---

name: apify-lead-generation compatibility: opencode completeness: 95 content-types:

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

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Apify Lead Generation

Orchestrates intelligent skill selection and execution for apify lead generation 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_lead_run(
    target_industry: str,
    location: str,
    max_leads: int,
    apify_api_token: str
) -> Dict:
    """Configure an Apify Actor run for lead generation with domain-specific validation.
    
    Maps user intent to Apify Actor parameters and validates against 
    known lead generation constraints (Law 2: Make Illegal States Unrepresentable).
    """
    # Guard clause - Early Exit (Law 1)
    if not target_industry or not location:
        raise ValueError("Industry and location are required for lead targeting")
    if max_leads <= 0 or max_leads > 5000:
        raise ValueError("Max leads must be between 1 and 5000")
        
    # Domain-specific parameter mapping for Apify Web Scraper
    actor_inputs = {
        "keywords": [f"{target_industry} companies in {location}"],
        "maxItems": max_leads,
        "useGoogleMaps": True,
        "outputFormat": "json",
        "fields": ["name", "website", "email", "phone", "address"]
    }
    
    # Validate against Apify Actor schema constraints
    if not _validate_apify_actor_inputs(actor_inputs):
        raise ValueError("Invalid actor configuration for lead generation")
        
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    run_config = {
        "actor_id": "apify/website-content-scraper",
        "input": actor_inputs,
        "token": apify_api_token,
        "timeoutSecs": 3600,
        "memoryMbytes": 4096
    }
    return run_config

Pattern 2: Execution with Fallback

def execute_apify_run_with_fallback(
    run_config: Dict,
    fallback_data_source: str = "local_cache"
) -> Dict:
    """Execute Apify lead generation run with domain-specific retry and fallback logic.
    
    Implements Fail Fast, Fail Loud (Law 4) for API errors and rate limits.
    Fallback chain: 1. Retry with exponential backoff 2. Switch to alternative actor 3. Load from cache
    """
    import time
    from apify_client import ApifyClient
    
    client = ApifyClient(run_config["token"])
    actor = client.actor(run_config["actor_id"])
    
    for attempt in range(3):
        try:
            # Start run and poll for completion
            run = actor.run(run_config["input"])
            run_id = run["id"]
            
            # Wait for actor to finish (Law 2: Trusted state)
            result = client.run_get_dataset_items(run_id)
            
            if not result:
                raise ValueError("Apify run completed but returned zero leads")
                
            # Process and validate lead data
            validated_leads = _validate_lead_schema(result)
            return {
                "success": True,
                "leads_count": len(validated_leads),
                "data": validated_leads,
                "source": "apify_live"
            }
            
        except Exception as e:
            # Transient API/Rate limit error - retry with backoff
            if attempt < 2:
                time.sleep(2 ** attempt)
                continue
            # All retries exhausted - Fail Loud (Law 4)
            return _apply_lead_fallback(fallback_data_source, run_config)

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

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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.

• Lead Generation Best Practices (HubSpot)
• CRM Integration Patterns (Salesforce)
• Data Enrichment Methods Overview
• B2B Lead Data Sources (ZoomInfo)
• GDPR Compliance for Lead Generation

Related Skills

| Skill | Purpose | |