AI agent skill · by @RKiding

Alphaear Signal Tracker

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.

Archived 2026-10-23 · Source updated 2026-03-29 · License: Apache-2.0. Check the original repository for the latest version.

SKILL.md instructions

# 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:**
1.  **Research**: Use **FinResearcher Prompt** to gather facts/price for a signal.
2.  **Analyze**: Use **FinAnalyst Prompt** to generate the initial `InvestmentSignal`.
3.  **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):**

```python
# 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.
```

## Dependencies

-   `agno` (Agent framework)
-   `sqlite3` (built-in)

Ensure `DatabaseManager` is initialized correctly.