Proactive Agent

A proactive agent that anticipates needs and takes initiative. Monitors environment, suggests actions, and proposes tasks based on context.

Features

• Context Analysis: Analyzes current time, market status, content pipeline
• Action Suggestion: Predicts needs and generates actionable suggestions
• Task Initiation: Proactively proposes tasks based on context
• Priority Management: 3-tier priority system (Trading > Content > Learning)

Quick Start

# Get next suggested action
python scripts/action_suggester.py --next

# Analyze current context
python scripts/context_analyzer.py --json

# Propose tasks
python scripts/task_initiator.py --propose

Components

1. Context Analyzer (scripts/context_analyzer.py)

Analyzes:

• Current time slot (morning_startup, trading_hours, content_window, etc.)
• Market status (open/closed)
• Content pipeline status
• Pending tasks
• Recent interactions

2. Action Suggester (scripts/action_suggester.py)

Generates suggestions based on:

• Time-based rules (trading hours, content window)
• Market conditions
• Content pipeline status
• Pending tasks
• Learning optimization needs

3. Task Initiator (scripts/task_initiator.py)

Proactively proposes tasks with:

• Task ID and timestamp
• Priority level
• Step-by-step instructions
• Auto-save to file

4. Proactive Monitor (scripts/proactive_monitor.py)

Continuous monitoring:

• Daemon mode for background operation
• Configurable check intervals
• Alert cooldown management

Configuration

Edit config/proactive_config.yaml:

proactive_level: 7          # 1-10, higher = more proactive
monitor_interval: 300       # Check interval in seconds
alert_cooldown: 1800        # Alert cooldown in seconds

priority_weights:
  trading: 10
  content: 8
  tasks: 6
  learning: 4
  routine: 3

Time Rules

The agent recognizes these time slots:

Integration

With AGENTS.md

Add to session startup:

1. Run proactive-agent: python skills/proactive-agent/scripts/action_suggester.py --next

With HEARTBEAT.md

Add to heartbeat checks:

python skills/proactive-agent/scripts/action_suggester.py --next

Example Output

[HIGH] 盘中交易时段
Suggestion: 观察候选股票，等待买入机会

[HIGH] 下午内容发布窗口
Suggestion: 检查待发布内容，准备发布小红书笔记

[MEDIUM] 晚间例行检查
Suggestion: 检查今日数据，记录交易日志

Use Cases