Proprietary Data Generator

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 2515ce9634da · 15 Oct 2026Report an issue or request a re-scan

• PublisherAffitor GitHub organizationOwner of the repository this skill is read from
• Source folderAffitor/affiliate-skills/skills/automation/proprietary-data-generatorThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict2515ce9634daA new audit runs when the source changes
• Repository statusNot archivedLast push 2 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its 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",…

---
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

```yaml
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

```yaml
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

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-15
   ✓SAFE B 89 / 100

   first audit · source 2515ce9634da

Questions about Proprietary Data Generator

• github.com/Affitor/affiliate-skills/tree/main/skills/automation/ — the audited source at commit 2515ce9634da and every finding aboveObserved · 15 Oct 2026

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