CrmClinicas/.claude-flow/CAPABILITIES.md

Claude Flow V3 - Complete Capabilities Reference

Generated: 2026-03-02T23:31:30.875Z Full documentation: https://github.com/ruvnet/claude-flow

📋 Table of Contents

1. Overview
2. Swarm Orchestration
3. Available Agents (60+)
4. CLI Commands (26 Commands, 140+ Subcommands)
5. Hooks System (27 Hooks + 12 Workers)
6. Memory & Intelligence (RuVector)
7. Hive-Mind Consensus
8. Performance Targets
9. Integration Ecosystem

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Overview

Claude Flow V3 is a domain-driven design architecture for multi-agent AI coordination with:

• 15-Agent Swarm Coordination with hierarchical and mesh topologies
• HNSW Vector Search - 150x-12,500x faster pattern retrieval
• SONA Neural Learning - Self-optimizing with <0.05ms adaptation
• Byzantine Fault Tolerance - Queen-led consensus mechanisms
• MCP Server Integration - Model Context Protocol support

Current Configuration

Setting	Value
Topology	hierarchical-mesh
Max Agents	15
Memory Backend	hybrid
HNSW Indexing	Enabled
Neural Learning	Enabled
LearningBridge	Enabled (SONA + ReasoningBank)
Knowledge Graph	Enabled (PageRank + Communities)
Agent Scopes	Enabled (project/local/user)

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Swarm Orchestration

Topologies

Topology	Description	Best For
hierarchical	Queen controls workers directly	Anti-drift, tight control
mesh	Fully connected peer network	Distributed tasks
hierarchical-mesh	V3 hybrid (recommended)	10+ agents
ring	Circular communication	Sequential workflows
star	Central coordinator	Simple coordination
adaptive	Dynamic based on load	Variable workloads

Strategies

• balanced - Even distribution across agents
• specialized - Clear roles, no overlap (anti-drift)
• adaptive - Dynamic task routing

Quick Commands

# Initialize swarm
npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized

# Check status
npx @claude-flow/cli@latest swarm status

# Monitor activity
npx @claude-flow/cli@latest swarm monitor

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Available Agents

Core Development (5)

coder, reviewer, tester, planner, researcher

V3 Specialized (4)

security-architect, security-auditor, memory-specialist, performance-engineer

Swarm Coordination (5)

hierarchical-coordinator, mesh-coordinator, adaptive-coordinator, collective-intelligence-coordinator, swarm-memory-manager

Consensus & Distributed (7)

byzantine-coordinator, raft-manager, gossip-coordinator, consensus-builder, crdt-synchronizer, quorum-manager, security-manager

Performance & Optimization (5)

perf-analyzer, performance-benchmarker, task-orchestrator, memory-coordinator, smart-agent

GitHub & Repository (9)

github-modes, pr-manager, code-review-swarm, issue-tracker, release-manager, workflow-automation, project-board-sync, repo-architect, multi-repo-swarm

SPARC Methodology (6)

sparc-coord, sparc-coder, specification, pseudocode, architecture, refinement

Specialized Development (8)

backend-dev, mobile-dev, ml-developer, cicd-engineer, api-docs, system-architect, code-analyzer, base-template-generator

Testing & Validation (2)

tdd-london-swarm, production-validator

Agent Routing by Task

Task Type	Recommended Agents	Topology
Bug Fix	researcher, coder, tester	mesh
New Feature	coordinator, architect, coder, tester, reviewer	hierarchical
Refactoring	architect, coder, reviewer	mesh
Performance	researcher, perf-engineer, coder	hierarchical
Security	security-architect, auditor, reviewer	hierarchical
Docs	researcher, api-docs	mesh

---

CLI Commands

Core Commands (12)

Command	Subcommands	Description
init	4	Project initialization
agent	8	Agent lifecycle management
swarm	6	Multi-agent coordination
memory	11	AgentDB with HNSW search
mcp	9	MCP server management
task	6	Task assignment
session	7	Session persistence
config	7	Configuration
status	3	System monitoring
workflow	6	Workflow templates
hooks	17	Self-learning hooks
hive-mind	6	Consensus coordination

Advanced Commands (14)

Command	Subcommands	Description
daemon	5	Background workers
neural	5	Pattern training
security	6	Security scanning
performance	5	Profiling & benchmarks
providers	5	AI provider config
plugins	5	Plugin management
deployment	5	Deploy management
embeddings	4	Vector embeddings
claims	4	Authorization
migrate	5	V2→V3 migration
process	4	Process management
doctor	1	Health diagnostics
completions	4	Shell completions

Example Commands

# Initialize
npx @claude-flow/cli@latest init --wizard

# Spawn agent
npx @claude-flow/cli@latest agent spawn -t coder --name my-coder

# Memory operations
npx @claude-flow/cli@latest memory store --key "pattern" --value "data" --namespace patterns
npx @claude-flow/cli@latest memory search --query "authentication"

# Diagnostics
npx @claude-flow/cli@latest doctor --fix

---

Hooks System

27 Available Hooks

Core Hooks (6)

Hook	Description
pre-edit	Context before file edits
post-edit	Record edit outcomes
pre-command	Risk assessment
post-command	Command metrics
pre-task	Task start + agent suggestions
post-task	Task completion learning

Session Hooks (4)

Hook	Description
session-start	Start/restore session
session-end	Persist state
session-restore	Restore previous
notify	Cross-agent notifications

Intelligence Hooks (5)

Hook	Description
route	Optimal agent routing
explain	Routing decisions
pretrain	Bootstrap intelligence
build-agents	Generate configs
transfer	Pattern transfer

Coverage Hooks (3)

Hook	Description
coverage-route	Coverage-based routing
coverage-suggest	Improvement suggestions
coverage-gaps	Gap analysis

12 Background Workers

Worker	Priority	Purpose
ultralearn	normal	Deep knowledge
optimize	high	Performance
consolidate	low	Memory consolidation
predict	normal	Predictive preload
audit	critical	Security
map	normal	Codebase mapping
preload	low	Resource preload
deepdive	normal	Deep analysis
document	normal	Auto-docs
refactor	normal	Suggestions
benchmark	normal	Benchmarking
testgaps	normal	Coverage gaps

---

Memory & Intelligence

RuVector Intelligence System

• SONA: Self-Optimizing Neural Architecture (<0.05ms)
• MoE: Mixture of Experts routing
• HNSW: 150x-12,500x faster search
• EWC++: Prevents catastrophic forgetting
• Flash Attention: 2.49x-7.47x speedup
• Int8 Quantization: 3.92x memory reduction

4-Step Intelligence Pipeline

1. RETRIEVE - HNSW pattern search
2. JUDGE - Success/failure verdicts
3. DISTILL - LoRA learning extraction
4. CONSOLIDATE - EWC++ preservation

Self-Learning Memory (ADR-049)

Component	Status	Description
LearningBridge	✅ Enabled	Connects insights to SONA/ReasoningBank neural pipeline
MemoryGraph	✅ Enabled	PageRank knowledge graph + community detection
AgentMemoryScope	✅ Enabled	3-scope agent memory (project/local/user)

LearningBridge - Insights trigger learning trajectories. Confidence evolves: +0.03 on access, -0.005/hour decay. Consolidation runs the JUDGE/DISTILL/CONSOLIDATE pipeline.

MemoryGraph - Builds a knowledge graph from entry references. PageRank identifies influential insights. Communities group related knowledge. Graph-aware ranking blends vector + structural scores.

AgentMemoryScope - Maps Claude Code 3-scope directories:

• project: <gitRoot>/.claude/agent-memory/<agent>/
• local: <gitRoot>/.claude/agent-memory-local/<agent>/
• user: ~/.claude/agent-memory/<agent>/

High-confidence insights (>0.8) can transfer between agents.

Memory Commands

# Store pattern
npx @claude-flow/cli@latest memory store --key "name" --value "data" --namespace patterns

# Semantic search
npx @claude-flow/cli@latest memory search --query "authentication"

# List entries
npx @claude-flow/cli@latest memory list --namespace patterns

# Initialize database
npx @claude-flow/cli@latest memory init --force

---

Hive-Mind Consensus

Queen Types

Type	Role
Strategic Queen	Long-term planning
Tactical Queen	Execution coordination
Adaptive Queen	Dynamic optimization

Worker Types (8)

researcher, coder, analyst, tester, architect, reviewer, optimizer, documenter

Consensus Mechanisms

Mechanism	Fault Tolerance	Use Case
byzantine	f < n/3 faulty	Adversarial
raft	f < n/2 failed	Leader-based
gossip	Eventually consistent	Large scale
crdt	Conflict-free	Distributed
quorum	Configurable	Flexible

Hive-Mind Commands

# Initialize
npx @claude-flow/cli@latest hive-mind init --queen-type strategic

# Status
npx @claude-flow/cli@latest hive-mind status

# Spawn workers
npx @claude-flow/cli@latest hive-mind spawn --count 5 --type worker

# Consensus
npx @claude-flow/cli@latest hive-mind consensus --propose "task"

---

Performance Targets

Metric	Target	Status
HNSW Search	150x-12,500x faster	✅ Implemented
Memory Reduction	50-75%	✅ Implemented (3.92x)
SONA Integration	Pattern learning	✅ Implemented
Flash Attention	2.49x-7.47x	🔄 In Progress
MCP Response	<100ms	✅ Achieved
CLI Startup	<500ms	✅ Achieved
SONA Adaptation	<0.05ms	🔄 In Progress
Graph Build (1k)	<200ms	✅ 2.78ms (71.9x headroom)
PageRank (1k)	<100ms	✅ 12.21ms (8.2x headroom)
Insight Recording	<5ms/each	✅ 0.12ms (41x headroom)
Consolidation	<500ms	✅ 0.26ms (1,955x headroom)
Knowledge Transfer	<100ms	✅ 1.25ms (80x headroom)

---

Integration Ecosystem

Integrated Packages

Package	Version	Purpose
agentic-flow	3.0.0-alpha.1	Core coordination + ReasoningBank + Router
agentdb	3.0.0-alpha.10	Vector database + 8 controllers
@ruvector/attention	0.1.3	Flash attention
@ruvector/sona	0.1.5	Neural learning

Optional Integrations

Package	Command
ruv-swarm	npx ruv-swarm mcp start
flow-nexus	npx flow-nexus@latest mcp start
agentic-jujutsu	npx agentic-jujutsu@latest

MCP Server Setup

# Add Claude Flow MCP
claude mcp add claude-flow -- npx -y @claude-flow/cli@latest

# Optional servers
claude mcp add ruv-swarm -- npx -y ruv-swarm mcp start
claude mcp add flow-nexus -- npx -y flow-nexus@latest mcp start

---

Quick Reference

Essential Commands

# Setup
npx @claude-flow/cli@latest init --wizard
npx @claude-flow/cli@latest daemon start
npx @claude-flow/cli@latest doctor --fix

# Swarm
npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8
npx @claude-flow/cli@latest swarm status

# Agents
npx @claude-flow/cli@latest agent spawn -t coder
npx @claude-flow/cli@latest agent list

# Memory
npx @claude-flow/cli@latest memory search --query "patterns"

# Hooks
npx @claude-flow/cli@latest hooks pre-task --description "task"
npx @claude-flow/cli@latest hooks worker dispatch --trigger optimize

File Structure

.claude-flow/
├── config.yaml      # Runtime configuration
├── CAPABILITIES.md  # This file
├── data/            # Memory storage
├── logs/            # Operation logs
├── sessions/        # Session state
├── hooks/           # Custom hooks
├── agents/          # Agent configs
└── workflows/       # Workflow templates

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Full Documentation: https://github.com/ruvnet/claude-flow Issues: https://github.com/ruvnet/claude-flow/issues