data-and-original-researchlisted

The original-research / data-study content type — turn proprietary data, a survey, a public-dataset analysis, or an experiment into the most linkable and AI-citable asset you can publish. Use when someone wants to build authority with original data, run a "state of X" survey or industry study, turn proprietary/customer data into a publishable stat, or get cited by journalists and AI search (GEO). Uses the PROVE framework. Reads brand-profile + audience-research first. The agent designs the study (question, method, analysis plan) and frames the findings (headline stat, report, social cuts); the human/tool gathers the real data; WoopSocial publishes the finished cuts. Feeds ai-search-optimization + social-seo, the format writers, and infographic-and-data-viz. NEVER fabricates data, stats, or methodology; discloses method + limits. Distinct from educational-content-and-how-to (existing knowledge), analytics-and-reporting (internal performance), competitor-analysis, and trend-jacking.
social-media-skills/skills · ★ 5 · Data & Documents · score 73

Install: claude install-skill social-media-skills/skills

# data-and-original-research The **original-data content type** — find a question inside a data void, run a sound method, analyse it honestly, voice the one finding that travels, and engineer it for citation. A study people *have to cite*; the **format writers** turn it into cuts, **WoopSocial publishes**, and recurring studies map into the **content-calendar.** ## The POV: own a number and the internet has to come to you Most content is undifferentiated — ~94% of published pages earn zero external links (per Backlinko). Original data is the rare exception: publications link to **stories, not products**, and a data finding is a story. It's also the **#1 GEO asset** — adding statistics is among the strongest levers for AI-answer visibility (per the Princeton/KDD GEO study), and original data is statistics nobody else owns. Brands skip it because it's harder than a listicle — which is exactly the moat. The catch: a study is worth **nothing the moment one number is wrong.** Rigor isn't pedantry; it's the entire value. So the skill is knowing **what** to study, **how** to get real data, and how to make the finding **impossible not to cite** — never inventing it. ## Read these first 1. **brand-profile** — the proprietary data/angle you actually own. 2. **audience-research** — the question your audience (and journalists/AI) would cite. ## The framework: PROVE (Depth: `references/the-prove-framework.md`.) - **P — Pick a question inside a data void:** a claim worth proving where