breadth-reader

Automate exploration, review, and summarization of large data sources with fork-context parallel tasks.

81|9|Updated Nov 10, 2025

Search Tags:#subagents #review #exploration #summarize #parallelism #fork-context #breadth

Ready to run

npx skills add https://github.com/barkain/claude-code-workflow-orchestration --skill breadth-reader

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: breadth-reader
Source: https://github.com/barkain/claude-code-workflow-orchestration/tree/main/skills/breadth-reader
Command: npx skills add https://github.com/barkain/claude-code-workflow-orchestration --skill breadth-reader

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Breadth Reader automates the process of exploring, reviewing, and summarizing large data sources, reducing manual surveying time and cognitive load.

Core Features & Use Cases

Automated breadth exploration: Scan large codebases, docs, and datasets to surface structure, hotspots, and key content.
Concise, structured summaries: Produce a high-level synopsis that enables quick decision-making.
Use Case: When onboarding to a new project, run breadth-reader to generate a ready-to-read overview of files, directories, and documentation.

Quick Start

Use the breadth-reader to explore the contents of a large project, e.g., run breadth-reader on '~/projects/my-project/' to obtain a high-level summary.

Frequently Asked Questions about breadth-reader

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

FAQPage Schema

How do I summarize a large codebase to get a high-level overview quickly?▼

To summarize a large codebase quickly, you need an automated tool that scans directories and surfaces structural hotspots. This skill automates that process by spawning parallel Explore subagents to review files and produce a concise, structured synopsis for fast onboarding.

What is the best way to explore and summarize large documentation sets at scale?▼

Exploring and summarizing large documentation sets at scale requires parallel breadth task execution. This skill utilizes a fork-context environment to spawn subagents that concurrently read and review documentation, generating a high-level summary that reduces manual surveying time.

Do I need a specific environment to run parallel breadth tasks for data exploration?▼

Running parallel breadth tasks for data exploration requires a fork-context environment. The execution setup must also provide the Read, Glob, Grep, Bash, and Task toolset to enable the Explore subagents to perform concurrent analysis on your datasets.

Can I use subagents to review datasets and surface key content hotspots?▼

Yes, you can use subagents to review datasets and surface key content hotspots. The skill spawns Explore subagents that apply parallel analysis across your data sources, identifying structural patterns and delivering a high-level synopsis for quick decision-making.

How does fork-context parallelism work for scanning large data sources?▼

Fork-context parallelism for scanning large data sources works by spawning multiple Explore subagents simultaneously. Each subagent handles a slice of the review and exploration process, which aggregates into a structured summary without overwhelming manual cognitive load.