deep-context

Name: deep-context
Availability: InStock
Author: Arpit

Orchestrate Capsule context, progressive reading, dependency analysis, and specialist agents for codebase understanding.

86|9|Updated Nov 12, 2025

Search Tags:#architecture #codebase #synthesis #dependency-analysis #specialist-agents #deep-context #progressive-reading

Ready to run

npx skills add https://github.com/arpitnath/claude-capsule-kit --skill deep-context-arpitnath

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: deep-context
Source: https://github.com/arpitnath/claude-capsule-kit/tree/main/skills/deep-context
Command: npx skills add https://github.com/arpitnath/claude-capsule-kit --skill deep-context-arpitnath

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Builds deep codebase understanding using Capsule context, progressive-reader, and specialist agents instead of overwhelming main context.

Core Features & Use Cases

Capsule Context: automatic injection of Last Session summaries, Top Discoveries, Recent Files, and Team Activity (crew mode) to guide decisions.
Progressive Reader: For large files (>50KB), list structure and read only relevant chunks to reduce token usage.
Dependency Analysis: Map imports, impact, and potential dead or circular dependencies without full file reads.
Specialist Agents: Run architecture, database, and code-pattern analyses in parallel for faster, fresher insights.
Synthesis: Combine capsule, progressive, and dependency results into a coherent mental model with automatic persistence.
Execution flow and integration: described usage flow across quick and deep modes.

Quick Start

Invoke the deep-context workflow to start layered analysis of the current codebase and begin automatic context expansion.

Frequently Asked Questions about deep-context

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

FAQPage Schema

How do I understand a large unfamiliar codebase without overwhelming the main context window?▼

To analyze large files without exhausting token limits, progressive reading lists the file structure and reads only relevant chunks, while specialist agents run parallel architecture and dependency analysis to synthesize a coherent mental model.

What is the best way to map circular dependencies and dead code across an unfamiliar subsystem?▼

The best way to map circular dependencies and dead code is using dependency analysis, which evaluates imports and structural impacts across the codebase without requiring full file reads to identify potential dead or circular dependencies.

How do you perform architectural analysis on large files over 50KB?▼

Architectural analysis on large files over 50KB is performed by progressive reading, which lists the file structure and retrieves only relevant chunks to reduce token usage, enabling specialist agents to execute parallel pattern analysis.

Does deep-context codebase synthesis automatically persist architectural insights across sessions?▼

Yes, codebase synthesis automatically persists architectural insights across sessions. It combines capsule context, progressive reading, and dependency results into a coherent mental model with automatic persistence for continuous layered understanding.

Can I run database and code-pattern analyses in parallel when exploring a new codebase?▼

Yes, you can run database and code-pattern analyses in parallel. Specialist agents execute these analyses concurrently to deliver faster, fresher insights while building deep codebase understanding and mapping imports.

When do I need staged context buildup for software architecture tasks?▼

Staged context buildup is needed when you require architectural clarity for large codebases or unfamiliar subsystems. It uses capsule context to inject summaries and recent files, orchestrating progressive reading to guide decisions.