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Edge Hint Extractor

tradermonty/claude-trading-skills

This extracts structured edge hints from market observations, anomalies, and news reactions, outputting a canonical hints.yaml for downstream concept synthesis. It's the first stage in a four-part workflow: observe, abstract, design, pipeline. You run it rule-based by default, but can augment with LLM-generated ideas either via subprocess or by passing a pre-written YAML file, which is handy if you're using Claude Code and want Claude to draft the hints itself. The file-based approach is a smart workaround for agentic workflows where you want the LLM in the loop without shelling out. Straightforward Python script with clear separation between deterministic extraction and optional creative augmentation.

skills.sh installs
3.5K
GitHub stars
3.7K
last updated
Aug 21, 2026
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Install

$ npx -y skills add tradermonty/claude-trading-skills --skill edge-hint-extractor --agent claude-code

Installs into .claude/skills of the current project.

Files

Edge Hint Extractor

Overview

Convert raw observation signals (market_summary, anomalies, news reactions) into structured edge hints. This skill is the first stage in the split workflow: observe -> abstract -> design -> pipeline.

When to Use

  • You want to turn daily market observations into reusable hint objects.
  • You want LLM-generated ideas constrained by current anomalies/news context.
  • You need a clean hints.yaml input for concept synthesis or auto detection.

Prerequisites

  • Python 3.9+
  • PyYAML
  • Optional inputs from detector run:
    • market_summary.json
    • anomalies.json
    • news_reactions.csv or news_reactions.json

Output

  • hints.yaml containing:
    • hints list
    • generation metadata
    • rule/LLM hint counts

Workflow

SKILL.md on GitHub

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Details

Repository
github.com/tradermonty/claude-trading-skills
Categories
Backend & APIs, AI & Agent Building, Marketing & SEO, Finance & Trading
First seen
Jun 3, 2026

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