context-cascade

Context Cascade - Nested Plugin Architecture for Claude Code

Official Claude Code Plugin | Version 1.0.0 | Last updated: 2026-02-08 (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.

The Context Cascade Architecture

PLAYBOOKS (30)     <-- Only these are loaded initially (~2k tokens)
    |
    v
SKILLS (176)       <-- Loaded when playbook invokes them
    |
    v
AGENTS (260)       <-- Loaded when skill needs them
    |
    v
COMMANDS (249)    <-- Embedded in agents, loaded last

Why Context Cascade?

• Traditional approach: Load everything upfront = 100k+ tokens consumed
• Context Cascade: Load on demand = ~2k tokens initially, expand as needed
• Result: 90%+ context savings while maintaining full capability

Built on Claude Flow - Enterprise-grade agent orchestration with memory, hooks, and swarm intelligence.

🧠 How It Works: Intelligent Auto-Routing

The system automatically selects the right skills and agents based on your intent. No manual selection required.

Phase 0: Intent Analysis → Auto-Skill Selection

Every request flows through this intelligent routing system:

User Request
    ↓
🔍 intent-analyzer (Auto-triggered on ambiguous/complex requests)
    ├─ Analyzes explicit and implicit goals
    ├─ Detects constraints and context
    ├─ Maps to probabilistic intent (>80% confidence = proceed)
    └─ Socratic clarification if needed (<80% confidence)
    ↓
🎯 orchestration-router (Auto-triggered for orchestration needs)
    ├─ Keyword extraction (agent count, complexity, patterns)
    ├─ Decision tree routing (Priority 1-4 logic)
    ├─ Skill selection with rationale
    └─ Automatic skill invocation
    ↓
⚡ Selected Skill Executes (e.g., parallel-swarm-implementation)
    ├─ Spawns specialized agents in parallel
    ├─ Coordinates via memory namespaces
    ├─ Theater detection via Byzantine consensus
    └─ Produces validated output

Example Flow:

User: "Build user authentication with JWT tokens"

🔍 intent-analyzer: High confidence (95%) - Feature implementation
🎯 orchestration-router: Routes to parallel-swarm-implementation (Loop 2)
⚡ Loop 2 spawns 6 agents in parallel:
   - researcher: Auth best practices
   - coder: JWT implementation
   - reviewer: Security audit
   - tester: Comprehensive tests
   - documenter: API docs
   - theater-detector: Byzantine validation
✅ Result: Production-ready auth system in 2 hours

The Playbook Decision Tree

After intent analysis and routing, execution follows this workflow:

flowchart TD
    A[🔍 Phase 0: Intent Analyzer] --> B[📋 Phase 1: Prompt Architect]
    B --> C{🎯 Workstream Signal}
    C -->|Feature/Build| D[🚀 Delivery Stack]
    C -->|Infrastructure/Release| E[⚙️ Operations Stack]
    C -->|Research/Discovery| F[🔬 Research Stack]
    C -->|Security/Compliance| G[🔒 Security Stack]
    C -->|Specialist Domain| H[🎨 Specialist Stack]
    D --> I[✅ Quality Gate]
    E --> I
    F --> I
    G --> I
    H --> I
    I --> J{📊 Ready to Close?}
    J -->|No| C
    J -->|Yes| K[🎉 Finalize & Report]

Stack Auto-Selection:

• Delivery Stack → feature-dev-complete (end-to-end feature shipping)
• Operations Stack → production-readiness (deployment gates, security, performance)
• Research Stack → deep-research-orchestrator (3-phase research SOP with quality gates)
• Security Stack → network-security-setup (lock down environments, layer security SOPs)
• Specialist Stack → Language/platform specialists (Python, TypeScript, React, etc.)

Key Principles:

1. Zero Decision Paralysis: Router auto-selects optimal skill from 171 options
2. Context-Aware: Detects intent from keywords, agent count, complexity signals
3. Transparent: Provides selection rationale and alternatives
4. Adaptive: Learns from corrections and adjusts future routing

See complete playbook

🎉 What's New in v3.0.0

• ✅ Current component counts: 30 playbooks, 196 skills (176 core + 20 supplementary), 260 agents, and 249 commands (see docs/COMPONENT-COUNTS.json).
• ✅ Discovery indexes: discovery/SKILL-INDEX.md, discovery/AGENT-REGISTRY.md, and discovery/COMMAND-INDEX.md map routing across the hierarchy.
• ✅ Modular marketplace: Five installable plugin packages defined in .claude-plugin/marketplace.json.
• ✅ Graphviz library: 41 workflow diagrams live in docs/workflows/graphviz/ with an index and README.
• ✅ MCP templates: .mcp.json documents sample servers (e.g., memory-mcp, connascence-analyzer, fetch, sequential-thinking, filesystem, playwright, ruv-swarm).

Quick Start (3 Steps)

1. Add Marketplace

/plugin marketplace add DNYoussef/context-cascade

2. Install Plugin(s)