Research

weizhena

Conduct preliminary research on a topic and generate research outline. For academic research, benchmark research, technology selection, etc.

Install

The individual skill folder is not confirmed. This command opens the repository’s skill selector; follow the upstream instructions.

skills CLI

npx skills add Weizhena/Deep-Research-skills

Competitive IntelligenceAnthropicOfficialCompetitive analysis two ways - the in-deal play against a named competitor, and win/loss patterns, competitor mentions, and battlecards across the book. Use when the user asks "competitive intel", "research competitors", "how do we compare to [competitor]", "what's new with [competitor]", "battlecard for [competitor]", "how do we beat [competitor]", "what's the play against [competitor] in…Other31.3k Repository stars
Knowledge SynthesisAnthropicOfficialCombines search results from multiple sources into coherent, deduplicated answers with source attribution. Handles confidence scoring based on freshness and authority, and summarizes large result sets effectively.Other31.3k Repository stars
User Research SynthesisAnthropicOfficialSynthesize qualitative and quantitative user research into structured insights and opportunity areas. Use when analyzing interview notes, survey responses, support tickets, or behavioral data to identify themes, build personas, or prioritize opportunities.Other31.3k Repository stars
ArxivNousResearchSearch arXiv papers by keyword, author, category, or ID.Other256.1k Repository stars
Fact CheckershubhamsabooSystematic fact verification and misinformation identification using evidence-based analysis. Use when: verifying claims, checking facts, identifying misinformation, evaluating source credibility, or when user asks to "fact check", "verify", "is this true", or mentions claims that need validation.Other142.6k Repository stars
PennylaneK-DenseHardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow.Other53.8k Repository stars

Install

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