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Found 1,942 Skills
Spot and evaluate trending product opportunities on Amazon, and tell a real trend from a fad. Reads trend signals, judges where a trend is in its curve, and decides whether a seller can enter in time to profit. Use when a user asks about trending products, hot products, viral products, jumping on a trend, trend spotting, or whether a product is a fad. Trigger phrases: "trending products", "hot products", "viral product", "is this a trend or a fad", "trend spotting", "should I jump on this trend". Works with zero tools.
Create structured handoff for session continuation. Triggers: handoff, pause, save context, end session, pick up later, continue later.
Range bar evaluation metrics for quant trading. TRIGGERS - range bar metrics, Sharpe ratio, WFO metrics, PSR DSR MinTRL.
LLM observability platform for tracing, evaluation, prompt management, and cost tracking. Use when setting up Langfuse, monitoring LLM costs, tracking token usage, or implementing prompt versioning.
Build and run evaluators for AI/LLM applications using Phoenix.
Coordinate multi-agent code review with specialized perspectives. Use when conducting code reviews, analyzing PRs, evaluating staged changes, or reviewing specific files. Handles security, performance, quality, and test coverage analysis with confidence scoring and actionable recommendations.
Master fine-tuning of large language models for specific domains and tasks. Covers data preparation, training techniques, optimization strategies, and evaluation methods. Use when adapting models for specialized applications, reducing inference costs, or improving domain-specific performance.
Build discounted cash flow (DCF) valuation models in Excel. Use when creating DCF models, calculating enterprise value, or valuing companies. Trigger with phrases like 'excel dcf', 'build dcf model', 'calculate enterprise value'.
Use when user wants to find a note to publish as a blog post. Triggers on「选一篇笔记发博客」「note to blog」「写博客」「博客选题」. Scans Obsidian notes via Python script, evaluates blog-readiness, supports batch selection with fast/deep dual-track and parallel Agent dispatch.
Technical spike and research investigation specialist. Use when exploring options for a technical decision, conducting timeboxed investigations, or evaluating technology choices.
Use when receiving UAT feedback, bug reports, user testing results, stakeholder feedback, QA findings, or any batch of issues to investigate. Investigates each item BEFORE creating issues, classifies by type and priority, creates well-formed GitHub issues with proper project board integration.
Use when "Polars", "fast dataframe", "lazy evaluation", "Arrow backend", or asking about "pandas alternative", "parallel dataframe", "large CSV processing", "ETL pipeline", "expression API"