Apify Competitor Intelligence

Apify Competitor Intelligence

Orchestrates intelligent skill selection and execution for apify competitor intelligence 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 evaluate_apify_actors_for_competitor(
    competitor_url: str,
    available_actors: List[Dict],
    min_confidence: float = 0.7
) -> Optional[Dict]:
    """Evaluate Apify actors to find the best fit for competitor intelligence.
    
    Scores actors based on URL compatibility, historical success rate,
    and current actor status (active/deprecated).
    """
    # Guard clause - Early Exit (Law 1)
    if not competitor_url or not available_actors:
        raise ValueError("Competitor URL and actor list are required")
        
    parsed_domain = urlparse(competitor_url).netloc
    best_actor = None
    best_score = 0.0
    
    for actor in available_actors:
        # Parse input - Make Illegal States Unrepresentable (Law 2)
        domain_match = _check_domain_compatibility(parsed_domain, actor.get("supported_domains", []))
        success_rate = actor.get("stats", {}).get("success_rate", 0.0)
        status = actor.get("status", "ACTIVE")
        
        if status != "ACTIVE":
            continue
            
        score = (domain_match * 0.6) + (success_rate * 0.4)
        if score > best_score and score >= min_confidence:
            best_score = score
            best_actor = actor
    
    if best_actor is None:
        return None
    
    # Atomic Predictability (Law 3) - Return new dict, don't mutate
    return {
        "actor_id": best_actor["id"],
        "actor_name": best_actor["name"],
        "confidence": best_score,
        "input_config": _build_default_input(competitor_url, best_actor),
        "fallback_actors": [a["id"] for a in available_actors if a["id"] != best_actor["id"]][:2]
    }

Pattern 2: Execution with Fallback

def run_apify_actor_with_fallback(
    actor_config: Dict,
    max_retries: int = 2
) -> Dict:
    """Execute an Apify actor with domain-specific fallback handling.
    
    Implements Fail Fast, Fail Loud (Law 4):
    - Invalid inputs halt immediately with descriptive errors
    - Rate limits trigger exponential backoff
    - Actor failures cascade to fallback actors
    
    Args:
        actor_config: Selected actor metadata and input configuration
        max_retries: Maximum retry attempts before fallback
        
    Returns:
        Execution result with metadata (success, timing, confidence)
    """
    actor_id = actor_config["actor_id"]
    input_config = actor_config["input_config"]
    fallback_actors = actor_config.get("fallback_actors", [])
    
    for attempt in range(max_retries + 1):
        try:
            # Execute via Apify API client
            run = apify_client.actor(actor_id).call(input=input_config)
            result = apify_client.dataset(run["defaultDatasetId"]).get_items()
            
            # Success - Atomic Predictability (Law 3)
            return {
                "success": True,
                "actor_executed": actor_config["actor_name"],
                "data_count": len(result),
                "attempts": attempt + 1,
                "latency_ms": run.get("stats", {}).get("actuatorSecondsTotal", 0) * 1000
            }
            
        except apify.ApiError as e:
            if e.status_code == 429:
                time.sleep(2 ** attempt) # Exponential backoff
                continue
            elif e.status_code == 400:
                # Invalid input - sanitize and retry
                input_config = _sanitize_input_for_actor(actor_id, input_config)
                continue
            else:
                # Fail Fast - Don't try to patch bad data (Law 4)
                raise ApifyExecutionError(f"Actor {actor_id} failed: {e}") from e
    
    # All retries exhausted - Fail Loud (Law 4)
    if fallback_actors:
        return run_apify_actor_with_fallback({
            **actor_config,
            "actor_id": fallback_actors[0],
            "input_config": input_config
        }, max_retries=1)
        
    raise ApifyExecutionError(f"All fallback actors exhausted for {actor_id}")

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.

• Competitive Intelligence Best Practices (CIPC)
• Web Scraping for Market Research
• Gartner Competitive Intelligence Framework
• OSINT Methodologies Overview
• Pricing Intelligence Methods

Related Skills

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