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PNWSkyHippy
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edge-hint-extractor

Convert market observation signals into structured hints.yaml files.

Updated Aug 23, 2026
Search Tags:#anomalies#yaml-output#edge-hints#market-observations#news-reactions#llm-augmentation
One-click install
npx skills add https://github.com/PNWSkyHippy/Trading_Bot_V2 --skill edge-hint-extractor-pnwskyhippy
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: edge-hint-extractor
Source: https://github.com/PNWSkyHippy/Trading_Bot_V2/tree/main/claud-skills/skills/edge-hint-extractor
Command: npx skills add https://github.com/PNWSkyHippy/Trading_Bot_V2 --skill edge-hint-extractor-pnwskyhippy

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Converts daily market observations and signals into structured edge hints that feed downstream concept synthesis and automated detection pipelines.

Core Features & Use Cases

  • Transforms raw inputs such as market_summary.json, anomalies.json, and news_reactions into a canonical hints.yaml format.
  • Supports optional augmentation with LLM ideas via an external CLI (--llm-ideas-cmd) or pre-written YAML hints (--llm-ideas-file).
  • Serves as the first step in the observe -> abstract -> design -> pipeline, enabling consistent downstream analysis and detection.

Quick Start

Run the build_hints.py script with your market_summary, anomalies, and news_reactions inputs to generate 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 and anomalies into structured YAML hints?▼

To convert raw market observations into structured YAML hints, you process market_summary.json, anomalies.json, and news_reactions inputs through a script that generates a canonical hints.yaml file. This structures daily signals into edge hints for downstream analysis.

What are edge hints in market observation pipelines and when do I need them?▼

Edge hints are structured signals extracted from daily market observations, anomalies, and news reactions. You need them when building an observe-abstract-design pipeline to enable consistent downstream concept synthesis and automated detection.

Can I use LLM augmentation to enrich market signal extraction with custom ideas?▼

Yes, you can augment market signal extraction with LLM ideas by passing an external CLI command via --llm-ideas-cmd or a pre-written YAML file using --llm-ideas-file. This enriches the generated hints.yaml with additional concepts.

What input formats are supported for feeding news reactions into a market hints generator?▼

The market hints generator supports news reactions in both CSV and JSON formats. These inputs combine with market_summary.json and anomalies.json to produce the structured edge hints output.

How do I integrate structured edge hints into downstream concept synthesis or auto detection?▼

You integrate structured edge hints by feeding the generated hints.yaml file, which includes generation metadata, directly into your concept synthesis or auto detection pipelines. This serves as the first step in the observe-abstract-design workflow.

Do I need PyYAML installed to generate YAML output from market signals?▼

Yes, PyYAML is a required dependency for generating YAML output from market signals. The extraction script relies on it to serialize processed market observations, anomalies, and news reactions into the canonical hints.yaml format.

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