---

name: apify-audience-analysis compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent apify audience analysis 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: apify-audience-analysis, apify audience analysis, how do i apify-audience-analysis, orchestrate apify-audience-analysis, automate apify-audience-analysis, agent apify-audience-analysis 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"

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Apify Audience Analysis

Orchestrates intelligent skill selection and execution for apify audience analysis 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_apify_audience_analysis(
    target_demographics: Dict[str, Any],
    data_sources: List[str],
    min_confidence: float = 0.7
) -> Dict[str, Any]:
    """Configure and validate Apify audience analysis parameters.
    
    Applies Law 1 (Early Exit) and Law 2 (Make illegal states unrepresentable)
    to ensure only valid audience analysis configurations are submitted.
    """
    # Law 1: Early exit on invalid inputs
    if not target_demographics or not data_sources:
        raise ValueError("Demographics and data sources are required for audience analysis")
        
    # Law 2: Validate and normalize inputs
    normalized_sources = [src.lower().strip() for src in data_sources if src]
    if not normalized_sources:
        raise ValueError("At least one valid data source must be provided")
        
    # Law 3: Return new structure, never mutate inputs
    config = {
        "actorId": "apify/audience-insights",
        "input": {
            "demographics": target_demographics,
            "data_sources": normalized_sources,
            "confidence_threshold": min_confidence,
            "output_format": "structured_json"
        },
        "meta": {
            "created_at": datetime.utcnow().isoformat(),
            "priority": "high" if min_confidence > 0.85 else "normal"
        }
    }
    
    # Validate against Apify API schema expectations
    _validate_apify_input_schema(config["input"])
    return config

Pattern 2: Execution with Fallback

def execute_apify_analysis_with_fallback(
    config: Dict[str, Any],
    apify_client: Any,
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute Apify audience analysis with domain-specific fallback chain.
    
    Implements Law 4 (Fail Fast, Fail Loud) and resilient execution patterns.
    """
    actor_run_id = None
    last_error = None
    
    for attempt in range(max_retries + 1):
        try:
            # Launch Apify actor run
            run = apify_client.actor(config["actorId"]).call_run(input=config["input"])
            actor_run_id = run["id"]
            
            # Wait for completion with timeout
            result = apify_client.actor_run(actor_run_id).get()
            if result["status"] != "SUCCEEDED":
                raise RuntimeError(f"Actor run failed: {result.get('status', 'unknown')}")
                
            # Law 3: Return new data structure
            return {
                "success": True,
                "actor_run_id": actor_run_id,
                "audience_segments": result.get("output", {}).get("segments", []),
                "confidence_score": result.get("output", {}).get("avg_confidence", 0.0),
                "attempts": attempt + 1
            }
            
        except RateLimitError:
            last_error = "Apify API rate limit exceeded"
            if attempt < max_retries:
                time.sleep(2 ** attempt)  # Exponential backoff
                continue
        except ActorFailedError as e:
            last_error = str(e)
            # Law 4: Fail fast on invalid actor state
            raise RuntimeError(f"Apify actor failed at attempt {attempt + 1}: {e}") from e
            
    # Fallback chain: Retry exhausted
    if actor_run_id:
        # Attempt fallback to cached/simplified analysis
        return _fallback_to_cached_analysis(config["input"]["demographics"])
        
    raise RuntimeError(f"Audience analysis failed after {max_retries + 1} attempts: {last_error}")

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.

• Audience Segmentation Methods (Wikipedia)
• Demographic Data Analysis Best Practices
• Market Research Methodologies Guide
• Data Privacy (GDPR Overview)
• Sentiment Analysis in Social Media Analytics

Related Skills

| Skill | Purpose | |