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edge-candidate-agent

Detect and prioritize edge research candidates from daily market observations.

Updated Mar 25, 2026
Search Tags:#validation#yaml#pipeline#export#edge-candidate#market-detection
One-click install
npx skills add https://github.com/MileniumTick/skills --skill edge-candidate-agent-mileniumtick
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: edge-candidate-agent
Source: https://github.com/MileniumTick/skills/tree/main/skills/edge-candidate-agent
Command: npx skills add https://github.com/MileniumTick/skills --skill edge-candidate-agent-mileniumtick

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

The Edge Candidate Agent automates turning daily market observations into structured, pipeline-ready research tickets and Phase I-compatible strategy artifacts.

Core Features & Use Cases

  • Auto-detect edge ideas from EOD data and hints, building ranked watchlists and seed tickets.
  • Export candidate artifacts as strategy.yaml + metadata.json and validate against the Edge Finder contract.
  • Integrate with ideation loops (LLM hints) and perform preflight checks before pipeline execution.

Quick Start

Run daily auto-detection to generate a set of exportable candidate artifacts for the trade-strategy-pipeline.

Frequently Asked Questions about edge-candidate-agent

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

FAQPage Schema
How do I automate edge candidate detection from daily market observations?▼

To automate edge candidate detection, run daily auto-detection against EOD data and hints. This identifies and prioritizes research candidates, building ranked watchlists and seed tickets for downstream pipeline execution.

How do I export and validate strategy YAML files for a trade pipeline?▼

Exporting and validating strategy YAML files involves generating strategy.yaml plus metadata.json artifacts. You then validate these candidate artifacts against the Edge Finder contract to ensure they are pipeline-ready.

Do I need pandas and numpy to compute features for market edge detection?▼

Pandas and numpy are optional dependencies for computing features during market edge detection. You need Python 3.9+ and PyYAML for the core workflow, but pandas and numpy enhance feature computation capabilities.

Can I integrate LLM hints into an automated edge research workflow?▼

You can integrate LLM hints into an automated edge research workflow through ideation loops. The system combines these hints with EOD data to auto-detect edge ideas and generate structured research tickets.

What is included in preflight checks before pipeline execution?▼

Preflight checks before pipeline execution verify that exported strategy.yaml and metadata.json artifacts pass validation against the Edge Finder contract. This ensures candidates are structurally sound and ready for downstream pipeline execution.

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