paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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.