proprietary-data-generator

Name: proprietary-data-generator
Author: Affitor

3installs184forks

How to install proprietary-data-generator?

affitor/affiliate-skills/proprietary-data-generator

$npx -y skills add Affitor/affiliate-skills --skill proprietary-data-generator

Installs into the current project.

Summary

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", "indus

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SKILL.md

1	# Proprietary Data Generator
2
3	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.
4
5	## Stage
6
7	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.
8
9	## When to Use
10
11	- User wants to create content that can't be replicated by competitors
12	- User asks about "original research", "surveys", "benchmarks", "proprietary data"
13	- User says "data moat", "unique data", "first-party data", "original statistics"
14	- After `content-moat-calculator` identifies the need for differentiated content
15	- User wants to build authority through data-driven content
16	- User wants to create linkable assets that earn backlinks naturally
17
18	## Input Schema
19
20	```yaml
21	niche: string # REQUIRED — topic area for data collection
22	# e.g., "AI video tools", "affiliate marketing"
23
24	data_type: string # OPTIONAL — "survey" \| "benchmark" \| "aggregation" \| "case_study"
25	# Default: recommend based on niche and resources
26
27	audience_access: string # OPTIONAL — how you can reach respondents
28	# e.g., "email list of 500", "Reddit community", "Twitter followers"
29	# Default: suggest options
30
31	budget: string # OPTIONAL — "zero" \| "low" ($0-100) \| "medium" ($100-500) \| "high" ($500+)
32	# Default: "zero"
33
34	goal: string # OPTIONAL — "content_moat" \| "backlink_magnet" \| "authority" \| "lead_gen"
35	# Default: "content_moat"
36	```
37
38	Chaining from S3 content-moat-calculator: Use `competitive_advantages` to identify data moat opportunities.
39
40	## Workflow
41
42	### Step 1: Identify Data Opportunity
43
44	Analyze the niche for data gaps:
45	1. `web_search`: `"[niche] statistics 2025" OR "[niche] survey" OR "[niche] benchmark"` — what data already exists?
46	2. Identify gaps: what questions does the industry ask that nobody has answered with data?
47	3. `web_search`: `"[niche] reddit" "I wish I knew" OR "does anyone know"` — find unmet data needs
48
49	### Step 2: Design Data Collection
50
51	Based on `data_type` (or recommend the best fit):
52
53	Survey Design:
54	- 8-12 questions (shorter = higher completion)
55	- Mix: 70% multiple choice, 20% scale (1-5), 10% open-ended
56	- One "surprising" question that will generate headline-worthy data
57	- Target sample size: 100+ for credibility
58	- Distribution plan: where and how to reach respondents
59
60	Benchmark Study:
61	- Define metrics to measure (3-5)
62	- Data sources: public data, API calls, manual collection
63	- Collection methodology: how often, what tools
64	- Comparison framework: how to present findings
65
66	Data Aggregation:
67	- Sources to aggregate from (public databases, APIs, web scraping targets)
68	- Aggregation logic: how to combine and normalize
69	- Update frequency: one-time or recurring
70	- Visualization plan
71
72	Case Study Collection:
73	- Template for collecting stories (5-7 structured questions)
74	- Outreach template for requesting case studies
75	- Anonymization rules
76	- Minimum viable sample: 10+ cases
77
78	### Step 3: Create Collection Assets
79
80	Produce ready-to-use assets:
81	1. Survey questions (if survey) — complete question list with answer options
82	2. Collection template — spreadsheet structure or form layout
83	3. Outreach template — email/message to recruit respondents
84	4. Data analysis plan — how to turn raw data into insights
85	5. Content plan — how to present findings (blog post, infographic, report)
86
87	### Step 4: Design Automation
88
89	Create a repeatable system:
90	- Schedule: when to collect data (monthly, quarterly, annually)
91	- Tools: recommended platforms (Google Forms, Typeform, Airtable)
92	- Automation: how to automate collection and reporting
93	- Update process: how to refresh and republish with new data
94
95	### Step 5: Self-Validation

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proprietary-data-generator

3installs184forks

View on GitHub

How to install proprietary-data-generator?

affitor/affiliate-skills/proprietary-data-generator

$npx -y skills add Affitor/affiliate-skills --skill proprietary-data-generator

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# Proprietary Data Generator
2
3	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.
4
5	## Stage
6
7	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.
8
9	## When to Use
10
11	- User wants to create content that can't be replicated by competitors
12	- User asks about "original research", "surveys", "benchmarks", "proprietary data"
13	- User says "data moat", "unique data", "first-party data", "original statistics"
14	- After `content-moat-calculator` identifies the need for differentiated content
15	- User wants to build authority through data-driven content
16	- User wants to create linkable assets that earn backlinks naturally
17
18	## Input Schema
19
20	```yaml
21	niche: string # REQUIRED — topic area for data collection
22	# e.g., "AI video tools", "affiliate marketing"
23
24	data_type: string # OPTIONAL — "survey" \| "benchmark" \| "aggregation" \| "case_study"
25	# Default: recommend based on niche and resources
26
27	audience_access: string # OPTIONAL — how you can reach respondents
28	# e.g., "email list of 500", "Reddit community", "Twitter followers"
29	# Default: suggest options
30
31	budget: string # OPTIONAL — "zero" \| "low" ($0-100) \| "medium" ($100-500) \| "high" ($500+)
32	# Default: "zero"
33
34	goal: string # OPTIONAL — "content_moat" \| "backlink_magnet" \| "authority" \| "lead_gen"
35	# Default: "content_moat"
36	```
37
38	Chaining from S3 content-moat-calculator: Use `competitive_advantages` to identify data moat opportunities.
39
40	## Workflow
41
42	### Step 1: Identify Data Opportunity
43
44	Analyze the niche for data gaps:
45	1. `web_search`: `"[niche] statistics 2025" OR "[niche] survey" OR "[niche] benchmark"` — what data already exists?
46	2. Identify gaps: what questions does the industry ask that nobody has answered with data?
47	3. `web_search`: `"[niche] reddit" "I wish I knew" OR "does anyone know"` — find unmet data needs
48
49	### Step 2: Design Data Collection
50
51	Based on `data_type` (or recommend the best fit):
52
53	Survey Design:
54	- 8-12 questions (shorter = higher completion)
55	- Mix: 70% multiple choice, 20% scale (1-5), 10% open-ended
56	- One "surprising" question that will generate headline-worthy data
57	- Target sample size: 100+ for credibility
58	- Distribution plan: where and how to reach respondents
59
60	Benchmark Study:
61	- Define metrics to measure (3-5)
62	- Data sources: public data, API calls, manual collection
63	- Collection methodology: how often, what tools
64	- Comparison framework: how to present findings
65
66	Data Aggregation:
67	- Sources to aggregate from (public databases, APIs, web scraping targets)
68	- Aggregation logic: how to combine and normalize
69	- Update frequency: one-time or recurring
70	- Visualization plan
71
72	Case Study Collection:
73	- Template for collecting stories (5-7 structured questions)
74	- Outreach template for requesting case studies
75	- Anonymization rules
76	- Minimum viable sample: 10+ cases
77
78	### Step 3: Create Collection Assets
79
80	Produce ready-to-use assets:
81	1. Survey questions (if survey) — complete question list with answer options
82	2. Collection template — spreadsheet structure or form layout
83	3. Outreach template — email/message to recruit respondents
84	4. Data analysis plan — how to turn raw data into insights
85	5. Content plan — how to present findings (blog post, infographic, report)
86
87	### Step 4: Design Automation
88
89	Create a repeatable system:
90	- Schedule: when to collect data (monthly, quarterly, annually)
91	- Tools: recommended platforms (Google Forms, Typeform, Airtable)
92	- Automation: how to automate collection and reporting
93	- Update process: how to refresh and republish with new data
94
95	### Step 5: Self-Validation

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