From readwise
Analyzes Readwise highlights, tags, and Reader documents to surface one surprising insight about reading patterns and interests.
npx claudepluginhub readwiseio/readwise-skills --plugin readwiseThis skill uses the workspace's default tool permissions.
You are analyzing the user's reading data from Readwise and Reader to surface a surprising insight about them as a reader and thinker. Follow this process carefully.
Builds personalized reader persona from Readwise Reader data using highlights searches, document lists, tags, and Python/Bash parsing for triage, quiz skills.
Queries Readwise for highlights, quotes, annotations, full document text, and article content. Adds highlights or tagged documents to notebooks. Auto-activates on search or fetch requests.
Guides systematic book reading with knowledge compilation from chapters, mastery testing, spaced repetition reviews, querying, and cross-book comparisons. Builds Markdown wiki under project root.
Share bugs, ideas, or general feedback.
You are analyzing the user's reading data from Readwise and Reader to surface a surprising insight about them as a reader and thinker. Follow this process carefully.
Check if Readwise MCP tools are available (e.g. mcp__readwise__reader_list_documents). If they are, use them throughout. If not, use the equivalent readwise CLI commands instead (e.g. readwise list, readwise read <id>, readwise search <query>). The instructions below reference MCP tool names — translate to CLI equivalents as needed.
Cast a wide net. Run ALL of these in parallel:
mcp__readwise__readwise_list_highlights with limit=100mcp__readwise__readwise_search_highlights with a broad term like "important" or "interesting"mcp__readwise__readwise_search_highlights with another broad term like "surprised" or "changed my mind"mcp__readwise__reader_list_tagsmcp__readwise__reader_list_documents with location="archive", limit=50, response_fields=["title", "author", "category", "tags", "word_count", "reading_progress", "saved_at", "last_opened_at"]mcp__readwise__reader_list_documents with location="shortlist", limit=50, response_fields=["title", "author", "category", "tags", "word_count", "reading_progress", "saved_at"]Then paginate the archive at least 2-3 more pages to get a larger sample.
Look across ALL the data for patterns, contradictions, and surprises. Consider:
Present ONE genuinely surprising insight. Not a generic observation like "you read a lot about technology" — something that would make them pause and think "huh, I never noticed that."
Format:
Here's something you might not know about yourself:
[The surprising insight — 2-3 sentences, specific and grounded in their actual data]
Then back it up with evidence:
After delivering the insight, offer: