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