---

name: apify-market-research compatibility: opencode completeness: 95 content-types:

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

---

Apify Market Research

Orchestrates intelligent skill selection and execution for apify market research 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_market_research(
    query: str,
    target_platforms: List[str],
    apify_api_token: str,
    actor_id: str = "apify/website-content"
) -> Dict:
    """Configure and validate an Apify actor run for market research.
    
    Validates query structure, maps platforms to actor inputs,
    and prepares the payload for the Apify API.
    """
    if not query or len(query) < 3:
        raise ValueError("Research query must be at least 3 characters")
        
    # Map research intent to specific Apify actor configurations
    platform_configs = {
        "web": {"startUrls": [{"url": f"https://www.google.com/search?q={query}"}], "maxItems": 50},
        "social": {"profileUrls": [f"https://twitter.com/search?q={query}"], "maxItems": 100},
        "ecommerce": {"startUrls": [{"url": f"https://www.amazon.com/s?k={query}"}], "maxItems": 30}
    }
    
    selected_config = platform_configs.get(target_platforms[0], platform_configs["web"])
    
    # Construct validated input payload
    payload = {
        "query": query,
        "platform": target_platforms[0],
        "actorConfig": selected_config,
        "timeoutSecs": 300,
        "memoryMbytes": 4096
    }
    
    # Validate against Apify API requirements
    if not apify_api_token.startswith("apify_api_"):
        raise ValueError("Invalid Apify API token format")
        
    return {
        "actor_id": actor_id,
        "input": payload,
        "token": apify_api_token,
        "run_type": "market_research"
    }

Pattern 2: Execution with Fallback

def execute_apify_research_run(config: Dict) -> Dict:
    """Execute an Apify actor run and process market research results.
    
    Handles run creation, status polling, dataset retrieval,
    and result aggregation for downstream analysis.
    """
    import requests
    import time
    
    api_base = "https://api.apify.com/v2"
    headers = {"Authorization": f"Bearer {config['token']}"}
    actor_id = config["actor_id"]
    
    # Create actor run
    run_response = requests.post(
        f"{api_base}/acts/{actor_id}/run",
        headers=headers,
        json={"input": config["input"]}
    )
    run_response.raise_for_status()
    run_id = run_response.json()["id"]
    
    # Poll until run completes
    status_url = f"{api_base}/runs/{run_id}/status"
    while True:
        status_resp = requests.get(status_url, headers=headers)
        status_data = status_resp.json()
        if status_data["status"] in ["READY", "RUNNING"]:
            time.sleep(5)
        else:
            break
            
    if status_data["status"] != "SUCCEEDED":
        raise RuntimeError(f"Apify run failed: {status_data.get('statusMessage', 'Unknown error')}")
        
    # Fetch and aggregate results
    dataset_url = f"{api_base}/runs/{run_id}/dataset/default"
    dataset_resp = requests.get(dataset_url, headers=headers)
    dataset_resp.raise_for_status()
    
    results = dataset_resp.json().get("items", [])
    return {
        "run_id": run_id,
        "status": "completed",
        "total_items": len(results),
        "data": results,
        "metadata": {
            "query": config["input"]["query"],
            "platform": config["input"]["platform"],
            "timestamp": time.time()
        }
    }

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

Related Skills

| Skill | Purpose | |