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edge-hint-extractor

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

2|Updated Apr 6, 2026
Search Tags:#market analysis#data processing#financial analysis#news analysis#edge hint extraction
One-click install
npx skills add https://github.com/k1064190/stock-expectation --skill edge-hint-extractor-k1064190
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: edge-hint-extractor
Source: https://github.com/k1064190/stock-expectation/tree/main/.claude/skills/_archived/edge-hint-extractor
Command: npx skills add https://github.com/k1064190/stock-expectation --skill edge-hint-extractor-k1064190

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the extraction of edge hints from market observations and news, providing structured data for concept synthesis and auto detection.

Core Features & Use Cases

  • Edge Hint Extraction: Converts raw observation signals into structured edge hints.
  • Optional LLM Ideation: Augments hints with ideas generated by an external LLM.
  • Output: Generates hints.yaml for downstream concept synthesis and auto detection.
  • Use Case: For a financial analyst who wants to quickly synthesize market observations and news into actionable insights.

Quick Start

Run the script to generate hints from market data and news reactions: uv run python .claude/skills/edge-hint-extractor/scripts/build_hints.py --market-summary path/to/market_summary.json --anomalies path/to/anomalies.json --news-reactions path/to/news_reactions.csv --output-dir path/to/output

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 edge hints from market observations and news reactions?▼

To extract edge hints from market observations, run the build_hints.py script with market summary JSON, anomalies JSON, and news reactions CSV files to generate a structured hints.yaml output.

What is edge hint extraction for financial analysis workflows?▼

Edge hint extraction converts raw market observation signals and news sentiment into structured data, automating concept synthesis and auto detection for downstream financial analysis workflows.

What input data formats are required for generating a canonical hints.yaml file?▼

Generating a canonical hints.yaml file requires market summary JSON, anomalies JSON, and news reactions CSV as input data formats to process market observations and extract structured edge hints.

Can I use an external LLM to augment market news analysis and concept synthesis?▼

Yes, you can use an optional external LLM to augment market news analysis and concept synthesis, adding generated ideas to the extracted edge hints during the data processing workflow.

Do I need Python 3.9 and PyYAML to process market data and generate hints.yaml?▼

Yes, you need Python 3.9 or higher and the PyYAML dependency to process market data and generate the canonical hints.yaml output for your financial analysis workflow.

What are the limitations of automated edge hint extraction from market data?▼

Automated edge hint extraction relies on predefined input formats like market summary JSON and anomalies JSON, meaning unstructured market data requires prior formatting before processing into hints.yaml.

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