---

name: audio-transcriber compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent audio transcriber with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: audio-transcriber, audio transcriber, how do i audio-transcriber, orchestrate audio-transcriber, automate audio-transcriber, agent audio-transcriber archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

---

Audio Transcriber

Orchestrates intelligent skill selection and execution for audio transcriber workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def select_transcription_engine(
    audio_path: str,
    language: str,
    noise_level: float,
    available_engines: List[Dict]
) -> Dict:
    """Select optimal transcription engine based on audio characteristics.
    
    Evaluates engines against audio metadata to pick the best match:
    - Whisper-large-v3 for high noise/long audio
    - Whisper-tiny for short/clean audio
    - Commercial API fallback for enterprise compliance
    
    Args:
        audio_path: Path to the audio file to transcribe
        language: Target language code (e.g., 'en', 'es', 'fr')
        noise_level: Estimated background noise ratio (0.0-1.0)
        available_engines: List of configured transcription engine metadata
        
    Returns:
        Selected engine configuration with confidence score and selection rationale
    """
    # Guard clause - Early Exit (Law 1)
    if not os.path.exists(audio_path):
        raise FileNotFoundError(f"Audio file not found: {audio_path}")
        
    if not available_engines:
        raise ValueError("No transcription engines available in configuration")
    
    # Parse input - Make Illegal States Unrepresentable (Law 2)
    file_size = os.path.getsize(audio_path)
    duration = _estimate_audio_duration(audio_path)
    
    best_engine = None
    best_score = -1.0
    
    for engine in available_engines:
        score = 0.0
        if engine["name"] == "whisper-large-v3":
            score = 1.0 if noise_level > 0.5 or duration > 300 else 0.6
        elif engine["name"] == "whisper-tiny":
            score = 0.9 if noise_level < 0.2 and duration < 60 else 0.3
        elif engine["name"] == "commercial-api":
            score = 0.8 if file_size > 50_000_000 else 0.5
        elif engine["name"] == "whisper-medium":
            score = 0.7 if language in ["en", "es", "fr", "de"] else 0.4
            
        if score > best_score:
            best_score = score
            best_engine = engine
    
    if best_score < 0.5:
        return {"fallback": True, "engine": "human-review", "confidence": 0.0}
    
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    result = dict(best_engine)
    result["confidence"] = best_score
    result["selection_reason"] = f"matched_audio_profile_{duration}s_noise_{noise_level}"
    result["timestamp"] = time.time()
    return result

Pattern 2: Execution with Fallback

def execute_transcription_pipeline(
    audio_path: str,
    target_language: str,
    engine_config: Dict,
    confidence_threshold: float = 0.75
) -> Dict:
    """Execute audio transcription with domain-specific fallback chain.
    
    Implements the Fail Fast, Fail Loud principle (Law 4):
    - Invalid audio formats halt immediately
    - Low-confidence segments trigger automatic fallback
    - No silent failures or partial results without metadata
    
    Fallback chain:
    1. Retry with original engine parameters
    2. Switch to larger model (whisper-large-v3) for difficult segments
    3. Defer to human review if confidence remains below threshold
    4. Log & return partial transcript with error markers
    
    Args:
        audio_path: Path to the source audio file
        target_language: ISO 639-1 language code for transcription
        engine_config: Selected engine metadata from Pattern 1
        confidence_threshold: Minimum acceptable confidence score (0.0-1.0)
        
    Returns:
        Transcription result with segments, confidence metrics, and fallback status
    """
    # Guard clause - validate audio format (Early Exit)
    if not _validate_audio_format(audio_path):
        raise ValueError("Unsupported audio format. Convert to WAV/MP3 before processing.")
    
    # Parse context - Ensure trusted state (Law 2)
    processed_audio = _normalize_and_trim(audio_path)
    chunks = _split_into_chunks(processed_audio, max_seconds=300)
    
    transcribed_segments = []
    fallback_triggered = False
    
    for i, chunk in enumerate(chunks):
        try:
            result = _run_transcription(chunk, engine_config)
            
            # Success - Atomic Predictability (Law 3)
            if result["confidence"] < confidence_threshold:
                fallback_triggered = True
                result = _run_transcription(chunk, {"name": "whisper-large-v3", "language": target_language})
                
            transcribed_segments.append({
                "chunk_index": i,
                "text": result["text"],
                "confidence": result["confidence"],
                "start_time": result["start_time"],
                "end_time": result["end_time"]
            })
            
        except InvalidStateError as e:
            # Fail Fast - Don't try to patch bad audio data (Law 4)
            raise TranscriptionError(f"Invalid audio state in chunk {i}: {str(e)}") from e
            
        except TransientError:
            # Transient error - try fallback or continue
            if i == len(chunks) - 1:
                return {"status": "human_review_required", "partial_transcript": transcribed_segments}
            continue
    
    # All retries exhausted - Fail Loud (Law 4)
    full_text = " ".join(seg["text"] for seg in transcribed_segments)
    return {
        "status": "success" if not fallback_triggered else "fallback_used",
        "transcript": full_text,
        "segments": transcribed_segments,
        "fallback_applied": fallback_triggered,
        "processing_time_ms": _get_elapsed_ms(),
        "average_confidence": sum(s["confidence"] for s in transcribed_segments) / len(transcribed_segments)
    }

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

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Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• OpenAI Whisper Documentation
• Speech Recognition Evaluation Metrics (Wikipedia)
• FFmpeg Documentation
• Audio Codec Formats Comparison
• Mozilla DeepSpeech Speech Recognition

Related Skills

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