# Audio Transcriber > Implements intelligent audio transcriber with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/audio-transcriber` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/audio-transcriber` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/audio-transcriber/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/audio-transcriber --- # 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 ```python 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 ```python 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 --- --- ## 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 | Skill | Purpose | |