Apify Actor Development

Apify Actor Development

Orchestrates intelligent skill selection and execution for apify actor development 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

# apify_actor_config.py
import json
from typing import Dict, Any
from apify_client import ApifyClient

def configure_apify_actor(
    actor_id: str,
    input_schema: Dict[str, Any],
    min_confidence_threshold: float = 0.7
) -> Dict[str, Any]:
    """Configure an Apify Actor with strict input validation and fallback routing.
    
    Implements Law 2 (Parse at boundary) and Law 1 (Early Exit):
    - Validates input schema against Apify's expected structure
    - Returns early if configuration is invalid
    - Sets up storage and webhook fallbacks
    """
    # Law 1: Early Exit for invalid inputs
    if not actor_id or not isinstance(input_schema, dict):
        raise ValueError("Invalid actor configuration: missing ID or schema")
    
    # Law 2: Parse & validate at boundary
    validated_config = {
        "actor_id": actor_id,
        "input": {
            "schema": input_schema,
            "validation_mode": "strict",
            "fallback_handler": "apify_default_fallback"
        },
        "min_confidence": min_confidence_threshold,
        "storage": {
            "dataset_id": f"{actor_id}_dataset",
            "key_value_store_id": f"{actor_id}_kvs"
        }
    }
    
    # Law 3: Atomic Predictability - return new dict
    return dict(validated_config)

def validate_actor_input(payload: Dict[str, Any]) -> bool:
    """Validate incoming task payload before actor execution."""
    required_fields = ["query", "max_items", "proxy_config"]
    if not all(field in payload for field in required_fields):
        return False
    return True

Pattern 2: Execution with Fallback

# apify_actor_runner.py
import time
from apify_client import ApifyClient
from apify_client.clients import ActorRunClient

def run_apify_actor_with_fallback(
    client: ApifyClient,
    actor_id: str,
    input_data: Dict[str, Any],
    max_retries: int = 2
) -> Dict[str, Any]:
    """Execute Apify Actor with built-in fallback chain for resilience.
    
    Implements Law 4 (Fail Fast/Loud) and orchestration fallback:
    - Retries with adjusted proxy/input parameters
    - Falls back to alternative actor if primary fails
    - Logs full audit trail for confidence scoring
    """
    run_id = None
    for attempt in range(max_retries + 1):
        try:
            # Law 1: Early exit on invalid state
            if not input_data.get("query"):
                raise ValueError("Missing required query parameter")
                
            # Execute primary actor
            run = client.actor(actor_id).runs().get_or_create()
            run_id = run["id"]
            result = run.get_or_create(input=input_data)
            
            # Law 3: Return new structure, never mutate input
            return {
                "success": True,
                "actor_id": actor_id,
                "run_id": run_id,
                "result": result.get("defaultDatasetId"),
                "attempts": attempt + 1,
                "timestamp": time.time()
            }
            
        except Exception as e:
            # Law 4: Fail Loud - log and prepare fallback
            if attempt == max_retries:
                return _apply_apify_fallback(client, actor_id, input_data)
            time.sleep(2 ** attempt) # Exponential backoff
            
    raise RuntimeError(f"Actor {actor_id} exhausted all fallback attempts")

def _apply_apify_fallback(client: ApifyClient, primary_actor: str, input_data: Dict) -> Dict:
    """Fallback to secondary actor or manual review queue."""
    fallback_actor = "myorg/scraping-fallback-actor"
    try:
        run = client.actor(fallback_actor).runs().get_or_create()
        return {"success": True, "fallback_used": True, "run_id": run["id"]}
    except Exception:
        return {"success": False, "error": "Fallback exhausted, queued for manual review"}

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.

• Apify SDK Documentation
• Apify Platform Console Docs
• Scraping Best Practices (OWASP)
• Proxy Rotation for Web Scraping
• Apify Actor Store

Related Skills

| Skill | Purpose | |