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Create original surveys, benchmarks, and aggregated data nobody else has. Automate data collection for content moats.
Passed 11 of 17 test cases with the skill, and 13 of 17 without it.
3 fixed12 passed either way3 still failing5 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 3 test cases the skill fixed. Same model, same prompt, with and without the skill.
I am planning to publish findings from my pet industry benchmark survey. I want to turn the key findings into viral social media threads across platforms. Which specific tool skill should I map this social media content generation to?
| Checked | Without skill | With skill |
|---|---|---|
| Maps the social media content output to `content-pillar-atomizer`. | Missed | Passed |
The instructions your model reads when the skill loads.
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.
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.
content-moat-calculator identifies the need for differentiated contentniche: 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.
test cases passed, vs 13 / 17 without
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