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This skill should be used when the user asks to "analyze an author's Twitter", "research author marketing on Twitter", "study how authors use Twitter/X", "compare author social media strategies", or mentions analyzing Twitter/X marketing approaches for book authors. Provides the analytical framework and signals to evaluate when examining author accounts.
How this skill is triggered — by the user, by Claude, or both
Slash command
/author-twitter:research-methodologyThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Framework for analyzing how book authors use Twitter/X as a marketing channel. This methodology guides systematic evaluation of author accounts to extract actionable marketing intelligence.
Framework for analyzing how book authors use Twitter/X as a marketing channel. This methodology guides systematic evaluation of author accounts to extract actionable marketing intelligence.
Provide a structured, repeatable approach to evaluating author Twitter/X accounts. The goal is to extract marketing patterns — not vanity metrics — that reveal how successful authors build audience, promote books, and sustain engagement.
Evaluate the author's static profile elements:
Categorize the last 50-100 visible tweets into these buckets:
| Category | Description | Examples |
|---|---|---|
| Direct Promo | Buy links, sale announcements, preorder CTAs | "My new book is live!" |
| Soft Promo | Tropes, aesthetic posts, character art, teasers | "POV: the villain loves you back" |
| Reader Engagement | Polls, questions, quote RTs of reviews | "What trope do you never get tired of?" |
| Personal/Relatable | Writing life, humor, behind-the-scenes | "Just deleted 3000 words. It's fine." |
| Community | Supporting other authors, RT swaps, shoutouts | "Happy release day @author!" |
| Thread/Long-form | Writing advice threads, story behind the book | Multi-tweet educational content |
| Trending/Cultural | Memes, trending topics tied back to books | Riding cultural moments |
Calculate approximate percentages. High-performing indie authors typically follow a 20/80 split (20% direct promo, 80% everything else).
Analyze observable timing and frequency:
Evaluate how the author drives and sustains interaction:
Look for evidence of structured marketing campaigns:
Identify how the author converts followers to readers/buyers:
When a genre is provided instead of a specific author:
Refer to references/genre-categories.md for Amazon KDP genre/subgenre navigation paths.
Structure findings as a Notion database with these properties:
| Property | Type | Description |
|---|---|---|
| Author Name | Title | Author's name |
| Twitter Handle | Text | @handle |
| Genre | Select | Primary genre |
| Followers | Number | Approximate follower count |
| Posts/Week | Number | Estimated posting frequency |
| Content Mix | Text | Brief summary of content ratio |
| Promo Style | Select | Direct / Soft / Balanced |
| Top Tactic | Text | Most effective engagement approach |
| KU/Wide | Select | KDP Select / Wide / Unknown |
| Newsletter | Checkbox | Has visible newsletter funnel |
| Research Date | Date | When analysis was performed |
For each author, create page content blocks with detailed analysis covering all six framework areas.
references/genre-categories.md — Amazon KDP genre/subgenre navigation paths for bestseller lookupreferences/analysis-template.md — Detailed template for per-author analysis write-upProvides CDSS development patterns for drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), and alert classification integrated into EMR workflows.
npx claudepluginhub wmiles81/author-twitter