Claude Skill

tanmingtao1994-gif/fincrew

FinCrew is a self-evolving multi-agent financial assistant powered by OpenClaw. Built with TypeScript, it orchestrates AI agents for portfolio analysis, market research, and financial planning.

Overview

Stars179
Forks11
LanguageTypeScript
Last pushed2026-04-01
Last synced2026-06-22
View on GitHub

Repository

Ownertanmingtao1994-gif
Repositoryfincrew
Full nametanmingtao1994-gif/fincrew
Repo ID1,197,215,683

Install this Skill

git clone https://github.com/tanmingtao1994-gif/fincrew.git

Registry

Typeopenclaw_skill
Quality score75/100
Verificationreadme_parsed
Last verified2026-06-22
Platforms
ClaudeOpenClaw
Capabilities
browsermemoryterminalworkflow
Detected files
README.mdpackage.json
Config keys
UIDTWITTER_BEARER_TOKENREDDIT_CLIENT_IDREDDIT_CLIENT_SECRETYAHOO_FINANCE_API_KEYPACKAGE_JSON
Install methods
  • git clone https://github.com/tanmingtao1994-gif/fincrew.git
  • npm install

Summary

FinCrew is a self-evolving multi-agent financial assistant built on OpenClaw. It leverages TypeScript to orchestrate specialized AI agents that collaborate on financial analysis, portfolio management, and market research tasks.

Chinese description

由OpenClaw驱动的自我进化多智能体金融助手

Key features

  • Multi-agent collaboration for financial tasks
  • Self-evolving agent behavior over time
  • Built on the OpenClaw framework
  • TypeScript codebase for reliability and scalability

Use cases

  • Automated portfolio analysis and rebalancing
  • Market trend research and sentiment analysis
  • Personalized financial planning assistance
  • Risk assessment and reporting

README excerpt

# FinCrew English | [简体中文](./README.zh.md) A self-evolving multi-agent financial assistant powered by [OpenClaw](https://github.com/nicepkg/openclaw). ## Overview FinCrew is a multi-agent financial assistant built on OpenClaw, featuring 5 specialized AI agents that collaborate on investment analysis, trading decisions, and portfolio management. Its core feature is a **self-evolution memory loop** that learns from every trade to continuously improve decision quality. **Use Cases**: - Daily market analysis and trading decision support for individual investors - Track and integrate KOL opinions into investment decisions - Customized analysis based on personal investment philosophy and risk preferences - Trade reviews and experience accumulation ## Agent Architecture ``` ┌─────────────────────────────────────────────────┐ │ Financial Manager │ │ Senior Private Wealth Advisor │ │ Coordinates all agents & makes final decisions │ └───────────┬──────┬──────┬──────┬────────────────┘ │ │ │ │ ┌──────▼──┐ ┌─▼────┐ ┌▼─────┐ ┌▼────────┐ │ Info │ │Macro │ │Tech │ │Reviewer │ │Processor│ │Analyst│ │Analyst│ │ │ │ │ │ │ │ │ │ │ │ Data │ │Market │ │Chart │ │ Trade │ │Collection│ │Trends │ │Signals│ │ Review │ └─────────┘ └───────┘ └──────┘ └──────────┘ ``` | Agent | Role | Responsibility | |-------|------|----------------| | **Financial Manager** | Coordinator | Dispatch tasks, synthesize analysis, make buy/hold/sell decisions | | **Info Processor** | Intelligence Officer | Collect stock data, news, KOL opinions, insider trading info | | **Macro Analyst** | Macro Strategist | Evaluate market trends, sector rotatio

Topics

No topics yet.

Explore more

Data from GitHub. Synced on 2026-06-22