/proprietary-data-generator

name: proprietary-data-generator
description: >
  Create original surveys, benchmarks, and aggregated data nobody else has. Automate data collection
  for content moats.
  Triggers on: "create original data", "proprietary data", "survey design", "benchmark study",
  "original research", "data-driven content", "create a survey", "industry benchmark",
  "aggregated data", "unique data", "first-party data", "data moat",
  "generate research data", "create a study", "original statistics",
  "data nobody else has", "competitive data advantage".
license: MIT
version: "1.0.0"
tags: ["affiliate-marketing", "automation", "scaling", "workflow", "data", "original-research"]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: affitor
  version: "1.0"
  stage: S7-Automation

Proprietary Data Generator

Create original surveys, benchmarks, and aggregated data that nobody else has. Proprietary data is the ultimate content moat — competitors can copy your writing style but they can't copy YOUR data. Automates the design and execution framework for data collection that feeds unique content angles.

Stage

S7: Automation & Scale — Generating data at scale requires automation. This skill designs the collection system, not just one data point. Creates repeatable data assets that compound over time.

When to Use

• User wants to create content that can't be replicated by competitors
• User asks about "original research", "surveys", "benchmarks", "proprietary data"
• User says "data moat", "unique data", "first-party data", "original statistics"
• After `content-moat-calculator` identifies the need for differentiated content
• User wants to build authority through data-driven content
• User wants to create linkable assets that earn backlinks naturally

Input Schema

niche: string                 # REQUIRED — topic area for data collection
                              # e.g., "AI video tools", "affiliate marketing"

data_type: string             # OPTIONAL — "survey" | "benchmark" | "aggregation" | "case_study"
                              # Default: recommend based on niche and resources

audience_access: string       # OPTIONAL — how you can reach respondents
                              # e.g., "email list of 500", "Reddit community", "Twitter followers"
                              # Default: suggest options

budget: string                # OPTIONAL — "zero" | "low" ($0-100) | "medium" ($100-500) | "high" ($500+)
                              # Default: "zero"

goal: string                  # OPTIONAL — "content_moat" | "backlink_magnet" | "authority" | "lead_gen"
                              # Default: "content_moat"

**Chaining from S3 content-moat-calculator**: Use `competitive_advantages` to identify data moat opportunities.

Workflow

Step 1: Identify Data Opportunity

Analyze the niche for data gaps: 1. `web_search`: `"[niche] statistics 2025" OR "[niche] survey" OR "[niche] benchmark"` — what data already exists? 2. Identify gaps: what questions does the industry ask that nobody has answered with data? 3. `web_search`: `"[niche] reddit" "I wish I knew" OR "does anyone know"` — find unmet data needs

Step 2: Design Data Collection

Based on `data_type` (or recommend the best fit):

**Survey Design:**

• 8-12 questions (shorter = higher completion)
• Mix: 70% multiple choice, 20% scale (1-5), 10% open-ended
• One "surprising" question that will generate headline-worthy data
• Target sample size: 100+ for credibility
• Distribution plan: where and how to reach respondents

**Benchmark Study:**

• Define metrics to measure (3-5)
• Data sources: public data, API calls, manual collection
• Collection methodology: how often, what tools
• Comparison framework: how to present findings

**Data Aggregation:**

• Sources to aggregate from (public databases, APIs, web scraping targets)
• Aggregation logic: how to combine and normalize
• Update frequency: one-time or recurring
• Visualization plan

**Case Study Collection:**

• Template for collecting stories (5-7 structured questions)
• Outreach template for requesting case studies
• Anonymization rules
• Minimum viable sample: 10+ cases

Step 3: Create Collection Assets

Produce ready-to-use assets: 1. **Survey questions** (if survey) — complete question list with answer options 2. **Collection template** — spreadsheet structure or form layout 3. **Outreach template** — email/message to recruit respondents 4. **Data analysis plan** — how to turn raw data into insights 5. **Content plan** — how to present findings (blog post, infographic, report)

Step 4: Design Automation

Create a repeatable system:

• Schedule: when to collect data (monthly, quarterly, annually)
• Tools: recommended platforms (Google Forms, Typeform, Airtable)
• Automation: how to automate collection and reporting
• Update process: how to refresh and republish with new data

Step 5: Self-Validation

• [ ] Data gap is real (verified by search — nobody else has this data)
• [ ] Sample size is realistic given audience access
• [ ] Questions are unbiased and well-structured
• [ ] Collection method is feasible with stated budget
• [ ] Output content plan is specific (not just "write a blog post")
• [ ] Data is ethically collected (no scraping private data, survey has consent)

Output Schema

output_schema_version: "1.0.0"
proprietary_data:
  niche: string
  data_type: string
  data_gap: string              # What data doesn't exist yet
  headline_potential: string    # The "surprising finding" angle

  collection:
    method: string
    sample_target: number
    tools: string[]
    timeline: string
    budget_needed: string

  assets:
    survey_questions: object[]  # If survey type
    collection_template: string # Template description
    outreach_template: string   # Recruitment message
    analysis_plan: string

  content_outputs:              # Content to create from th