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Found 21 Skills
Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.
Benchmark Instagram posts and Reels to discover winning content patterns, shortlist high-value examples, and extract reusable hooks and formats.
Batch identify candidate stocks with mature breakout patterns, healthy volume-price structures, and good catalyst alignment, and output priorities, trigger conditions, and failure boundaries. Suitable for scenarios such as short-to-medium-term stock selection, pre-market candidate pool sorting, and screening leading candidate stocks in sector rotation.
Analyzes your recent Claude Code chat history to identify coding patterns, development gaps, and areas for improvement, generating a personalized growth report with actionable recommendations.
Search Mobbin for real app UI screenshots and visually analyze them. Required before calling the `search_screens` MCP tool — this skill defines how to plan searches, respond, and build HTML evidence boards when the screens are the answer. Use whenever the user asks about UI/UX design patterns, wants to see how other apps handle a screen or flow, needs design inspiration or references, asks to compare UI approaches across apps, mentions Mobbin, or whenever `search_screens` would be relevant. Trigger aggressively for any design-related question — even if screenshots aren't explicitly requested.
Log emotions with optional structure. Use when user says "feel", "feeling", "mood", "tired", "sleepy", "frustrated", "happy", "excited".
Analyze recent coding patterns and generate a personalized developer growth report.
Use this skill when you need to run the unslop repo, analyze a domain for repetitive AI defaults, generate a reusable skill file, and verify that the output is specific and materially different from the baseline.
Screen post-earnings gap-up stocks for PEAD (Post-Earnings Announcement Drift) patterns. Analyzes weekly candle formation to detect red candle pullbacks and breakout signals. Supports two input modes - FMP earnings calendar (Mode A) or earnings-trade-analyzer JSON output (Mode B). Use when user asks about PEAD screening, post-earnings drift, earnings gap follow-through, red candle breakout patterns, or weekly earnings momentum setups.