apify-lead-generation

# 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 ```python 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 ```python 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 --- --- ## 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)](<https://www.hubspot.com/marketing-statistics>) - [CRM Integration Patterns (Salesforce)](<https://developer.salesforce.com/docs/atlas.en-us.api_rest.meta/api_rest/>) - [Data Enrichment Methods Overview](<https://en.wikipedia.org/wiki/Data_enrichment>) - [B2B Lead Data Sources (ZoomInfo)](<https://www.zoominfo.com/company/blog/b2b-lead-generation-guide>) - [GDPR Compliance for Lead Generation](<https://gdpr.eu/business/what-is-gdpr-compliance/>) ## Related Skills | Skill | Purpose | |