---

name: apify-content-analytics compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent apify content analytics 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-content-analytics, apify content analytics, how do i apify-content-analytics, orchestrate apify-content-analytics, automate apify-content-analytics, agent apify-content-analytics 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 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

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

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

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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.

• 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 | |