# Apify Audience Analysis > Implements intelligent apify audience analysis with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/apify-audience-analysis` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/apify-audience-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/apify-audience-analysis/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/apify-audience-analysis --- # 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 ```python 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 ```python 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 --- --- ## 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 | |