skills.rest
MileniumTick
MileniumTickCOMMUNITY
Explore all skills by this author →
GitHub

edge-hint-extractor

Convert market observations, anomalies, and news reactions into structured hints.yaml.

Updated Mar 25, 2026
Search Tags:#anomalies#yaml-output#edge-hints#market-observations#news-reactions#llm-augmentation
One-click install
npx skills add https://github.com/MileniumTick/skills --skill edge-hint-extractor-mileniumtick
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: edge-hint-extractor
Source: https://github.com/MileniumTick/skills/tree/main/skills/edge-hint-extractor
Command: npx skills add https://github.com/MileniumTick/skills --skill edge-hint-extractor-mileniumtick

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Converts raw market observations (market_summary), anomalies, and news reactions into structured edge hints that can be consumed by downstream synthesis and auto-detection pipelines.

Core Features & Use Cases

  • Deterministic generation of edge hints from market signals, anomalies, and news reactions
  • Optional augmentation with external LLMs to surface ideas and hypotheses
  • Outputs a canonical hints.yaml with generation metadata for integration into automated workflows

Quick Start

Run the edge-hint-extractor with market_summary, anomalies, and optional news_reactions 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 market anomalies and news reactions into structured YAML hints?▼

A canonical hints.yaml file is generated by consuming market_summary.json, anomalies.json, and optional news_reactions to produce deterministic edge hints with generation metadata for downstream concept synthesis pipelines.

What is the best way to structure raw market observations for downstream synthesis pipelines?▼

Structuring raw market observations into a canonical hints.yaml file provides deterministic edge hints and generation metadata, enabling seamless integration into downstream auto-detection and concept synthesis workflows.

Do I need Python and PyYAML to generate edge hints from market summary data?▼

Yes, Python 3.9+ and the PyYAML library are required dependencies to execute the extraction process and successfully output the canonical hints.yaml file from market summary and anomaly data.

Can I use an external LLM to augment market anomaly extraction?▼

Yes, an optional external LLM can augment the deterministic rule-based extraction to surface additional ideas and hypotheses from market observations, anomalies, and news reactions within the generated hints.

Does the edge hint extraction tool accept news reactions in CSV format?▼

Yes, the tool accepts optional news_reactions input in both CSV and JSON formats, alongside the required market_summary.json and anomalies.json files, to generate the final hints.yaml output.

When should I use a rule-based extractor instead of an LLM for market signal processing?▼

Use a rule-based extractor when deterministic edge hints and generation metadata are required for reliable automated workflows, utilizing an external LLM only as an optional augmentation layer to surface hypotheses.

Related Skills

Explore More Data & Analytics Skills→
anysiteioanysiteio
19

anysite-market-research

Integrate YC startup data, SEC filings, social signals, and web data into market research.

Official
Advanced
Fabio29TFabio29T
2

edge-hint-extractor

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

Community
Intermediate
pasie15pasie15

edge-hint-extractor

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

Community
Intermediate
YlopoYlopo

realestate-quick

Assess property value, rental yield, and neighborhood via WebSearch.

Official
Intermediate