/voice-ai-engine-development, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Related Skills

• @00-andruia-consultant - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @00-andruia-consultant-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @10-andruia-skill-smith-v2 - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• common_pitfalls.md
• provider_comparison.md
• complete_voice_engine.py
• gemini_agent_example.py
• interrupt_system_example.py
• README.md

Imported Reference Notes

Imported: Resources

Libraries:

• asyncio - Async programming
• websockets - WebSocket client/server
• FastAPI - WebSocket server framework
• pydub - Audio manipulation
• numpy - Audio data processing

API Providers:

• Transcription: Deepgram, AssemblyAI, Azure Speech, Google Cloud Speech
• LLM: OpenAI, Google Gemini, Anthropic Claude
• TTS: ElevenLabs, Azure TTS, Google Cloud TTS, Amazon Polly, Play.ht

Imported: The Interrupt System

The interrupt system is critical for natural conversations.

How Interrupts Work

Scenario: Bot is saying "I think the weather will be nice today and tomorrow and—" when user interrupts with "Stop".

Step 1: User starts speaking

# TranscriptionsWorker detects new transcription while bot speaking
async def process(self, transcription):
    if not self.conversation.is_human_speaking:  # Bot was speaking!
        # Broadcast interrupt to all in-flight events
        interrupted = self.conversation.broadcast_interrupt()
        transcription.is_interrupt = interrupted

Step 2: broadcast_interrupt() stops everything

def broadcast_interrupt(self):
    num_interrupts = 0
    # Interrupt all queued events
    while True:
        try:
            interruptible_event = self.interruptible_events.get_nowait()
            if interruptible_event.interrupt():  # Sets interruption_event
                num_interrupts += 1
        except queue.Empty:
            break

    # Cancel current tasks
    self.agent.cancel_current_task()              # Stop generating text
    self.agent_responses_worker.cancel_current_task()  # Stop synthesizing
    return num_interrupts > 0

Step 3: SynthesisResultsWorker detects interrupt

async def send_speech_to_output(self, synthesis_result, stop_event, ...):
    async for chunk_result in synthesis_result.chunk_generator:
        # Check stop_event (this is the interruption_event)
        if stop_event.is_set():
            logger.debug("Interrupted! Stopping speech.")
            # Calculate what was actually spoken
            seconds_spoken = chunk_idx * seconds_per_chunk
            partial_message = synthesis_result.get_message_up_to(seconds_spoken)
            # e.g., "I think the weather will be nice today"
            return partial_message, True  # cut_off = True

Step 4: Agent updates history

if cut_off:
    # Update conversation history with partial message
    self.agent.update_last_bot_message_on_cut_off(message_sent)
    # History now shows:
    # Bot: "I think the weather will be nice today" (incomplete)

InterruptibleEvent Pattern

Every event in the pipeline is wrapped in an InterruptibleEvent:

class InterruptibleEvent:
    def __init__(self, payload, is_interruptible=True):
        self.payload = payload
        self.is_interruptible = is_interruptible
        self.interruption_event = threading.Event()  # Initially not set
        self.interrupted = False

    def interrupt(self) -> bool:
        """Interrupt this event"""
        if not self.is_interruptible:
            return False
        if not self.interrupted:
            self.interruption_event.set()  # Signal to stop!
            self.interrupted = True
            return True
        return False

    def is_interrupted(self) -> bool:
        return self.interruption_event.is_set()

Imported: Multi-Provider Factory Pattern

Support multiple providers with a factory pattern:

class VoiceHandler:
    """Multi-provider factory for voice components"""

    def create_transcriber(self, agent_config: Dict):
        """Create transcriber based on transcriberProvider"""
        provider = agent_config.get("transcriberProvider", "deepgram")

        if provider == "deepgram":
            return self._create_deepgram_transcriber(agent_config)
        elif provider == "assemblyai":
            return self._create_assemblyai_transcriber(agent_config)
        elif provider == "azure":
            return self._create_azure_transcriber(agent_config)
        elif provider == "google":
            return self._create_google_transcriber(agent_config)
        else:
            raise ValueError(f"Unknown transcriber provider: {provider}")

    def create_agent(self, agent_config: Dict):
        """Create LLM agent based on llmProvider"""
        provider = agent_config.get("llmProvider", "openai")

        if provider == "openai":
            return self._create_openai_agent(agent_config)
        elif provider == "gemini":
            return self._create_gemini_agent(agent_config)
        else:
            raise ValueError(f"Unknown LLM provider: {provider}")

    def create_synthesizer(self, agent_config: Dict):
        """Create voice synthesizer based on voiceProvider"""
        provider = agent_config.get("voiceProvider", "elevenlabs")

        if provider == "elevenlabs":
            return self._create_elevenlabs_synthesizer(agent_config)
        elif provider == "azure":
            return self._create_azure_synthesizer(agent_config)
        elif provider == "google":
            return self._create_google_synthesizer(agent_config)
        elif provider == "polly":
            return self._create_polly_synthesizer(agent_config)
        elif provider == "playht":
            return self._create_playht_synthesizer(agent_config)
        else:
            raise ValueError(f"Unknown voice provider: {provider}")

Imported: WebSocket Integration

Voice AI engines typically use WebSocket for bidirectional audio streaming:

@app.websocket("/conversation")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()

    # Create voice components
    voice_handler = VoiceHandler()
    transcriber = voice_handler.create_transcriber(agent_config)
    agent = voice_handler.create_agent(agent_config)
    synthesizer = voice_handler.create_synthesizer(agent_config)

    # Create output device
    output_device = WebsocketOutputDevice(
        ws=websocket,
        sampling_rate=16000,
        audio_encoding=AudioEncoding.LINEAR16
    )

    # Create conversation orchestrator
    conversation = StreamingConversation(
        output_device=output_device,
        transcriber=transcriber,
        agent=agent,
        synthesizer=synthesizer
    )

    # Start all workers
    await conversation.start()

    try:
        # Receive audio from client
        async for message in websocket.iter_bytes():
            conversation.receive_audio(message)
    except WebSocketDisconnect:
        logger.info("Client disconnected")
    finally:
        await conversation.terminate()

Imported: Common Pitfalls and Solutions

1. Audio Jumping/Cutting Off

Problem: Bot's audio jumps or cuts off mid-response.

Cause: Sending text to synthesizer in small chunks causes multiple TTS calls.

Solution: Buffer the entire LLM response before sending to synthesizer:

# ❌ Bad: Yields sentence-by-sentence
async for sentence in llm_stream:
    yield GeneratedResponse(message=BaseMessage(text=sentence))

# ✅ Good: Buffer entire response
full_response = ""
async for chunk in llm_stream:
    full_response += chunk
yield GeneratedResponse(message=BaseMessage(text=full_response))

2. Echo/Feedback Loop

Problem: Bot hears itself speaking and responds to its own audio.

Cause: Transcriber not muted during bot speech.

Solution: Mute transcriber when bot starts speaking:

# Before sending audio to output
self.transcriber.mute()
# After audio playback complete
self.transcriber.unmute()

3. Interrupts Not Working

Problem: User can't interrupt bot mid-sentence.

Cause: All audio chunks sent at once instead of rate-limited.

Solution: Rate-limit audio chunks to match real-time playback:

async for chunk in synthesis_result.chunk_generator:
    start_time = time.time()

    # Send chunk
    output_device.consume_nonblocking(chunk)

    # Wait for chunk duration before sending next
    processing_time = time.time() - start_time
    await asyncio.sleep(max(seconds_per_chunk - processing_time, 0))

4. Memory Leaks from Unclosed Streams

Problem: Memory usage grows over time.

Cause: WebSocket connections or API streams not properly closed.

Solution: Always use context managers and cleanup:

try:
    async with websockets.connect(url) as ws:
        # Use websocket
        pass
finally:
    # Cleanup
    await conversation.terminate()
    await transcriber.terminate()

Imported: Production Considerations

1. Error Handling

async def _run_loop(self):
    while self.active:
        try:
            item = await self.input_queue.get()
            await self.process(item)
        except Exception as e:
            logger.error(f"Worker error: {e}", exc_info=True)
            # Don't crash the worker, continue processing

2. Graceful Shutdown

async def terminate(self):
    """Gracefully shut down all workers"""
    self.active = False

    # Stop all workers
    self.transcriber.terminate()
    self.agent.terminate()
    self.synthesizer.terminate()

    # Wait for queues to drain
    await asyncio.sleep(0.5)

    # Close connections
    if self.websocket:
        await self.websocket.close()

3. Monitoring and Logging

# Log key events
logger.info(f"🎤 [TRANSCRIBER] Received: '{transcription.message}'")
logger.info(f"🤖 [AGENT] Generating response...")
logger.info(f"🔊 [SYNTHESIZER] Synthesizing {len(text)} characters")
logger.info(f"⚠️ [INTERRUPT] User interrupted bot")

# Track metrics
metrics.increment("transcriptions.count")
metrics.timing("agent.response_time", duration)
metrics.gauge("active_conversations", count)

4. Rate Limiting and Quotas

# Implement rate limiting for API calls
from aiolimiter import AsyncLimiter

rate_limiter = AsyncLimiter(max_rate=10, time_period=1)  # 10 calls/second

async def call_api(self, data):
    async with rate_limiter:
        return await self.client.post(data)

Imported: Key Design Patterns

1. Producer-Consumer with Queues

# Producer
async def producer(queue):
    while True:
        item = await generate_item()
        queue.put_nowait(item)

# Consumer
async def consumer(queue):
    while True:
        item = await queue.get()
        await process_item(item)

2. Streaming Generators

Instead of returning complete results:

# ❌ Bad: Wait for entire response
async def generate_response(prompt):
    response = await openai.complete(prompt)  # 5 seconds
    return response

# ✅ Good: Stream chunks as they arrive
async def generate_response(prompt):
    async for chunk in openai.complete(prompt, stream=True):
        yield chunk  # Yield after 0.1s, 0.2s, etc.

3. Conversation State Management

Maintain conversation history for context:

class Transcript:
    event_logs: List[Message] = []

    def add_human_message(self, text):
        self.event_logs.append(Message(sender=Sender.HUMAN, text=text))

    def add_bot_message(self, text):
        self.event_logs.append(Message(sender=Sender.BOT, text=text))

    def to_openai_messages(self):
        return [
            {"role": "user" if msg.sender == Sender.HUMAN else "assistant",
             "content": msg.text}
            for msg in self.event_logs
        ]

Imported: Testing Strategies

1. Unit Test Workers in Isolation

async def test_transcriber():
    transcriber = DeepgramTranscriber(config)

    # Mock audio input
    audio_chunk = b'\x00\x01\x02...'
    transcriber.send_audio(audio_chunk)

    # Check output
    transcription = await transcriber.output_queue.get()
    assert transcription.message == "expected text"

2. Integration Test Pipeline

async def test_full_pipeline():
    # Create all components
    conversation = create_test_conversation()

    # Send test audio
    conversation.receive_audio(test_audio_chunk)

    # Wait for response
    response = await wait_for_audio_output(timeout=5)

    assert response is not None

3. Test Interrupts

async def test_interrupt():
    conversation = create_test_conversation()

    # Start bot speaking
    await conversation.agent.generate_response("Tell me a long story")

    # Interrupt mid-response
    await asyncio.sleep(1)  # Let it speak for 1 second
    conversation.broadcast_interrupt()

    # Verify partial message in transcript
    last_message = conversation.transcript.event_logs[-1]
    assert last_message.text != full_expected_message

Imported: Limitations

• Use this skill only when the task clearly matches the scope described above.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.