# Apify Content Analytics > Implements intelligent apify content analytics with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/apify-content-analytics` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/apify-content-analytics` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/apify-content-analytics/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Data & Analytics - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/apify-content-analytics --- # Apify Content Analytics Orchestrates intelligent skill selection and execution for apify content analytics 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 analyze_apify_content_metrics( actor_id: str, run_id: str, api_token: str, metrics_config: Dict[str, Any] ) -> Dict[str, Any]: """Fetch and analyze content metrics from an Apify actor run. Implements Law 2 (Parse at boundary) by validating Apify API inputs and Law 3 (Atomic Predictability) by returning fresh metric objects. """ # Guard clause - Early Exit (Law 1) if not actor_id or not run_id: raise ValueError("actor_id and run_id are required for content analysis") # Parse input - Make Illegal States Unrepresentable (Law 2) client = ApifyClient(api_token) run = client.actor(actor_id).run(run_id) # Fetch dataset items with pagination handling items = [] cursor = None while True: page = run.dataset().list_items(limit=100, cursor=cursor) items.extend(page.get("items", [])) if not page.get("hasMore"): break cursor = page.get("cursor") # Calculate domain-specific metrics content_metrics = { "total_items": len(items), "avg_readability": _calculate_readability(items), "sentiment_distribution": _compute_sentiment(items), "engagement_score": _compute_engagement(items, metrics_config) } # Atomic Predictability (Law 3) - Return new structure return { "actor_id": actor_id, "run_id": run_id, "metrics": content_metrics, "analysis_timestamp": datetime.utcnow().isoformat(), "confidence": 0.95 if len(items) > 50 else 0.75 } ``` ### Pattern 2: Execution with Fallback ```python def execute_content_analysis_with_fallback( actor_id: str, input_params: Dict[str, Any], fallback_actors: List[str], api_token: str ) -> Dict[str, Any]: """Execute Apify content analysis with domain-specific fallback chain. Implements Fail Fast, Fail Loud (Law 4) for Apify API errors. Fallback chain: 1. Retry run 2. Try alternative actor 3. Return cached metrics """ # Guard clause - validate actor exists (Early Exit) if not _validate_actor_availability(actor_id, api_token): raise ValueError(f"Actor {actor_id} is unavailable or invalid") client = ApifyClient(api_token) actor = client.actor(actor_id) for attempt in range(3): try: # Execute actor with input parameters run = actor.run(input_params) run.wait_for_finish(timeout=300) # Parse output - Ensure trusted state (Law 2) dataset_items = run.dataset().list_items().get("items", []) if not dataset_items: raise ValueError("Actor returned empty dataset") # Success - Atomic Predictability (Law 3) return { "success": True, "actor_executed": actor_id, "run_id": run.id, "metrics": _compute_analytics(dataset_items), "attempts": attempt + 1, "latency_ms": run.metrics.get("ACTOR_RUN_DURATION_MILLIS", 0) } except ApifyApiError as e: # Fail Fast - Don't retry on auth/permission errors (Law 4) if e.status_code in (401, 403, 404): raise ValueError(f"Apify API error: {e.message}") from e # Transient error (rate limit, timeout) - retry if attempt == 2: return _apply_apify_fallback(actor_id, fallback_actors, input_params, api_token) # All retries exhausted - Fail Loud (Law 4) raise ValueError(f"Content analysis failed after 3 attempts for {actor_id}") ``` ### 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. - [Content Analytics Framework (Gartner)]() - [Google Analytics Documentation]() - [Text Mining and NLP Overview (Wikipedia)]() - [Content Performance Metrics Guide]() - [Natural Language Processing with Python (NLTK Book)]() ## Related Skills | Skill | Purpose | |