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edge-pipeline-orchestrator

Orchestrate multi-stage edge research workflows from candidate detection to strategy export.

2|Updated Apr 6, 2026
Search Tags:#market data#pipeline orchestration#review-loop#strategy design#edge research
One-click install
npx skills add https://github.com/k1064190/stock-expectation --skill edge-pipeline-orchestrator-k1064190
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: edge-pipeline-orchestrator
Source: https://github.com/k1064190/stock-expectation/tree/main/.claude/skills/_archived/edge-pipeline-orchestrator
Command: npx skills add https://github.com/k1064190/stock-expectation --skill edge-pipeline-orchestrator-k1064190

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires auto_detect_candidates.py, build_hints.py, synthesize_edge_concepts.py, design_strategy_drafts.py, review_strategy_drafts.py, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides a comprehensive framework for orchestrating the entire edge research pipeline, including candidate detection, strategy design, review, revision, and export. It streamlines complex, multi-stage workflows for coordinating edge research efforts.

Core Features & Use Cases

  • Full Pipeline Orchestration: Automates all stages from raw OHLCV data to exportable strategies.
  • Pipeline Resumption: Continues partially completed pipelines from the drafts stage.
  • Feedback Loop: Supports review and revision of existing strategy drafts with a feedback loop.
  • Dry Run: Allows users to preview pipeline results without exporting.
  • Use Case: Ideal for researchers looking to automate the process of generating and reviewing strategy drafts based on various market data and edge hints.

Quick Start

Run the full edge research pipeline with:

python3 scripts/orchestrate_edge_pipeline.py \
  --tickets-dir path/to/tickets/ \
  --output-dir reports/edge_pipeline/

Frequently Asked Questions about edge-pipeline-orchestrator

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

FAQPage Schema
How do I automate end-to-end edge research workflows from market data to exportable strategies?▼

Edge research workflows can be automated end-to-end by running a pipeline orchestrator script that processes raw OHLCV tickets, detects candidates, synthesizes concepts, designs drafts, and exports final strategies to a specified output directory.

Can I resume a partially completed strategy design pipeline from the drafts stage?▼

Yes, pipeline orchestration supports resumption from the drafts stage, allowing you to continue partially completed workflows and apply a review-loop feedback mechanism to revise existing strategy drafts without restarting.

What is the best way to preview edge research results without exporting the final strategy?▼

The best way to preview results is using the dry run feature, which executes the edge research pipeline stages and displays strategy drafts without committing to the final export action.

Do I need multiple Python scripts to orchestrate market data processing and review-loop feedback?▼

Yes, orchestrating this workflow requires multiple Python scripts including auto_detect_candidates.py, build_hints.py, synthesize_edge_concepts.py, design_strategy_drafts.py, and review_strategy_drafts.py to manage the distinct processing stages.

How does the review-loop mechanism work when revising strategy drafts in an edge research pipeline?▼

The review-loop mechanism works by routing designed strategy drafts through an automated review script that provides feedback, enabling iterative revisions before the final export stage of the edge research workflow.

What are the limitations of using pipeline orchestration for edge research and strategy design?▼

Limitations include dependency on specific Python script execution environments and structured ticket directories containing raw market data, requiring precise input formatting before the orchestration pipeline can successfully process and export strategies.

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