proprietary-data-generator

proprietary-data-generator is a skill for Claude Code, Codex from Gingg7260/affiliate-skills. It costs 112 tokens per session (2,612 once invoked), scanned A, a copy of proprietary-data-generator, MIT.

A research tool for designing surveys, benchmarks, and combined datasets about a specific topic. The resulting original data can support articles and other content.

In plain words
What is it for?
Use it to plan original research, collect survey or benchmark data, produce industry statistics, and build data-based content assets.
Why use it?
It helps create information competitors cannot simply copy from existing sources.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: positional $N argument; mentions Claude Code; built for openclaw.

Good fit Use it to plan original research, collect survey or benchmark data, produce industry statistics, and build data-based content assets.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/gingg7260/affiliate-skills/proprietary-data-generator
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add Gingg7260/affiliate-skills --skill proprietary-data-generator
Clone the repo
git clone --depth 1 https://github.com/Gingg7260/affiliate-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for proprietary-data-generator

README.md
[![agentmods](https://agentmods.dev/badge/skills/gingg7260/affiliate-skills/proprietary-data-generator/github.svg)](https://agentmods.dev/skills/gingg7260/affiliate-skills/proprietary-data-generator)
Your own site
<a href="https://agentmods.dev/skills/gingg7260/affiliate-skills/proprietary-data-generator"><img src="https://agentmods.dev/badge/skills/gingg7260/affiliate-skills/proprietary-data-generator/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for proprietary-data-generator

Your own site · 80×15
<a href="https://agentmods.dev/skills/gingg7260/affiliate-skills/proprietary-data-generator"><img src="https://agentmods.dev/badge/skills/gingg7260/affiliate-skills/proprietary-data-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 112 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,612 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 100% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00112 $0.02612
Opus 5 $0.00056 $0.01306
Sonnet 5 $0.00022 $0.00522
Haiku 4.5 $0.00011 $0.00261

Measured 9d ago against content hash c31521bd90be, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

proprietary-data-generator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

100% identical to proprietary-data-generator — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/automation/proprietary-data-generator/SKILL.md · 258 lines

How it starts

The opening of the file, as written. The whole thing — 258 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 258 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 258 lines · 112 tokens per session scan A c31521bd90be

Subscribe to this mod's changes

proprietary-data-generator is a skill published in the GitHub repository Gingg7260/affiliate-skills (5 stars, last pushed 2d ago), licensed MIT. It adds 112 tokens to every session and 2,612 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to proprietary-data-generator, differing in 0 lines, and is treated as a copy.

Related

Other skills, from other repositories

proprietary-data-generator

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"…

Affitor/affiliate-skills · 112 tokens

proprietary-data-generator

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"…

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