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

alphaear-signal-tracker

GitHub

追踪金融投资信号演变，基于新市场信息评估信号是加强、减弱还是证伪。通过研究和分析流程更新信号状态及置信度。

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

Trigger Scenarios

监控金融信号变化根据新信息判断信号强度

Install

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

More Options

Non-standard path

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

Use without installing

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.