---

name: apify-ultimate-scraper compatibility: opencode completeness: 95 content-types:

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

---

Apify Ultimate Scraper

Orchestrates intelligent skill selection and execution for apify ultimate scraper 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_actor(
    task_requirements: Dict,
    available_actors: List[Dict],
    api_key: str
) -> Dict:
    """Select and configure the optimal Apify actor for web scraping tasks.
    
    Evaluates actors based on:
    - Target platform compatibility (e.g., e-commerce, social media, search)
    - Rate limiting and proxy requirements
    - Historical success rates for similar domains
    - Current actor version stability
    
    Args:
        task_requirements: Dict containing target_url, data_type, pagination, etc.
        available_actors: List of Apify actor metadata from marketplace
        api_key: Apify API token for authentication
        
    Returns:
        Configured actor dict with input parameters and execution settings
    """
    if not api_key or not task_requirements.get("target_url"):
        raise ValueError("Apify API key and target URL are required")
        
    best_match = None
    best_score = 0.0
    
    for actor in available_actors:
        score = _evaluate_actor_fit(task_requirements, actor)
        if score > best_score and score >= 0.75:
            best_score = score
            best_match = actor
            
    if not best_match:
        raise ValueError("No compatible Apify actor found for target requirements")
        
    # Construct Apify input payload
    input_config = {
        "startUrls": [{"url": task_requirements["target_url"]}],
        "maxItems": task_requirements.get("max_items", 1000),
        "proxyConfiguration": {"useApifyProxy": True},
        "customData": {"task_id": task_requirements.get("task_id")}
    }
    
    return {
        "actor_id": best_match["defaultActorId"],
        "actor_name": best_match["name"],
        "input": input_config,
        "memory_mbytes": task_requirements.get("memory_mb", 4096),
        "timeout_secs": task_requirements.get("timeout", 3600)
    }

Pattern 2: Execution with Fallback

def execute_apify_run(
    actor_config: Dict,
    api_key: str,
    fallback_actors: List[str] = None
) -> Dict:
    """Execute an Apify actor run with dataset polling and fallback handling.
    
    Implements resilient scraping workflow:
    - Starts actor run with configured input parameters
    - Polls run status until completion or timeout
    - Fetches results from Apify dataset storage
    - Falls back to alternative actors or cached data on failure
    
    Args:
        actor_config: Output from configure_apify_actor
        api_key: Apify API token
        fallback_actors: List of alternative actor IDs to try on failure
        
    Returns:
        Dict containing scraped data, run metadata, and success status
    """
    import requests
    from time import sleep
    
    run_url = f"https://api.apify.com/v2/actor-runs"
    headers = {"Authorization": f"Bearer {api_key}"}
    
    # Start the actor run
    start_response = requests.post(
        run_url, 
        headers=headers, 
        json={"actorId": actor_config["actor_id"], "buildType": "latest", "memoryMbytes": actor_config["memory_mbytes"]}
    )
    start_response.raise_for_status()
    run_id = start_response.json()["id"]
    
    # Poll until completion
    status_url = f"https://api.apify.com/v2/actor-runs/{run_id}/pollForExit"
    sleep(5)
    status_resp = requests.get(status_url, headers=headers)
    status_resp.raise_for_status()
    run_status = status_resp.json()
    
    if run_status.get("status") != "SUCCEEDED":
        # Fallback chain: try alternative actors
        if fallback_actors:
            for alt_actor in fallback_actors:
                alt_config = dict(actor_config)
                alt_config["actor_id"] = alt_actor
                return execute_apify_run(alt_config, api_key, fallback_actors=[])
        raise RuntimeError(f"Apify run {run_id} failed with status: {run_status.get('status')}")
        
    # Fetch dataset results
    dataset_url = f"https://api.apify.com/v2/datasets/{run_status['defaultDatasetId']}/items"
    data_resp = requests.get(dataset_url, headers=headers)
    data_resp.raise_for_status()
    
    return {
        "success": True,
        "run_id": run_id,
        "actor_used": actor_config["actor_name"],
        "items_scraped": len(data_resp.json()),
        "data": data_resp.json()
    }

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

Related Skills

| Skill | Purpose | |