edge-hint-extractor

Name: edge-hint-extractor
Availability: InStock
Author: fabio29t

Extract structured edge hints from market observations into canonical hints.yaml files.

2|Updated Mar 4, 2026

Search Tags:#automation #llm #market analysis #data processing #trading #hints

Ready to run

npx skills add https://github.com/Fabio29T/Trading-Skills --skill edge-hint-extractor

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: edge-hint-extractor
Source: https://github.com/Fabio29T/Trading-Skills/tree/main/skills/edge-hint-extractor
Command: npx skills add https://github.com/Fabio29T/Trading-Skills --skill edge-hint-extractor

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill transforms raw market observations, anomalies, and news reactions into structured "edge hints" that can be used for further analysis and automated trading strategies.

Core Features & Use Cases

Structured Hint Generation: Creates canonical hints.yaml files from various market data inputs.
LLM Augmentation: Optionally integrates LLM-generated ideas to enrich the hints.
Use Case: Automatically process daily market news and anomalies to generate a list of potential trading opportunities, formatted for downstream systems.

Quick Start

Use the edge-hint-extractor skill to process market summary and anomaly files for today's date.

Frequently Asked Questions about edge-hint-extractor

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract structured trading hints from market observations and news reactions?▼

To extract structured trading hints from market observations, you process raw market anomalies and news data using Python scripts. This generates a canonical hints.yaml file for downstream concept synthesis and automated trading workflows.

What is a structured edge hint in market analysis?▼

A structured edge hint in market analysis is a formatted output derived from raw market anomalies and news reactions. It standardizes trading opportunities into a canonical hints.yaml file for automated strategy detection and concept synthesis.

Can I use LLM augmentation to enrich market anomaly hints?▼

Yes, you can use LLM augmentation to enrich market anomaly hints. The extraction process supports optional LLM-generated ideas via CLI or pre-written files to enhance the structured hints.yaml output for trading workflows.

What's the best way to convert daily market news into a canonical hints.yaml format?▼

The best way to convert daily market news into a canonical hints.yaml format is by running rule-based Python scripts. These scripts process market summary and anomaly files to automatically generate structured edge hints for trading.

Do I need Python dependencies to generate edge hints from market data?▼

You need Python to run the rule-based scripts for generating edge hints from market data. The process operates via CLI or pre-written files, requiring no additional dependencies to output the structured hints.yaml.

When should I use rule-based generation over LLM augmentation for market hints?▼

Use rule-based generation for market hints when you need deterministic processing of observations, and use LLM augmentation when you need enriched, conceptual synthesis. Both methods output to the same canonical hints.yaml format.