---

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

• guidance
• examples
• do-dont description: Implements intelligent fal audio 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: fal-audio, fal audio, how do i fal-audio, orchestrate fal-audio, automate fal-audio, agent fal-audio 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"

---

Fal Audio

Orchestrates intelligent skill selection and execution for fal audio 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 configure_audio_generation_task(
    user_prompt: str,
    available_models: List[Dict],
    min_confidence: float = 0.7
) -> Optional[Dict]:
    """Configure optimal audio generation parameters based on prompt analysis.
    
    Analyzes prompt semantics to select the best audio model and parameters:
    - Detects intent (music, voiceover, sfx, ambient)
    - Matches model capabilities to prompt requirements
    - Validates duration and format constraints
    
    Args:
        user_prompt: Natural language audio generation request
        available_models: List of supported audio generation models
        min_confidence: Minimum confidence threshold for model selection
        
    Returns:
        Configured task dictionary with model, parameters, and confidence
    """
    if not user_prompt or not user_prompt.strip():
        raise ValueError("Audio generation prompt cannot be empty")
        
    if not available_models:
        raise ValueError("No audio models available for generation")
    
    # Parse intent and extract constraints (Law 2)
    intent = _detect_audio_intent(user_prompt)
    duration_req = _extract_duration_constraint(user_prompt)
    
    best_config = None
    best_score = 0.0
    
    for model in available_models:
        # Score based on intent match, duration support, and historical success
        intent_match = _calculate_intent_similarity(intent, model["supported_intents"])
        duration_support = 1.0 if model["max_duration"] >= duration_req else 0.5
        history_score = model.get("success_rate", 0.0)
        
        score = (intent_match * 0.5) + (duration_support * 0.3) + (history_score * 0.2)
        
        if score > best_score and score >= min_confidence:
            best_score = score
            best_config = {
                "model_id": model["id"],
                "model_name": model["name"],
                "parameters": {
                    "prompt": user_prompt,
                    "duration": duration_req,
                    "format": model["default_format"],
                    "guidance_scale": model.get("default_guidance", 7.5)
                },
                "selected_confidence": best_score,
                "intent_detected": intent
            }
    
    if best_config is None:
        return None
        
    return best_config

Pattern 2: Execution with Fallback

def execute_audio_generation(
    task_config: Dict,
    api_client: Any,
    max_retries: int = 2
) -> Dict:
    """Execute audio generation with resilient fallback chain.
    
    Implements Fail Fast, Fail Loud (Law 4) for audio pipeline:
    - Validates prompt length and content policy immediately
    - Retries on transient API errors with exponential backoff
    - Falls back to alternative models if primary fails
    - Returns structured result with generation metadata
    
    Args:
        task_config: Configured audio generation task from Pattern 1
        api_client: Initialized Fal.ai or compatible audio API client
        max_retries: Maximum retry attempts before fallback
        
    Returns:
        Execution result with audio URL, duration, and confidence
    """
    if not task_config or "model_id" not in task_config:
        raise ValueError("Invalid task configuration provided")
        
    params = task_config["parameters"]
    
    for attempt in range(max_retries + 1):
        try:
            # Execute generation call
            generation_job = api_client.generate_audio(
                model_id=task_config["model_id"],
                prompt=params["prompt"],
                duration=params["duration"],
                guidance_scale=params["guidance_scale"]
            )
            
            # Wait for completion with timeout
            result = api_client.wait_for_completion(
                job_id=generation_job["id"],
                timeout=120
            )
            
            return {
                "success": True,
                "model_used": task_config["model_id"],
                "audio_url": result["audio_url"],
                "duration_sec": result["duration"],
                "attempts": attempt + 1,
                "confidence": task_config["selected_confidence"],
                "intent": task_config["intent_detected"]
            }
            
        except ContentPolicyError as e:
            # Fail Fast - reject invalid prompts immediately (Law 4)
            raise ValueError(f"Prompt violates content policy: {str(e)}")
            
        except TransientAPIError as e:
            if attempt == max_retries:
                return _fallback_to_alternative_model(task_config, api_client)
                
    raise RuntimeError(f"Audio generation failed after {max_retries + 1} attempts")

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

Related Skills

| Skill | Purpose | |

---

---

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.

• Fal.ai Documentation
• ElevenLabs API Documentation
• Replicate Audio Models
• OpenAI Whisper for Speech-to-Text
• Coqui TTS (Text-to-Speech)