context-cascade
🥈SilverContext Cascade - Nested Plugin Architecture for Claude Code Official Claude Code Plugin | Version 3.1.0 | Last updated: 2026-01-09 (see docs/COMPONENT-COUNTS.json for source counts) Context-saving nested architecture: Playbooks -> Skills -> Agents -> Commands. Load only what you need, saving 90%+ context space.
3240Updated 2mo ago
Intermediate30min to implementautomation
Saves ~180 min per use
Quick InstallView Source
git clone https://github.com/DNYoussef/context-cascade.gitWorks with:
Claude
Overview
About This Skill
Context Cascade is an official Claude Code plugin that implements a hierarchical architecture—Playbooks → Skills → Agents → Commands—to dramatically reduce token consumption. Instead of loading all 176 skills and 260 agents upfront (consuming 100k+ tokens), the system loads only what's needed, starting with 30 playbooks (~2k tokens) and expanding on demand. An intelligent intent-analyzer and orchestration-router automatically select the right skills based on your request, routing to specialized stacks for delivery, operations, research, security, or domain-specific work. Built on Claude Flow for enterprise-grade agent orchestration with memory, hooks, and swarm intelligence, Context Cascade maintains full capability while reducing context overhead—ideal for developers building complex multi-agent workflows in Claude Code or Cursor.
How to Use
[{"step":"Define your top-level Playbook structure in a JSON file, specifying the project name, description, and required skills.","tip":"Use the COMPONENT-COUNTS.json from the official plugin as a reference for component sizing and dependencies."},{"step":"For each Skill, create a nested JSON object that includes the skill name, description, and the Agents it requires.","tip":"Focus on isolating skills that can operate independently to maximize context savings."},{"step":"Within each Agent, list the specific Commands needed for that agent's operation, excluding any legacy or unused functionality.","tip":"Use the Claude Code plugin's context analysis tools to identify unused commands in your current implementation."},{"step":"Run the Context Cascade generator to produce the final JSON architecture file and memory optimization metrics.","tip":"Validate the output using the plugin's built-in context analyzer to confirm savings before deployment."},{"step":"Implement the architecture in your Claude Code environment and monitor the actual memory usage during execution.","tip":"Compare pre- and post-implementation memory metrics to validate the 90%+ context savings claim."}]
Use Cases
Reducing context token usage in long-running AI agent workflows
Automating feature development with parallel agent teams (researcher, coder, reviewer, tester, documenter)
Deploying production-ready applications through orchestrated security and performance gates
Conducting structured multi-phase research with automated quality validation
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/DNYoussef/context-cascadeRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Design a nested Context Cascade architecture for [PROJECT_NAME] using the following hierarchy: Playbook: [PLAYBOOK_NAME] -> Skill: [SKILL_NAME] -> Agent: [AGENT_NAME] -> Commands: [COMMAND_LIST]. Load only the required components and document the context savings achieved. Output a JSON file with the structure and a summary of memory optimization metrics.
Example Output
```json
{
"playbook": {
"name": "Automated Customer Onboarding Playbook",
"description": "Handles new customer onboarding with minimal manual intervention",
"skills": [
{
"name": "Data Validation Skill",
"description": "Validates customer data against CRM and billing systems",
"agents": [
{
"name": "Email Verification Agent",
"commands": [
"check_email_format",
"verify_domain_existence",
"send_verification_link"
],
"context_saved": "85% by excluding legacy validation rules"
}
],
"context_saved": "72% reduction in skill-level context"
}
],
"total_context_saved": "94% compared to monolithic approach"
},
"memory_optimization": {
"before": "12.4MB total context",
"after": "0.7MB total context",
"savings": "94.35% reduction in memory usage"
}
}
```
The Context Cascade architecture for the Automated Customer Onboarding Playbook demonstrates significant memory optimization by implementing a nested plugin structure. The playbook contains a single skill focused on data validation, which in turn utilizes an email verification agent with three specific commands. By loading only the required components at each level of the hierarchy, the system achieves a 94.35% reduction in memory usage compared to a traditional monolithic approach. The email verification agent, for instance, excludes legacy validation rules that aren't relevant to the current onboarding workflow, further reducing its context footprint by 85%. This hierarchical approach ensures that each component operates with minimal context while maintaining full functionality, allowing the system to handle complex workflows without overwhelming the available memory resources.Apply to these tools
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