Context Cascade
Context 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.
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Context 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.
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.
π§ 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 β Auto-Skill Selection
Every request flows through this intelligent routing system:
User Request
β
π intent-analyzer (Auto-triggered on ambiguous/complex requests)
ββ Analyzes explicit and implicit goals
ββ Detects constraints and context
ββ Maps to probabilistic intent (>80% confidence = proceed)
ββ Socratic clarification if needed (<80% confidence)
β
π― orchestration-router (Auto-triggered for orchestration needs)
ββ Keyword extraction (agent count, complexity, patterns)
ββ Decision tree routing (Priority 1-4 logic)
ββ Skill selection with rationale
ββ Automatic skill invocation
β
β‘ Selected Skill Executes (e.g., parallel-swarm-implementation)
ββ Spawns specialized agents in parallel
ββ Coordinates via memory namespaces
ββ Theater detection via Byzantine consensus
ββ Produces validated output
Example Flow:
User: "Build user authentication with JWT tokens"
π intent-analyzer: High confidence (95%) - Feature implementation
π― orchestration-router: Routes to parallel-swarm-implementation (Loop 2)
β‘ 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
β
Result: Production-ready auth system in 2 hours
The Playbook Decision Tree
After intent analysis and routing, execution follows this workflow:
flowchart TD
A[π Phase 0: Intent Analyzer] --> B[π Phase 1: Prompt Architect]
B --> C{π― Workstream Signal}
C -->|Feature/Build| D[π Delivery Stack]
C -->|Infrastructure/Release| E[βοΈ Operations Stack]
C -->|Research/Discovery| F[π¬ Research Stack]
C -->|Security/Compliance| G[π Security Stack]
C -->|Specialist Domain| H[π¨ Specialist Stack]
D --> I[β
Quality Gate]
E --> I
F --> I
G --> I
H --> I
I --> J{π Ready to Close?}
J -->|No| C
J -->|Yes| K[π Finalize & Report]
Stack Auto-Selection:
- Delivery Stack β
feature-dev-complete(end-to-end feature shipping) - Operations Stack β
production-readiness(deployment gates, security, performance) - Research Stack β
deep-research-orchestrator(3-phase research SOP with quality gates) - Security Stack β
network-security-setup(lock down environments, layer security SOPs) - Specialist Stack β Language/platform specialists (Python, TypeScript, React, etc.)
Key Principles:
- Zero Decision Paralysis: Router auto-selects optimal skill from 171 options
- Context-Aware: Detects intent from keywords, agent count, complexity signals
- Transparent: Provides selection rationale and alternatives
- Adaptive: Learns from corrections and adjusts future routing
π What's New in v3.0.0
- β
Current component counts: 30 playbooks, 196 skills (176 core + 20 supplementary), 260 agents, and 249 commands (see
docs/COMPONENT-COUNTS.json). - β
Discovery indexes:
discovery/SKILL-INDEX.md,discovery/AGENT-REGISTRY.md, anddiscovery/COMMAND-INDEX.mdmap routing across the hierarchy. - β
Modular marketplace: Five installable plugin packages defined in
.claude-plugin/marketplace.json. - β
Graphviz library: 41 workflow diagrams live in
docs/workflows/graphviz/with an index and README. - β
MCP templates:
.mcp.jsondocuments 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)
Option A - Core Only (Recommended for beginners):
/plugin install 12fa-core
Option B - Full Stack (Everything):
/plugin install 12fa-core 12fa-three-loop 12fa-security 12fa-visual-docs 12fa-swarm
3. Setup MCP Servers
Required (Claude Flow - our foundation):
npm install -g claude-flow@alpha
claude mcp add claude-flow npx claude-flow@alpha mcp start
What This Gives You:
- πΎ Memory System - Persistent coordination across agents
- π Hooks - Lifecycle events (pre-task, post-task, pre-edit, post-edit, session-end)
- π Swarm Intelligence - Multi-agent coordination (2.8-4.4x baseline speedup)
- π AgentDB - 96x-164x performance boost with semantic search
- π― ReasoningBank - Pattern learning with 46% faster performance
Recommended (for advanced features):
npm install -g ruv-swarm flow-nexus@latest
claude mcp add ruv-swarm npx ruv-swarm mcp start
claude mcp add flow-nexus npx flow-nexus@latest mcp start
Production MCP Systems (code quality & persistent memory):
- Install the projects:
# Clone to your preferred location (e.g., ~/Projects or D:/Projects)
git clone https://github.com/DNYoussef/connascence.git
git clone https://github.com/DNYoussef/memory-mcp-triple-system.git
# Install dependencies
cd connascence && pip install -e . && cd ..
cd memory-mcp-triple-system && pip install -e . && cd ..
- Configure MCP servers (add to
claude_desktop_config.json):
{
"mcpServers": {
"connascence-analyzer": {
"command": "python",
"args": ["mcp_local/stdio_server.py"],
"cwd": "/path/to/connascence"
},
"memory-mcp": {
"command": "python",
"args": ["-m", "src.mcp.stdio_server"],
"cwd": "/path/to/memory-mcp-triple-system"
}
}
}
- Optional environment variables (for custom paths):
# Set these if projects are not in standard locations
export CONNASCENCE_PATH=/your/path/to/connascence
export MEMORY_MCP_PATH=/your/path/to/memory-mcp-triple-system
The cognitive architecture will auto-discover project paths from your MCP config.
Done! π You can now use /sparc, /audit-pipeline, /quick-check, and all other commands.
Learn More: See CLAUDE-FLOW-INTEGRATION.md for how we enhance Claude Flow.
π Integration with Production MCP Systems
Connascence Safety Analyzer - https://github.com/DNYoussef/connascence-safety-analyzer
- 7+ violation types (God Objects, Parameter Bombs, Complexity, Deep Nesting, etc.)
- NASA compliance checking (Power of 10 Rules)
- 0.018s analysis performance
- Access: 14 code quality agents only (coder, reviewer, tester, etc.)
- MCP Integration: docs/MCP-INTEGRATION.md
Memory MCP Triple System - https://github.com/DNYoussef/memory-mcp-triple-system
- Triple-layer retention (24h/7d/30d+)
- Mode-aware context adaptation (Execution/Planning/Brainstorming)
- Automatic tagging protocol (WHO/WHEN/PROJECT/WHY)
- 384-dimensional vector embeddings with HNSW indexing
- Access: ALL agents (global)
- MCP Integration: docs/MCP-INTEGRATION.md
Agent Access Control: See hooks/12fa/agent-mcp-access-control.js for agent-specific MCP permissions
Tagging Protocol: See hooks/12fa/memory-mcp-tagging-protocol.js for automatic metadata injection
MCP Marketplace & Ecosystem: See docs/MCP-MARKETPLACE-GUIDE.md for:
- FREE SERVERS ONLY - No payment, API keys, or accounts required
- Complete catalog of 11 free MCP servers (4 local + 7 official Anthropic)
- Agent-to-MCP-server mapping for all 90 agents
- Installation guide for all free servers
- Usage examples and best practices
Cognitive Architecture: VeriLingua x VERIX x GlobalMOO
Context Cascade includes a sophisticated cognitive architecture for improved AI reasoning:
VeriLingua: 7 Cognitive Frames
VeriLingua forces explicit cognitive distinctions drawn from natural languages:
| Frame | Source Language | Forces Distinction |
|---|---|---|
| Evidential | Turkish (-mis/-di) | How do you know? (witnessed/reported/inferred/assumed) |
| Aspectual | Russian (perfective/imperfective) | Complete or ongoing? |
| Morphological | Arabic (trilateral roots) | What are the semantic components? |
| Compositional | German (compounding) | Build from primitives |
| Honorific | Japanese (keigo) | Who is the audience? |
| Classifier | Chinese (measure words) | What type/category? |
| Spatial | Guugu Yimithirr (absolute direction) | Absolute position in codebase |
VERIX: Epistemic Notation
Every claim encodes its epistemic status:
[illocution|affect] content [ground:source] [conf:0.XX] [state:status]
Example: [assert|positive] Use async/await here [ground:performance_testing] [conf:0.92] [state:confirmed]
Four-Loop Self-Improvement Architecture
| Loop | Trigger | Duration | Purpose |
|---|---|---|---|
| Loop 1: Execution | Every request | Seconds-minutes | 5-phase workflow |
| Loop 1.5: Reflect | Per session | Minutes | Extract learnings from corrections/approvals |
| Loop 2: Quality | Per session | Minutes-hours | Theater detection, audits |
| Loop 3: Meta | Every 3 days | Hours | Self-optimization via GlobalMOO |
Loop 1.5: Session Reflection (NEW)
The /reflect skill implements per-session micro-learning:
Session End
|
v
Signal Detection (corrections, approvals, explicit rules)
|
v
Confidence Classification (HIGH/MEDIUM/LOW)
|
v
Skill File Updates (LEARNED PATTERNS section)
|
v
Memory MCP Storage (for Loop 3 aggregation)
Commands:
/reflect- Manual reflection on current session/reflect-on- Enable automatic reflection on session end/reflect-off- Disable automatic reflection/reflect-status- Check current state and history
Signal Types:
| Signal | Confidence | Action |
|---|---|---|
| Corrections ("No, use X instead") | HIGH (0.90) | Requires approval |
| Explicit Rules ("Always/Never") | HIGH (0.90) | Requires approval |
| Approvals ("Perfect", "Exactly") | MEDIUM (0.75) | Auto-apply if enabled |
| Observations (patterns) | LOW (0.55) | Auto-apply if enabled |
Value: Correct once, never again. Session learnings persist in skill files.
GlobalMOO Integration
Multi-objective optimization for cognitive architecture tuning:
- Pattern: GlobalMOO (5D exploration) -> PyMOO NSGA-II (14D refinement)
- Objectives: Accuracy, Efficiency, Reliability, User Satisfaction
- Output: Named modes (audit, speed, research, robust, balanced)
| Mode | Accuracy | Efficiency | Use Case |
|---|---|---|---|
| audit | 0.960 | 0.763 | Code review, security |
| speed | 0.734 | 0.950 | Quick tasks |
| research | 0.980 | 0.824 | Deep research |
| balanced | 0.882 | 0.928 | General purpose |
Full Documentation: docs/COGNITIVE-ARCHITECTURE.md
Cognitive Architecture Integration (NEW in v3.1.0)
The cognitive architecture now includes FrozenHarness - an immutable evaluation system integrated with the 7-Analyzer Suite from Connascence Safety Analyzer.
FrozenHarness Evaluation System
Located at cognitive-architecture/loopctl/core.py, FrozenHarness provides:
from loopctl.core import FrozenHarness
harness = FrozenHarness(loop_dir=".", use_cli_evaluator=True, use_connascence=True)
metrics = harness.grade(artifact_path)
# Returns comprehensive metrics:
# - task_accuracy, token_efficiency, edge_robustness, epistemic_consistency
# - connascence: sigma_level, dpmo, nasa_compliance, mece_score, theater_risk
# - overall score with quality gate enforcement
Information Flow Architecture
User Request -> Loop 1 (Execution) -> FrozenHarness.grade()
|
+---------------------+-+--------------------+
| | |
CLI Evaluator ConnascenceBridge TelemetryBridge
| | |
{metrics} {quality_gate} Memory MCP Storage
| | |
+---------------------+----------------------+
|
eval_report.json
|
Loop 1.5 (Reflect) -> Memory MCP -> Loop 3 (Meta-Opt)
Smoke Test Results (2026-01-09)
| Component | Status | Mode | Key Metrics |
|---|---|---|---|
| FrozenHarness | PASS | cli_evaluator | overall: 0.79 |
| ConnascenceBridge | PASS | cli (mock fallback) | sigma: 4.0, dpmo: 3132 |
| TelemetryBridge | PASS | file-based | 17 items stored |
| Library Catalog | PASS | json | 25 components |
| Meta-Loop Runner | PASS | script ready | 9 functions |
| Reflect-to-Memory | PASS | script ready | 6 functions |
| Memory MCP Storage | PASS | mcp-fallback | 17+ items |
| Scheduled Task | PASS | Windows Task Scheduler | Ready |
Key Files
| Component | Location | Purpose |
|---|---|---|
| FrozenHarness | cognitive-architecture/loopctl/core.py | Immutable evaluation harness |
| ConnascenceBridge | cognitive-architecture/integration/connascence_bridge.py | 7-Analyzer quality metrics |
| TelemetryBridge | cognitive-architecture/integration/telemetry_bridge.py | Memory MCP storage |
| Information Flow | cognitive-architecture/docs/INFORMATION-FLOW-DIAGRAM.md | Complete flow documentation |
Full Documentation: cognitive-architecture/INTEGRATION-COMPLETE.md
Complete Command Taxonomy
Total Commands: 231 (tracked in docs/COMPONENT-COUNTS.json)
Organization: Commands live under commands/ and are indexed in discovery/COMMAND-INDEX.md.
GraphViz Diagrams: 41 workflow visualizations in docs/workflows/graphviz/
Command Distribution
| Category | Commands | Documentation |
|---|---|---|
| delivery | 67 | commands/delivery/ |
| foundry | 13 | commands/foundry/ |
| operations | 74 | commands/operations/ |
| orchestration | 32 | commands/orchestration/ |
| platforms | 4 | commands/platforms/ |
| quality | 16 | commands/quality/ |
| research | 9 | commands/research/ |
| security | 11 | commands/security/ |
| tooling | 7 | commands/tooling/ (includes /reflect commands) |
| root (quality loop + templates) | 2 | commands/quality-loop.md, commands/VERIX-COMMAND-TEMPLATE.md |
Complete Reference: MASTER-COMMAND-INDEX.md Visual Workflows: GraphViz Diagrams (41 diagrams)
π¬ Specialized Capability Areas
Reverse Engineering & Binary Analysis
3 Production-Ready RE Skills covering all 5 levels of reverse engineering:
1. reverse-engineering-quick - RE Levels 1-2 (β€2 hours)
- String Reconnaissance (Level 1, β€30 min) - Extract URLs, IPs, IOCs, file paths, crypto indicators
- Static Analysis (Level 2, 1-2 hrs) - Disassemble with Ghidra/radare2, map control flow, decompile to C
- Decision Gate: Automatically evaluates if Level 2 is needed after Level 1
- Use cases: Malware triage, IOC extraction, initial binary analysis, threat intelligence
2. reverse-engineering-deep - RE Levels 3-4 (4-8 hours)
- Dynamic Analysis (Level 3, 2-4 hrs) - GDB debugging, syscall tracing, runtime behavior analysis
- Symbolic Execution (Level 4, 2-4 hrs) - Angr path exploration, constraint solving, vulnerability discovery
- Use cases: Advanced malware analysis, vulnerability research, exploit development, CTF challenges
3. reverse-engineering-firmware - RE Level 5 (2-8 hours)
- Firmware Extraction - binwalk, unsquashfs, jefferson for embedded filesystems
- IoT Security Analysis - Router firmware, embedded systems, hardcoded credentials
- Emulation - QEMU + firmadyne for dynamic firmware analysis
- Use cases: IoT security audits, router vulnerability research, embedded systems reverse engineering
Security Features:
- β οΈ Comprehensive security warnings for VM/Docker/E2B sandboxing
- π‘οΈ Malware analysis best practices documented
- π Required isolation for all binary execution
- π Tool syntax corrections (binwalk, unsquashfs, jefferson)
Quality Improvements: reverse-engineering-firmware improved from 6.5/10 β 9.2/10 (+2.7 points)
Deep Research SOP - Academic ML Research Pipeline
9 Production-Ready Skills implementing comprehensive research methodology with quality gates:
Research Foundations (Pipelines A-B):
- baseline-replication - Pipeline B: Replicate published baselines with Β±1% tolerance, statistical validation
- literature-synthesis - Pipeline A: Systematic literature review, gap analysis, research positioning
Method Development (Pipeline C): 3. method-development - Pipeline C: Novel algorithm design, ablation studies, statistical rigor
Evaluation & Validation (Pipeline D): 4. holistic-evaluation - Pipeline D: Multi-metric evaluation (performance, efficiency, robustness, interpretability)
Production & Deployment (Pipeline E): 5. deployment-readiness - Pipeline E: Production deployment, monitoring, A/B testing, rollback strategies
Orchestration & Integration (Pipeline F): 6. deep-research-orchestrator - Pipeline F: Full research workflow orchestrator, agent coordination
Reproducibility & Archival (Pipelines G-H): 7. reproducibility-audit - Pipeline G: ACM Artifact Evaluation compliance, Docker validation, Zenodo archival 8. research-publication - Pipeline H: Paper writing, conference submission, peer review response
Quality Gates (GO/NO-GO Decision Framework): 9. gate-validation - Quality Gates 1-3: Validate phase transitions with comprehensive requirement checklists
Statistical Rigor Enhancements:
- β Bonferroni multiple comparison correction
- β Cohen's d effect size calculation
- β Statistical power analysis (1-Ξ² β₯ 0.8 requirement)
- β Paired t-tests with significance thresholds
Quality Improvements:
- gate-validation: 7.2/10 β 9.0/10 (+1.8 points)
- reproducibility-audit: 7.8/10 β 9.5/10 (+1.7 points)
- Average: 8.5/10 β 9.4/10 (+0.9 points)
ACM Compliance: Supports ACM Artifact Evaluation badges (Available, Functional, Reproduced, Reusable)
Use Cases:
- Academic ML research (NeurIPS, ICML, CVPR submissions)
- Systematic experimentation with reproducibility
- Baseline replication and comparative studies
- Production ML deployment with rigorous validation
Rapid Research Pipeline (NEW - December 2025)
4 NEW Skills for rapid research with academic integrity:
1. rapid-idea-generator - Generate research ideas in <5 minutes
- 5-Whys causal analysis
- MECE component decomposition
- Root cause identification
- 5-10 ranked ideas with literature pointers
2. research-gap-visualizer - Visual gap maps from literature
- Gap matrices (methods vs domains)
- Research landscape diagrams
- Opportunity quadrants
- Prioritized gap lists with evidence
3. visual-asset-generator - Auto diagrams, charts, tables
- PRISMA flow diagrams
- Methodology flowcharts
- Comparison tables
- Model architecture diagrams
- NEVER fabricates data (uses [YOUR_DATA] placeholders)
4. rapid-manuscript-drafter - Quick manuscript scaffolding
- IMRaD structure (Introduction, Methods, Results, Discussion)
- Section templates with writing tips
- Placeholder insertion for your data
- Completion checklists
Key Design Principles:
- NEVER fabricate data or results
- All outputs use clear [YOUR_DATA] placeholders
- Full transparency in reasoning
- Integrates with quality gates for academic rigor
- Ethical by design
Time: 30-45 minutes (vs 2-4 weeks for Deep Research SOP) Use When: Quick first drafts, grant proposals, thesis outlines
Available Plugins
1. 12fa-core - Core System β START HERE
Essential tools for production-grade AI development
What you get:
- β SPARC 5-phase methodology - Systematic development workflow
- β 10 Core Skills - agent-creator, sparc-methodology, functionality-audit, theater-detection-audit, production-readiness, audit-pipeline, code-review-assistant, quick-quality-check, smart-bug-fix, prompt-architect
- β 12 Core Agents - coder, reviewer, tester, planner, researcher, sparc-coord, sparc-coder, specification, pseudocode, architecture, refinement, production-validator
- β
11 Commands -
/sparc,/audit-pipeline,/quick-check,/fix-bug,/review-pr, and more - β 5 Hooks - pre-task, post-task, pre-edit, post-edit, session-end
- β Quality Gates - Theater detection, functionality audit, production validation
Install: /plugin install 12fa-core
Metrics: 2.5-4x speedup | <3% failure rate | 100% 12-FA compliance
Use when: You want systematic development with TDD, quality gates, and proven methodology
2. 12fa-three-loop - Three-Loop Architecture
Advanced research β implementation β recovery workflow
What you get:
- β Loop 1: Research-Driven Planning - 5x pre-mortem validation, >97% planning accuracy
- β Loop 2: Parallel Swarm Implementation - 6.75x speedup, theater detection
- β Loop 3: CI/CD Intelligent Recovery - 100% recovery rate, automated debugging
- β 6 Advanced Skills - research-driven-planning, parallel-swarm-implementation, cicd-intelligent-recovery, multi-model, cascade-orchestrator, feature-dev-complete
- β 6 Specialized Agents - task-orchestrator, migration-planner, cicd-engineer, performance-benchmarker, gemini-search-agent, codex-auto-agent
- β
6 Commands -
/development,/build-feature,/gemini-search,/codex-auto,/sparc:integration,/sparc:devops
Install: /plugin install 12fa-three-loop (requires 12fa-core)
Metrics: >97% planning accuracy | 6.75x parallel speedup | 100% recovery rate
Use when: You need research-driven planning, parallel implementation, and automatic failure recovery
3. 12fa-security - Security Hardening
Enterprise-grade security infrastructure
What you get:
- β
6 Production Security Components:
- Agent Spec Generator CLI - Standardized agent specifications
- Policy DSL Engine - Declarative YAML-based constraints
- Guardrail Enforcement - Real-time validation (93.5% secrets detection, 0% false positives, <5ms overhead)
- Agent Registry API - Service discovery with OpenAPI 3.1 spec
- Secrets Management - Vault integration with automated rotation
- OpenTelemetry Collector - Distributed tracing and metrics
- β 2 Security Skills - network-security-setup, sandbox-configurator
- β 2 Agents - security-manager, security-manager-enhanced
- β
2 Commands -
/sparc:security-review,/setup
Install: /plugin install 12fa-security (requires 12fa-core, Vault, Prometheus, Grafana)
Metrics: 100% security score | 0 vulnerabilities | 100% compliance rate
Use when: You need enterprise security, compliance requirements, or production deployments
4. 12fa-visual-docs - Visual Documentation
Graphviz workflow diagrams
What you get:
- β
41 Graphviz diagrams in
docs/workflows/graphviz/plus anindex.htmlbrowser viewer - β Validation Tools - Cross-platform validation scripts (Bash + PowerShell)
- β Templates - Skill, agent, and command process DOT templates
- β Directory README - Describes structure and usage for the diagram set
Install: /plugin install 12fa-visual-docs (requires 12fa-core, Graphviz)
Metrics: 41 DOT sources tracked by docs/workflows/graphviz/validate-all-diagrams.*
Use when: You want visual documentation, AI-comprehensible workflows, or training materials
Based on: fsck.com article - "Claude seems better at understanding and following rules written as dot"
5. 12fa-swarm - Advanced Swarm Coordination
Multi-agent swarm systems with Byzantine consensus
What you get:
- β 4 Topologies - Hierarchical (6.3x), Mesh (8.3x), Adaptive (7.2x), Ring (4.5x)
- β 3 Consensus Protocols - Byzantine (fault-tolerant), Raft (strong consistency), Gossip (scalable)
- β Hive Mind Coordination - Queen-led with workers, scouts, memory manager
- β 15 Swarm Agents - hierarchical-coordinator, mesh-coordinator, byzantine-coordinator, queen-coordinator, and more
- β 7 Swarm Skills - swarm-advanced, hive-mind-advanced, github-multi-repo, github-project-management, and more
- β
8 Commands -
/swarm-init,/agent-spawn,/task-orchestrate,/github-swarm, and more
Install: /plugin install 12fa-swarm (requires 12fa-core, claude-flow, ruv-swarm MCP)
Metrics: 8.3x max speedup | 4 topologies | Byzantine fault tolerance | 100+ agents supported
Use when: You need parallel execution, fault tolerance, or complex multi-agent coordination
π― Use Cases: Auto-Triggered Workflows
For Individual Developers
Start with: 12fa-core
Just describe what you want - the system handles the rest:
# β Old way: Manual skill selection
"Use parallel-swarm-implementation skill to build REST API"
# β
New way: Natural language β automatic routing
"Build a REST API for user management"
# What happens automatically:
# 1. intent-analyzer detects feature implementation intent
# 2. orchestration-router selects parallel-swarm-implementation
# 3. 6 agents spawn in parallel (researcher, coder, reviewer, tester, documenter, theater-detector)
# 4. Result: Production-ready API in 2 hours
More examples:
# Auto-triggers quick-quality-check skill
"Quick validation before I commit"
# Auto-triggers smart-bug-fix skill
"Authentication randomly fails for OAuth users"
# Auto-triggers functionality-audit skill
"Does this code actually work?"
For Teams
Recommended: 12fa-core + 12fa-three-loop + 12fa-security
Context-aware team workflows:
# Auto-triggers research-driven-planning (Loop 1) β parallel-swarm-implementation (Loop 2)
"Add payment processing with Stripe"
# What happens:
# Loop 1: Research best practices, 5x pre-mortem, validated plan
# Loop 2: 8 agents parallel (researcher, backend, frontend, security, tester, reviewer, documenter, ops)
# Loop 3: CI/CD with auto-fix if tests fail
# Result: Payment feature deployed in 4 hours
# Auto-triggers code-review-assistant skill
"Review PR #123"
# Auto-triggers production-readiness skill
"Security review before deployment"
For Enterprises
Full Stack: All 5 plugins
Large-scale orchestration with automatic swarm coordination:
# Auto-triggers deep-research-orchestrator for complex architecture decisions
"Migrate monolith to microservices architecture"
# What happens:
# Phase 0: Intent analysis - Architecture migration detected
# Phase 1: Prompt blueprint with constraints and success criteria
# Phase 2: Hierarchical swarm coordination (1 queen + 15 workers)
# Phase 3: Agent assignment (architecture-specialist, backend-dev, ops-engineer, etc.)
# Phase 4-6: Execution β Quality gates β Deployment
# Result: Migration plan + implementation roadmap in 1 week
# Auto-triggers hive-mind-advanced with queen-led coordination
"Complex distributed system with 20+ microservices"
# Auto-triggers network-security-setup + security stack
"Lock down production environment"
π Repository Snapshot
| Item | Current State (repository data) |
|---|---|
| Component counts | 30 playbooks, 196 skills (176 core + 20 supplementary), 260 agents, 249 commands (docs/COMPONENT-COUNTS.json) |
| Plugin bundles | 5 marketplace packages in .claude-plugin/marketplace.json |
| Graphviz library | 41 DOT diagrams in docs/workflows/graphviz/ |
| Discovery indexes | Skill/agent/command indexes under discovery/ |
| MCP template | Sample servers listed in .mcp.json |
ποΈ System Architecture
Core Methodology: SPARC
Specification β Pseudocode β Architecture β Refinement β Code
β β β β β
Requirements Algorithm System TDD Integration
Analysis Design Design Implementation Testing
Four-Loop Self-Improvement Architecture
βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β Loop 1: Execution (Per-Request) β
β - 5-phase workflow (IntentβPromptβPlanβRouteβExec) β
β - Multi-agent task execution β
β - Output: Completed task with quality gates β
βββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β
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β Loop 1.5: Session Reflection (NEW) β
β - Signal detection (corrections, approvals, rules) β
β - Confidence classification (HIGH/MEDIUM/LOW) β
β - Skill file updates (LEARNED PATTERNS section) β
β - Output: Persistent learnings in skill files β
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β
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β Loop 2: Quality Validation (Per-Session) β
β - Theater detection via Byzantine consensus β
β - Functionality audits β
β - Security and performance validation β
β - Output: Reality-validated implementation β
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β
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β Loop 3: Meta-Optimization (Every 3 Days) β
β - Aggregate learnings from Loop 1.5 β
β - GlobalMOO 5D + PyMOO NSGA-II 14D optimization β
β - Cascade updates to skills, agents, commands β
β - Output: System-wide cognitive tuning β
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Swarm Topologies (optional plugin)
Hierarchical (6.3x) Mesh (8.3x) Adaptive (7.2x) Ring (4.5x)
Queen Agent1 ββ Agent2 Auto-selects Agent1
/ | \ β β β Best Topology β
W1 W2 W3 Agent3 ββ Agent4 Agent2
β
Agent3
π§ Requirements
Minimum (for 12fa-core)
- Claude Code β₯ 2.0.13
- Node.js β₯ 18.0.0
- npm β₯ 9.0.0
- Git
Required MCP Server
- claude-flow@alpha - Core coordination (required for all plugins)
Recommended MCP Servers
- ruv-swarm - Enhanced swarm coordination (required for 12fa-swarm)
- flow-nexus - Cloud features and neural training (optional)
- connascence-analyzer - Code quality analysis (optional, 14 code quality agents)
- memory-mcp - Persistent cross-session memory (optional, all agents)
Optional Tools (by plugin)
- Graphviz β₯ 2.44.0 - For 12fa-visual-docs rendering
- HashiCorp Vault β₯ 1.15.0 - For 12fa-security secrets management
- Prometheus β₯ 2.45.0 - For 12fa-security telemetry
- Grafana β₯ 10.0.0 - For 12fa-security dashboards
- Docker - For containerized deployments (optional)
π Documentation
Main Documentation
- This README - Quick start and plugin overview
- CLAUDE-FLOW-INTEGRATION.md - How we integrate with and enhance Claude Flow β
- Marketplace README - Detailed installation guide
- MIGRATION.md - Migration guide from v2.x to v3.0
- CHANGELOG.md - Complete version history
Plugin Documentation
- 12fa-core README - Core system guide
- 12fa-three-loop README - Three-Loop Architecture
- 12fa-security README - Security components
- 12fa-visual-docs README - Visual documentation
- 12fa-swarm README - Swarm coordination
Phase Reports
- Week 1: Quick Wins - Initial 12-FA deployment
- Week 2: Integrations - Integration layer
- Week 3: Security - Security hardening
- Phase 1: Graphviz - Custom diagrams
- Phase 2: Graphviz - Template diagrams
- Phase 3: Graphviz - Validation infrastructure
API Documentation
- Agent Registry OpenAPI - REST API spec
- Policy DSL Reference - Policy language guide
π€ Support & Credits
Support
- Issues: GitHub Issues
- Discussions: GitHub Discussions
- Plugin Marketplace: Claude Code Plugins
Built On
- Claude Flow by @ruvnet - Enterprise-grade agent orchestration platform
- Provides: Memory system, hooks, swarm intelligence, MCP protocol, AgentDB, ReasoningBank
- We enhance with: Graphviz diagrams (
docs/workflows/graphviz/), evidence-based prompting, SPARC methodology, Three-Loop Architecture, security hardening, 12-Factor compliance - See CLAUDE-FLOW-INTEGRATION.md for full integration details
π Recognition
- 100% 12-Factor Compliance - Perfect score across all 12 factors
- 0 Security Vulnerabilities - 100% security audit pass
- Production Certified - Ready for enterprise deployment
- Official Claude Code Plugin - October 2025 marketplace launch
π License
MIT - See LICENSE
π Quick Example: Natural Language β Auto-Execution
No manual skill selection. Just describe what you want.
# 1. Add marketplace
/plugin marketplace add DNYoussef/ruv-sparc-three-loop-system
# 2. Install core
/plugin install 12fa-core
# 3. Natural language request - automatic routing
"Build a REST API for user management with JWT authentication"
# π intent-analyzer: Detects feature implementation (95% confidence)
# π― orchestration-router: Routes to parallel-swarm-implementation
# β‘ AUTO-EXECUTES:
# - Specification: Requirements analysis
# - Pseudocode: Algorithm design
# - Architecture: System structure
# - Refinement: TDD implementation (6 agents in parallel)
# - Code: Integration and comprehensive tests
# β
Result: Production-ready API
# 4. Quick validation (auto-triggers quick-quality-check)
"Quick check before I commit"
# AUTO-RUNS in parallel:
# - Theater detection (Byzantine consensus)
# - Linting (ESLint/Prettier)
# - Security scan (OWASP Top 10)
# - Basic tests (unit + integration)
# 5. PR review (auto-triggers code-review-assistant)
"Review PR #123"
# 5-agent swarm AUTO-REVIEWS:
# - Security: Vulnerability scan
# - Performance: Bottleneck detection
# - Style: Code quality audit
# - Tests: Coverage analysis
# - Docs: Documentation completeness
# 6. Deploy check (auto-triggers production-readiness)
"Ready to deploy to production?"
# AUTO-VALIDATES:
# - All tests pass (100%)
# - No security vulnerabilities
# - Production-ready checklist complete
# - Performance benchmarks met
Key Insight: You never manually select skills. The system detects intent and auto-routes to optimal workflows.
Version: 3.0.0 | Author: DNYoussef | Last Updated: 2026-01-03
β If this helped you, please star the repository!
