# Aibrary Foryou Topic > [Aibrary] Generate personalized 'For You' book topic recommendations based on the user's profile, interests, career stage, and recent learning activity. Use when the user wants personalized topic suggestions, asks 'what should I learn today', wants a curated feed of book-based topics, or needs inspiration for their next area of exploration. Proactively suggest this when the user seems undecided about what to read or learn next. - Skill: `dvcrn/aibrary-foryou-topic` (Agent Skill) - Install (CLI): `npx skillmds@latest add dvcrn/aibrary-foryou-topic` - Raw SKILL.md: https://api.skillmd.com/api/skills/dvcrn/aibrary-foryou-topic/raw - Safety review: pending (external: skill-scanner PASS, skillspector PASS) - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: dvcrn (https://skillmd.com/u/dvcrn) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/dvcrn/aibrary-foryou-topic --- # ForYou Topic — Aibrary Your personalized book topic feed. AI-curated topic recommendations based on who you are and where you're headed. ## Input The user provides context (the more, the better): - **Interests** — topics they care about or are curious about - **Recent focus** — what they've been working on, reading, or thinking about lately - **Career/life stage** — their current professional or personal situation - **Goals** (optional) — what they're working toward - **Topics to avoid** (optional) — what they've already covered or aren't interested in ## Workflow 1. **Build user profile**: From the provided context, map out: - Primary interest domains (2-3) - Current knowledge level in those domains - Growth direction — where they're headed vs. where they are - Gaps — important adjacent topics they might not have considered 2. **Generate topic recommendations**: Create 3-5 personalized topics, each: - Connected to the user's interests but not obvious (avoid recommending what they already know) - Timely — relevant to current trends, challenges, or opportunities in their field - Actionable — each topic leads naturally to specific books - Diverse — cover different angles (depth in core area + breadth in adjacent areas + one wildcard) 3. **For each topic, curate books**: Select 2-3 books that best explore the topic, explaining why each was chosen for this specific user. 4. **Add "why now" reasoning**: For each topic, explain why this is the right time for this person to explore it. 5. **Language**: Detect the user's input language and respond in the same language. ## Output Format ``` # 📚 Your Personalized Topics — For You Based on your profile: [1-sentence summary of user context] --- ### Topic 1: [Topic Title] **Why now**: [1-2 sentences on why this topic is relevant to the user right now] **The angle**: [What specific perspective on this topic is most valuable for this user] 📖 **Recommended books**: 1. **[Book Title]** by [Author] — [Why this book, for this person] 2. **[Book Title]** by [Author] — [Why this book, for this person] 💡 **Key question this topic answers**: [A compelling question that makes the user want to explore] --- ### Topic 2: [Topic Title] **Why now**: [Relevance explanation] **The angle**: [Specific perspective] 📖 **Recommended books**: 1. **[Book Title]** by [Author] — [Why] 2. **[Book Title]** by [Author] — [Why] 💡 **Key question this topic answers**: [Compelling question] --- ### Topic 3: [Topic Title] 🌟 Wildcard **Why now**: [This one is deliberately outside your usual domain — here's why it matters] **The angle**: [How this connects back to your core interests in an unexpected way] 📖 **Recommended books**: 1. **[Book Title]** by [Author] — [Why] 2. **[Book Title]** by [Author] — [Why] 💡 **Key question this topic answers**: [Compelling question] --- ### 🎯 My top pick for you today **[Topic X]** — [One sentence on why to start here] ``` ### Example Output **User input**: "I'm a product manager at a fintech startup, interested in behavioral economics and AI. Recently been thinking about user retention." --- # 📚 Your Personalized Topics — For You Based on your profile: Fintech PM exploring behavioral economics and AI, with a current focus on user retention. --- ### Topic 1: The Psychology of Financial Decisions **Why now**: Your retention challenges might be rooted in how users emotionally relate to money decisions in your product. **The angle**: Not general behavioral economics — specifically how cognitive biases shape financial product engagement. 📖 **Recommended books**: 1. **Misbehaving** by Richard Thaler — The foundational work on behavioral economics in real-world decisions, directly applicable to fintech product design 2. **Dollars and Sense** by Dan Ariely — Practical exploration of irrational money behaviors that affect user engagement 💡 **Key question this topic answers**: Why do users abandon financial tools even when they know those tools help them? --- ### Topic 3: Biomimicry in System Design 🌟 Wildcard **Why now**: Biological systems have solved retention and engagement over millions of years — ecosystems keep organisms coming back. **The angle**: How patterns from nature (symbiosis, feedback loops, adaptation) can inspire stickier product design. 📖 **Recommended books**: 1. **Biomimicry** by Janine Benyus — The original work on learning design principles from nature 2. **The Nature of Technology** by W. Brian Arthur — How technology evolves like biological systems 💡 **Key question this topic answers**: What can millions of years of natural selection teach us about building products people can't leave? --- ## Guidelines - Always include at least one "wildcard" topic — something unexpected that connects to the user's interests in a non-obvious way - Topics should be specific enough to act on, not vague categories ("The Psychology of Financial Decisions" > "Psychology") - Each topic's book recommendations should be tailored to the user, not just "best books on this topic" - The "Why now" should feel personally relevant, not generic - Include a "top pick" recommendation to reduce decision paralysis - If user context is too sparse, ask 2-3 clarifying questions before generating recommendations