paulpas 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
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.
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.
Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.
Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.
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:
- Selected Skills - List of skill names with confidence scores
- Selection Rationale - Why each skill was chosen (match score, history, availability)
- Execution Plan - Order of execution with dependencies
- Fallback Strategy - Which fallback skills will be tried and in what order
- Risk Assessment - Any potential failure points and their impact
- 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 | |