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