Agent Skills › ninehills/skills › alphaear-signal-tracker

alphaear-signal-tracker

GitHub

追踪金融投资信号演变，根据新市场信息评估并更新信号状态（强化、弱化或证伪）。结合研究与分析提示词，对比新旧信息以确定影响方向及置信度变化。

alphaear-signal-tracker/SKILL.md ninehills/skills

触发场景

监控金融信号是否需要更新根据新新闻或价格数据判断投资信号强度变化

安装

npx skills add ninehills/skills --skill alphaear-signal-tracker -g -y

更多选项

非标准路径

npx skills add https://github.com/ninehills/skills/tree/main/alphaear-signal-tracker -g -y

不安装直接使用

npx skills use ninehills/skills@alphaear-signal-tracker

指定 Agent (Claude Code)

npx skills add ninehills/skills --skill alphaear-signal-tracker -a claude-code -g -y

安装 repo 全部 skill

npx skills add ninehills/skills --all -g -y

预览 repo 内 skill

npx skills add ninehills/skills --list

SKILL.md

Frontmatter

{
    "name": "alphaear-signal-tracker",
    "description": "Track finance investment signal evolution and update logic based on new finance market information. Use when monitoring finance signals and determining if they are strengthened, weakened, or falsified."
}

AlphaEar Signal Tracker Skill

Overview

This skill provides logic to track and update investment signals. It assesses how new market information impacts existing signals (Strengthened, Weakened, Falsified, or Unchanged).

Capabilities

1. Track Signal Evolution

1. Track Signal Evolution (Agentic Workflow)

YOU (the Agent) are the Tracker. Use the prompts in references/PROMPTS.md.

Workflow:

Research: Use FinResearcher Prompt to gather facts/price for a signal.
Analyze: Use FinAnalyst Prompt to generate the initial InvestmentSignal.
Track: For existing signals, use Signal Tracking Prompt to assess evolution (Strengthened/Weakened/Falsified) based on new info.

Tools:

Use alphaear-search and alphaear-stock skills to gather the necessary data.
Use scripts/fin_agent.py helper _sanitize_signal_output if needing to clean JSON.

Key Logic:

Input: Existing Signal State + New Information (News/Price).
Process:
1. Compare new info with signal thesis.
2. Determine impact direction (Positive/Negative/Neutral).
3. Update confidence and intensity.
Output: Updated Signal.

Example Usage (Conceptual):

# This skill is currently a pattern extracted from FinAgent.
# In a future refactor, it should be a standalone utility class.
# For now, refer to `scripts/fin_agent.py`'s `track_signal` method implementation.