review-pr
ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.
Parcourez et installez des milliers de compétences pour AI Agents dans le répertoire Killer-Skills. Compatible avec Claude Code, Windsurf, Cursor et plus.
ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.
Claude Code infrastructure template for empirical economics research papers
Work with Cargo commands, workspace configuration, dependency management, and build systems. Use when managing dependencies, configuring workspaces, building projects, running tests, or publishing crates. Handles Cargo.toml configuration, workspace setup, dependency resolution, and build commands.
Analyze student git activity, lab submissions, and project work for software engineering courses. Use when asked to update student analysis, check student activity, analyze the class, or when working with student rosters and git repositories. Handles inconsistencies in student behavior like multiple usernames, name variations, and missing data.
In-depth analysis of investment assets (gold, silver, bitcoin, equities, etc.) to support investment decisions. Use when the user wants to understand an assets current state, market analysis, patterns, forecasts, or whether it fits their situation. Triggers on investment analysis, analyze gold/bitcoin/silver, should I invest in X, current state of X, market analysis, is X a good investment.
Guide for reading, interpreting, and applying statutes, regulations, and rules in legal and compliance contexts. Use when the user asks about (1) how to read and interpret statutes, regulations, or rules, (2) statutory interpretation methods and canons of construction, (3) understanding legislative intent, (4) applying statutes to specific legal situations, (5) extracting requirements from legal text, (6) distinguishing between different types of legal requirements, or (7) cross-jurisdictional compliance analysis.
Analyze product/feature requirements for the PigeonPod project with software engineering rigor. Use when users ask to evaluate a requirements value, feasibility, architecture fit, implementation impact, risk, delivery scope, or tradeoffs. Always inspect current repository docs/code first (especially architecture and README files), then use MCP tools (including Context7) to verify external library/framework/API constraints before concluding.
Deep dive into a single content pillars performance across all platforms and reporting periods. Shows how the same pillar performs differently on each surface, identifies format and platform fit, and provides coaching on strategic direction per the clients pillar goals.
Analyse the codebase of the system. Look for dependencies. Understand how the business logic is implemented. Look for duplications.
주식 및 ETF 투자를 위한 종합 분석 스킬. 매수/매도 판단을 위한 분석을 수행합니다. 다음 상황에서 사용 - (1) 특정 종목/ETF 매수 검토 요청, (2) 보유 종목 매도 타이밍 분석, (3) 종목 비교 분석, (4) 투자 아이디어 검증, (5) 실적 발표 후 분석, (6) ETF 추세 매매 분석, (7) ETF 괴리율 확인
지정된 디렉토리/파일의 문제점을 분석하고 각 문제를 개별 GitHub 이슈로 등록합니다.
需求澄清与拆解,识别干系人并将需求条目标注所属对象与业务实体;仅产出结构化需求描述,不做建模定义,可作为后续建模输入