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 ```python 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 ```python 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)](<https://www.cipc.com/resources/best-practices/>) - [Web Scraping for Market Research](<https://scrapingbee.com/blog/web-scraping-market-research/>) - [Gartner Competitive Intelligence Framework](<https://www.gartner.com/en/research/methodologies/competitive-intelligence>) - [OSINT Methodologies Overview](<https://en.wikipedia.org/wiki/Open-source_intelligence>) - [Pricing Intelligence Methods](<https://pricemonitor.io/blog/pricing-strategies-for-ecommerce/>) ## Related Skills | Skill | Purpose | |