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edge-hint-extractor

Extract structured edge hints from market observations and news reactions into hints.yaml.

Updated Apr 17, 2026
Search Tags:#automation#yaml#anomaly-detection#edge-hints#llm-augmentation#trading-research#market-observation
One-click install
npx skills add https://github.com/pasie15/claude-trading-skills-marketplace --skill edge-hint-extractor-pasie15
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: edge-hint-extractor
Source: https://github.com/pasie15/claude-trading-skills-marketplace/tree/main/plugins/trading-strategy-tools/skills/edge-hint-extractor
Command: npx skills add https://github.com/pasie15/claude-trading-skills-marketplace --skill edge-hint-extractor-pasie15

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Convert raw market observations, anomaly lists, and news reactions into structured, canonical edge hints so research and detection pipelines can consume consistent inputs for concept synthesis and automated detection.

Core Features & Use Cases

  • Deterministic hint generation: Rule-based extraction from market_summary and anomalies to produce reproducible hints.yaml outputs.
  • Optional LLM augmentation: Accept external LLM CLI output or a prewritten YAML of ideas to expand hints while preserving schema and metadata.
  • Integration-ready output: Emits hints.yaml with generation metadata and hint counts for downstream clustering, concept synthesis, or auto-detection workflows.
  • Use Case: Daily market-monitoring pipeline formats breadth, flow, and news reactions into actionable hypothesis hints for quant researchers and strategy designers.

Quick Start

Run the build_hints.py script with your market_summary.json and anomalies.json and optionally include a news_reactions file to produce reports/edge_hint_extractor/hints.yaml.

Frequently Asked Questions about edge-hint-extractor

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

FAQPage Schema
How do I convert raw market observations into structured trading hints?▼

The edge hint extraction process applies rule-based deterministic logic to market summaries and anomaly lists, producing reproducible canonical hints in a hints.yaml file for downstream concept synthesis and detection.

Can I use LLM augmentation to expand market anomaly detection hints?▼

The edge hint extractor accepts external LLM CLI output or prewritten YAML files to expand hints while preserving the canonical schema and generation metadata for downstream detection workflows.

What input formats are needed to generate a hints.yaml file from market data?▼

You need market_summary.json and anomalies.json as required inputs, with optional news_reactions.csv or news_reactions.json files, to generate the hints.yaml output file.

Does edge-hint-extractor work with daily market monitoring workflows?▼

The Skill is specifically designed for daily market monitoring workflows that ingest market summaries, anomalies, and news reactions to output actionable hypothesis hints for quant researchers and strategy designers.

What's the best way to format news reactions for edge hint generation?▼

For deterministic edge hint generation, rule-based extraction from market_summary and anomalies produces reproducible hints.yaml outputs, offering a consistent baseline compared to relying solely on LLM interpretation.

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