scout-loop

Crawl GitHub and arXiv leads into grounded engineering backlog tasks.

30|12|Updated Jun 21, 2026
One-click install
npx skills add https://github.com/anthony-chaudhary/fak --skill scout-loop
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: scout-loop
Source: https://github.com/anthony-chaudhary/fak/tree/main/.claude/skills/scout-loop
Command: npx skills add https://github.com/anthony-chaudhary/fak --skill scout-loop

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the disconnect between outward research signals and actionable engineering backlog by automating the crawl, study, and filing process.

Core Features & Use Cases

  • Automated Research Pipeline: Chains arXiv and GitHub crawlers into a study-and-witness workflow.
  • Grounded Backlog Generation: Automatically decomposes new research leads into small, independently-shippable tasks with verified code anchors.
  • Use Case: Use this to continuously monitor trending GitHub repositories and automatically convert high-value leads into witnessed, ready-to-implement tickets without manual intervention.

Quick Start

Run the scout-loop skill to begin the automated research-to-backlog cycle for new repository leads.

Frequently Asked Questions about scout-loop

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

FAQPage Schema
How do I automate converting GitHub repository research into engineering backlog tickets?

Automating research-to-backlog conversion requires chaining arXiv and GitHub crawlers into a study-and-witness workflow that decomposes high-value research leads into shippable engineering tasks with verified code anchors.

What is an automated research-to-backlog discovery loop for software engineering?

An automated research-to-backlog discovery loop is a cadenced process that crawls, studies, and witnesses external repository leads to continuously convert trending research signals into grounded, independently shippable backlog tickets.

How do I continuously monitor trending GitHub repositories for actionable engineering tasks?

Monitoring trending GitHub repositories for engineering tasks involves running a cadenced automation skill that crawls external repositories, studies their capabilities, and files witnessed leads as ready-to-implement tickets without manual intervention.

Do I need existing study-repo and field-borrow tools to run automated backlog generation?

Yes, automated backlog generation requires integration with existing study-repo and field-borrow tools to maintain strict honesty boundaries and verify capability presence when converting external research leads into shippable tasks.

Can I schedule automated crawling of arXiv and GitHub repositories for backlog generation?

Scheduling automated crawling of arXiv and GitHub repositories for backlog generation is supported by operating the discovery loop on a cadenced schedule to continuously identify high-value research leads and convert them into grounded engineering tickets.

What are the limitations of automating research-to-backlog pipelines for external repositories?

Limitations of automating research-to-backlog pipelines include the strict dependency on existing study-repo and field-borrow tools to verify capability presence and maintain honesty boundaries when decomposing external research leads into shippable tasks.