Claude Code 안에서 살아가는 AI-powered Product Analyst. 일반 영어에 대한 비즈니스 질문, 유효성 검사, 브랜드 차트 및 이해 관계자 독서 슬라이드 데크를 얻을 - 분, 아니 일.

18 에이전트 | 55 기술 | 40 헬퍼 모듈 | DAG 기반 병렬 실행 | PDF + HTML + Google Docs 수출

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이것은 무엇인가?

AI Analyst Plus는 Claude Code를 제품 분석 시스템으로 전환합니다. 그것은 결합합니다:

• Skills (.claude/skills/) - 표준 Claude는 (chart styling, data validation, 질문 framing, stakeholder communication)를 따릅니다.
• Agents (agents/) - DAG 엔진에 의해 관현된 다단계 분석 워크플로우(question framing → data exploration → analysis → storytelling → deck 생성)
• Helpers ([T2]]) - 차트, SQL 유효성 검사, 데이터 연결성, 통계 검사 및 기타 Python 모듈
• Knowledge (.knowledge/]) - dataset 컨텍스트, 보정, 검증된 SQL 패턴 및 비즈니스 광택에 대한 지속적인 메모리

Claude에 대해 이야기하고 있습니다. 질문, 슬래시 명령을 호출, 또는 적용 할 수있는 능력과 에이전트를 Claude 그림.

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빠른 시작

1. 복제 및 설치

**2.

AI Analyst Plus

An AI-powered product analyst that lives inside Claude Code. Ask business questions in plain English, get validated analyses, branded charts, and stakeholder-ready slide decks — in minutes, not days.

18 agents | 55 skills | 40 helper modules | DAG-based parallel execution | PDF + HTML + Google Docs export

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What This Is

AI Analyst Plus turns Claude Code into a product analytics system. It combines:

• Skills (.claude/skills/) — standards Claude follows automatically (chart styling, data validation, question framing, stakeholder communication)
• Agents (agents/) — multi-step analytical workflows orchestrated by a DAG engine (question framing → data exploration → analysis → storytelling → deck creation)
• Helpers (helpers/) — Python modules for charting, SQL validation, data connectivity, statistical tests, and more
• Knowledge (.knowledge/) — persistent memory for dataset context, corrections, proven SQL patterns, and business glossary

You interact by talking to Claude. Ask a question, invoke a slash command, or let Claude figure out which skills and agents to apply.

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Quick Start

1. Clone and install

git clone https://github.com/ai-analyst-lab/ai-analyst-plus.git
cd ai-analyst-plus
pip install -e ".[dev]"

2. Connect your data (optional)

In Claude Code, run /connect-data to wire up a warehouse (DuckDB, Postgres, BigQuery, Snowflake) or a CSV directory. Curated public datasets with guides live in data/examples/.

3. Start Claude Code

claude

4. Ask a question

What's our conversion rate by device?

Or run the full pipeline:

/run-pipeline question="Why is conversion dropping on mobile?"

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Don’t Know What to Do? Just Ask.

Claude knows the entire system — every agent, skill, command, and dataset. If you’re stuck:

What can I do with this data?
What should I run to refresh the deck?
How do I connect my own CSV files?
Which agents handle root cause analysis?
Re-run just the chart maker and deck creator.

You don’t need to memorize anything in this README. Think of it as a reference — Claude is the guide.

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Five Things You Can Do

1. Ask a quick question

What's our conversion rate by device?

Claude queries the data and returns an answer with a chart. Simple questions get answered in under 2 minutes without running the full pipeline.

2. Run a full analysis

/run-pipeline question="What's driving the decline in conversion?"