ruvnet--ruflo/v3/@claude-flow/cli/CLAUDE.md

Claude Code Configuration - Claude Flow V3

🚨 AUTOMATIC SWARM ORCHESTRATION

When starting work on complex tasks, Claude Code MUST automatically:

1. Initialize the swarm using CLI tools via Bash
2. Spawn concurrent agents using Claude Code's Task tool
3. Coordinate via hooks and memory

🚨 CRITICAL: CLI + Task Tool in SAME Message

When user says "spawn swarm" or requests complex work, Claude Code MUST in ONE message:

1. Call CLI tools via Bash to initialize coordination
2. IMMEDIATELY call Task tool to spawn REAL working agents
3. Both CLI and Task calls must be in the SAME response

CLI coordinates, Task tool agents do the actual work!

🤖 INTELLIGENT 3-TIER MODEL ROUTING (ADR-026, ADR-143)

The routing system has 3 tiers for optimal cost/performance:

Tier	Handler	Latency	Cost	Use Cases
1	Deterministic codemod	~1ms	$0	Structural transforms, no LLM: var→const, remove-console, add-logging
2	Haiku	~500ms	$0.0002	Simple tasks, bug fixes, low complexity
3	Sonnet/Opus	2-5s	$0.003-$0.015	Architecture, security, complex reasoning

Before spawning agents, get routing recommendation:

npx @claude-flow/cli@latest hooks pre-task --description "[task description]"

When you see these recommendations:

1. [CODEMOD_AVAILABLE] → call the hooks_codemod MCP tool (intent + file). It applies the transform deterministically via the TypeScript compiler at $0, no LLM.

   • Deterministic intents (Tier 1): var-to-const, remove-console, add-logging
   • add-types, add-error-handling, async-await need judgement → they route to a model (Tier 2/3), NOT a $0 codemod

2. [TASK_MODEL_RECOMMENDATION] Use model="X" → Use that model in Task tool:

Task({
  prompt: "...",
  subagent_type: "coder",
  model: "haiku"  // ← USE THE RECOMMENDED MODEL (haiku/sonnet/opus)
})

Benefits: Tier-1 codemods are $0 and ~1ms (no model call); routing keeps simple edits off Sonnet/Opus.

---

🛡️ Anti-Drift Config (PREFERRED)

Use this to prevent agent drift:

# Small teams (6-8 agents) - use hierarchical for tight control
npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized

# Large teams (10-15 agents) - use hierarchical-mesh for V3 queen + peer communication
npx @claude-flow/cli@latest swarm init --topology hierarchical-mesh --max-agents 15 --strategy specialized

Valid Topologies:

• hierarchical - Queen controls workers directly (anti-drift for small teams)
• hierarchical-mesh - V3 queen + peer communication (recommended for 10+ agents)
• mesh - Fully connected peer network
• ring - Circular communication pattern
• star - Central coordinator with spokes
• hybrid - Dynamic topology switching

Anti-Drift Guidelines:

• hierarchical: Coordinator catches divergence
• max-agents 6-8: Smaller team = less drift
• specialized: Clear roles, no overlap
• consensus: raft (leader maintains state)

---

🔄 Auto-Start Swarm Protocol (Background Execution)

When the user requests a complex task, spawn agents in background and WAIT for completion:

// STEP 1: Initialize swarm coordination (anti-drift config)
Bash("npx @claude-flow/cli@latest swarm init --topology hierarchical --max-agents 8 --strategy specialized")

// STEP 2: Spawn ALL agents IN BACKGROUND in a SINGLE message
// Use run_in_background: true so agents work concurrently
Task({
  prompt: "Research requirements, analyze codebase patterns, store findings in memory",
  subagent_type: "researcher",
  description: "Research phase",
  run_in_background: true  // ← CRITICAL: Run in background
})
Task({
  prompt: "Design architecture based on research. Document decisions.",
  subagent_type: "system-architect",
  description: "Architecture phase",
  run_in_background: true
})
Task({
  prompt: "Implement the solution following the design. Write clean code.",
  subagent_type: "coder",
  description: "Implementation phase",
  run_in_background: true
})
Task({
  prompt: "Write comprehensive tests for the implementation.",
  subagent_type: "tester",
  description: "Testing phase",
  run_in_background: true
})
Task({
  prompt: "Review code quality, security, and best practices.",
  subagent_type: "reviewer",
  description: "Review phase",
  run_in_background: true
})

// STEP 3: WAIT - Tell user agents are working, then STOP
// Say: "I've spawned 5 agents to work on this in parallel. They'll report back when done."
// DO NOT check status repeatedly. Just wait for user or agent responses.

⏸️ CRITICAL: Spawn and Wait Pattern

After spawning background agents:

1. TELL USER - "I've spawned X agents working in parallel on: [list tasks]"
2. STOP - Do not continue with more tool calls
3. WAIT - Let the background agents complete their work
4. RESPOND - When agents return results, review and synthesize

Example response after spawning:

I've launched 5 concurrent agents to work on this:
- 🔍 Researcher: Analyzing requirements and codebase
- 🏗️ Architect: Designing the implementation approach
- 💻 Coder: Implementing the solution
- 🧪 Tester: Writing tests
- 👀 Reviewer: Code review and security check

They're working in parallel. I'll synthesize their results when they complete.

🚫 DO NOT:

• Continuously check swarm status
• Poll TaskOutput repeatedly
• Add more tool calls after spawning
• Ask "should I check on the agents?"

✅ DO:

• Spawn all agents in ONE message
• Tell user what's happening
• Wait for agent results to arrive
• Synthesize results when they return

🧠 AUTO-LEARNING PROTOCOL

Before Starting Any Task

# 1. Search memory for relevant patterns from past successes
Bash("npx @claude-flow/cli@latest memory search --query '[task keywords]' --namespace patterns")

# 2. Check if similar task was done before
Bash("npx @claude-flow/cli@latest memory search --query '[task type]' --namespace tasks")

# 3. Load learned optimizations
Bash("npx @claude-flow/cli@latest hooks route --task '[task description]'")

After Completing Any Task Successfully

# 1. Store successful pattern for future reference
Bash("npx @claude-flow/cli@latest memory store --namespace patterns --key '[pattern-name]' --value '[what worked]'")

# 2. Train neural patterns on the successful approach
Bash("npx @claude-flow/cli@latest hooks post-edit --file '[main-file]' --train-neural true")

# 3. Record task completion with metrics
Bash("npx @claude-flow/cli@latest hooks post-task --task-id '[id]' --success true --store-results true")

# 4. Trigger optimization worker if performance-related
Bash("npx @claude-flow/cli@latest hooks worker dispatch --trigger optimize")

Continuous Improvement Triggers

Trigger	Worker	When to Use
After major refactor	optimize	Performance optimization
After adding features	testgaps	Find missing test coverage
After security changes	audit	Security analysis
After API changes	document	Update documentation
Every 5+ file changes	map	Update codebase map
Complex debugging	deepdive	Deep code analysis

Memory-Enhanced Development

ALWAYS check memory before:

• Starting a new feature (search for similar implementations)
• Debugging an issue (search for past solutions)
• Refactoring code (search for learned patterns)
• Performance work (search for optimization strategies)

ALWAYS store in memory after:

• Solving a tricky bug (store the solution pattern)
• Completing a feature (store the approach)
• Finding a performance fix (store the optimization)
• Discovering a security issue (store the vulnerability pattern)

📋 Agent Routing (Anti-Drift)

Code	Task	Agents
1	Bug Fix	coordinator, researcher, coder, tester
3	Feature	coordinator, architect, coder, tester, reviewer
5	Refactor	coordinator, architect, coder, reviewer
7	Performance	coordinator, perf-engineer, coder
9	Security	coordinator, security-architect, auditor
11	Docs	researcher, api-docs

Codes 1-9: hierarchical/specialized (anti-drift). Code 11: mesh/balanced

🎯 Task Complexity Detection

AUTO-INVOKE SWARM when task involves:

• Multiple files (3+)
• New feature implementation
• Refactoring across modules
• API changes with tests
• Security-related changes
• Performance optimization
• Database schema changes

SKIP SWARM for:

• Single file edits
• Simple bug fixes (1-2 lines)
• Documentation updates
• Configuration changes
• Quick questions/exploration

🚨 CRITICAL: CONCURRENT EXECUTION & FILE MANAGEMENT

ABSOLUTE RULES:

1. ALL operations MUST be concurrent/parallel in a single message
2. NEVER save working files, text/mds and tests to the root folder
3. ALWAYS organize files in appropriate subdirectories
4. USE CLAUDE CODE'S TASK TOOL for spawning agents concurrently, not just MCP

⚡ GOLDEN RULE: "1 MESSAGE = ALL RELATED OPERATIONS"

MANDATORY PATTERNS:

• TodoWrite: ALWAYS batch ALL todos in ONE call (5-10+ todos minimum)
• Task tool (Claude Code): ALWAYS spawn ALL agents in ONE message with full instructions
• File operations: ALWAYS batch ALL reads/writes/edits in ONE message
• Bash commands: ALWAYS batch ALL terminal operations in ONE message
• Memory operations: ALWAYS batch ALL memory store/retrieve in ONE message

📁 File Organization Rules

NEVER save to root folder. Use these directories:

• /src - Source code files
• /tests - Test files
• /docs - Documentation and markdown files
• /config - Configuration files
• /scripts - Utility scripts
• /examples - Example code

Project Config (Anti-Drift Defaults)

• Topology: hierarchical (prevents drift)
• Max Agents: 8 (smaller = less drift)
• Strategy: specialized (clear roles)
• Consensus: raft
• Memory: hybrid
• HNSW: Enabled
• Neural: Enabled

🚀 V3 CLI Commands (26 Commands, 140+ Subcommands)

Core Commands

Command	Subcommands	Description
init	4	Project initialization with wizard, presets, skills, hooks
agent	8	Agent lifecycle (spawn, list, status, stop, metrics, pool, health, logs)
swarm	6	Multi-agent swarm coordination and orchestration
memory	11	AgentDB memory with HNSW vector search (measured ~1.9x–4.7x vs brute force above crossover)
mcp	9	MCP server management and tool execution
task	6	Task creation, assignment, and lifecycle
session	7	Session state management and persistence
config	7	Configuration management and provider setup
status	3	System status monitoring with watch mode
workflow	6	Workflow execution and template management
hooks	17	Self-learning hooks + 12 background workers
hive-mind	6	Queen-led Byzantine fault-tolerant consensus

Advanced Commands

Command	Subcommands	Description
daemon	5	Background worker daemon (start, stop, status, trigger, enable)
neural	5	Neural pattern training (train, status, patterns, predict, optimize)
security	6	Security scanning (scan, audit, cve, threats, validate, report)
performance	5	Performance profiling (benchmark, profile, metrics, optimize, report)
providers	5	AI providers (list, add, remove, test, configure)
plugins	5	Plugin management (list, install, uninstall, enable, disable)
deployment	5	Deployment management (deploy, rollback, status, environments, release)
embeddings	4	Vector embeddings (embed, batch, search, init) — agentic-flow ONNX backend (speedup unverified, no benchmark)
claims	4	Claims-based authorization (check, grant, revoke, list)
migrate	5	V2 to V3 migration with rollback support
doctor	1	System diagnostics with health checks
completions	4	Shell completions (bash, zsh, fish, powershell)

Quick CLI Examples

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

# Start daemon with background workers
npx @claude-flow/cli@latest daemon start

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

# Initialize swarm
npx @claude-flow/cli@latest swarm init --v3-mode

# Search memory (HNSW-indexed)
npx @claude-flow/cli@latest memory search --query "authentication patterns"

# System diagnostics
npx @claude-flow/cli@latest doctor --fix

# Security scan
npx @claude-flow/cli@latest security scan --depth full

# Performance benchmark
npx @claude-flow/cli@latest performance benchmark --suite all

🚀 Available Agents (60+ Types)

Core Development

coder, reviewer, tester, planner, researcher

V3 Specialized Agents

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

🔐 @claude-flow/security

CVE remediation, input validation, path security:

• InputValidator - Zod validation
• PathValidator - Traversal prevention
• SafeExecutor - Injection protection

Swarm Coordination

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

Consensus & Distributed

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

Performance & Optimization

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

GitHub & Repository

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

SPARC Methodology

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

Specialized Development

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

Testing & Validation

tdd-london-swarm, production-validator

🪝 V3 Hooks System (27 Hooks + 12 Workers)

All Available Hooks

Hook	Description	Key Options
pre-edit	Get context before editing files	--file, --operation
post-edit	Record editing outcome for learning	--file, --success, --train-neural
pre-command	Assess risk before commands	--command, --validate-safety
post-command	Record command execution outcome	--command, --track-metrics
pre-task	Record task start, get agent suggestions	--description, --coordinate-swarm
post-task	Record task completion for learning	--task-id, --success, --store-results
session-start	Start/restore session (v2 compat)	--session-id, --auto-configure
session-end	End session and persist state	--generate-summary, --export-metrics
session-restore	Restore a previous session	--session-id, --latest
route	Route task to optimal agent	--task, --context, --top-k
route-task	(v2 compat) Alias for route	--task, --auto-swarm
explain	Explain routing decision	--topic, --detailed
pretrain	Bootstrap intelligence from repo	--model-type, --epochs
build-agents	Generate optimized agent configs	--agent-types, --focus
metrics	View learning metrics dashboard	--v3-dashboard, --format
transfer	Transfer patterns via IPFS registry	store, from-project
list	List all registered hooks	--format
intelligence	RuVector intelligence system	trajectory-*, pattern-*, stats
worker	Background worker management	list, dispatch, status, detect
progress	Check V3 implementation progress	--detailed, --format
statusline	Generate dynamic statusline	--json, --compact, --no-color
coverage-route	Route based on test coverage gaps	--task, --path
coverage-suggest	Suggest coverage improvements	--path
coverage-gaps	List coverage gaps with priorities	--format, --limit
pre-bash	(v2 compat) Alias for pre-command	Same as pre-command
post-bash	(v2 compat) Alias for post-command	Same as post-command

12 Background Workers

Worker	Priority	Description
ultralearn	normal	Deep knowledge acquisition
optimize	high	Performance optimization
consolidate	low	Memory consolidation
predict	normal	Predictive preloading
audit	critical	Security analysis
map	normal	Codebase mapping
preload	low	Resource preloading
deepdive	normal	Deep code analysis
document	normal	Auto-documentation
refactor	normal	Refactoring suggestions
benchmark	normal	Performance benchmarking
testgaps	normal	Test coverage analysis

Essential Hook Commands

# Core hooks
npx @claude-flow/cli@latest hooks pre-task --description "[task]"
npx @claude-flow/cli@latest hooks post-task --task-id "[id]" --success true
npx @claude-flow/cli@latest hooks post-edit --file "[file]" --train-neural true

# Session management
npx @claude-flow/cli@latest hooks session-start --session-id "[id]"
npx @claude-flow/cli@latest hooks session-end --export-metrics true
npx @claude-flow/cli@latest hooks session-restore --session-id "[id]"

# Intelligence routing
npx @claude-flow/cli@latest hooks route --task "[task]"
npx @claude-flow/cli@latest hooks explain --topic "[topic]"

# Neural learning
npx @claude-flow/cli@latest hooks pretrain --model-type moe --epochs 10
npx @claude-flow/cli@latest hooks build-agents --agent-types coder,tester

# Background workers
npx @claude-flow/cli@latest hooks worker list
npx @claude-flow/cli@latest hooks worker dispatch --trigger audit
npx @claude-flow/cli@latest hooks worker status

# Coverage-aware routing
npx @claude-flow/cli@latest hooks coverage-gaps --format table
npx @claude-flow/cli@latest hooks coverage-route --task "[task]"

# Statusline (for Claude Code integration)
npx @claude-flow/cli@latest hooks statusline
npx @claude-flow/cli@latest hooks statusline --json

🔄 Migration (V2 to V3)

# Check migration status
npx @claude-flow/cli@latest migrate status

# Run migration with backup
npx @claude-flow/cli@latest migrate run --backup

# Rollback if needed
npx @claude-flow/cli@latest migrate rollback

# Validate migration
npx @claude-flow/cli@latest migrate validate

🧠 Intelligence System (RuVector)

V3 includes the RuVector Intelligence System (measured numbers: see audit + scripts/benchmark-intelligence.mjs):

• SONA: Self-Optimizing Neural Architecture (measured 0.0043ms/adapt, target <0.05ms met)
• MoE: Mixture of Experts for specialized routing (gate converges — confidence 0.13→0.88 after rewards)
• HNSW: measured ~1.9x at N=20k, ~3.2x–4.7x at N=5k vs brute force (recall@10 ~0.99); ANN wins above the crossover, ruvector NAPI backend (WASM not active on test host)
• EWC++: Elastic Weight Consolidation (prevents forgetting)
• Flash Attention: unverified — no benchmark exists for this claim

The 4-step intelligence pipeline:

1. RETRIEVE - Fetch relevant patterns via HNSW
2. JUDGE - Evaluate with verdicts (success/failure)
3. DISTILL - Extract key learnings via LoRA
4. CONSOLIDATE - Prevent catastrophic forgetting via EWC++

📦 Embeddings Package (v3.0.0-alpha.12)

Features:

• sql.js: Cross-platform SQLite persistent cache (WASM, no native compilation)
• Document chunking: Configurable overlap and size
• Normalization: L2, L1, min-max, z-score
• Hyperbolic embeddings: Poincaré ball model for hierarchical data
• agentic-flow ONNX integration: speedup unverified (no benchmark; backend reported onnx, model all-MiniLM-L6-v2, 384-dim)
• Neural substrate: Integration with RuVector

🐝 Hive-Mind Consensus

Topologies

• hierarchical - Queen controls workers directly
• mesh - Fully connected peer network
• hierarchical-mesh - Hybrid (recommended)
• adaptive - Dynamic based on load

Consensus Strategies

• byzantine - BFT (tolerates f < n/3 faulty)
• raft - Leader-based (tolerates f < n/2)
• gossip - Epidemic for eventual consistency
• crdt - Conflict-free replicated data types
• quorum - Configurable quorum-based

V3 Performance Targets

Source of truth: docs/reviews/intelligence-system-audit-2026-05-29.md + scripts/benchmark-intelligence.mjs. Numbers below are measured unless marked "target/unverified".

Metric	Measured / Target	Status
HNSW Search	~1.9x at N=20k, ~3.2x–4.7x at N=5k vs brute force (recall@10 ~0.99)	Measured (ruvector NAPI; 150x-12,500x NOT reproduced)
Int8 Quantization	3.84x compression, reconstruction cosine 0.99999	Measured
RaBitQ Quantization	32x compression, 0.60ms/query	Measured
SONA Adaptation	0.0043ms/adapt (target <0.05ms met)	Measured
MoE Gate	converges (confidence 0.13→0.88)	Measured
Flash Attention	2.49x-7.47x	Unverified (no benchmark)
MCP Response	<100ms	target
CLI Startup	<500ms	target

📊 Performance Optimization Protocol

Automatic Performance Tracking

# After any significant operation, track metrics
Bash("npx @claude-flow/cli@latest hooks post-command --command '[operation]' --track-metrics true")

# Periodically run benchmarks (every major feature)
Bash("npx @claude-flow/cli@latest performance benchmark --suite all")

# Analyze bottlenecks when performance degrades
Bash("npx @claude-flow/cli@latest performance profile --target '[component]'")

Session Persistence (Cross-Conversation Learning)

# At session start - restore previous context
Bash("npx @claude-flow/cli@latest session restore --latest")

# At session end - persist learned patterns
Bash("npx @claude-flow/cli@latest hooks session-end --generate-summary true --persist-state true --export-metrics true")

Neural Pattern Training

# Train on successful code patterns
Bash("npx @claude-flow/cli@latest neural train --pattern-type coordination --epochs 10")

# Predict optimal approach for new tasks
Bash("npx @claude-flow/cli@latest neural predict --input '[task description]'")

# View learned patterns
Bash("npx @claude-flow/cli@latest neural patterns --list")

🔧 Environment Variables

# Configuration
CLAUDE_FLOW_CONFIG=./claude-flow.config.json
CLAUDE_FLOW_LOG_LEVEL=info

# Provider API Keys
ANTHROPIC_API_KEY=sk-ant-...
OPENAI_API_KEY=sk-...
GOOGLE_API_KEY=...

# MCP Server
CLAUDE_FLOW_MCP_PORT=3000
CLAUDE_FLOW_MCP_HOST=localhost
CLAUDE_FLOW_MCP_TRANSPORT=stdio

# Memory
CLAUDE_FLOW_MEMORY_BACKEND=hybrid
CLAUDE_FLOW_MEMORY_PATH=./data/memory

🔍 Doctor Health Checks

Run npx @claude-flow/cli@latest doctor to check:

• Node.js version (20+)
• npm version (9+)
• Git installation
• Config file validity
• Daemon status
• Memory database
• API keys
• MCP servers
• Disk space
• TypeScript installation

🚀 Quick Setup

# Add MCP servers (auto-detects MCP mode when stdin is piped)
claude mcp add claude-flow -- npx -y ruflo@latest mcp start
claude mcp add ruv-swarm -- npx -y ruv-swarm mcp start  # Optional
claude mcp add flow-nexus -- npx -y flow-nexus@latest mcp start  # Optional

# Start daemon
npx @claude-flow/cli@latest daemon start

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

🎯 Claude Code vs CLI Tools

Claude Code Handles ALL EXECUTION:

• Task tool: Spawn and run agents concurrently
• File operations (Read, Write, Edit, MultiEdit, Glob, Grep)
• Code generation and programming
• Bash commands and system operations
• TodoWrite and task management
• Git operations

CLI Tools Handle Coordination (via Bash):

• Swarm init: npx @claude-flow/cli@latest swarm init --topology <type>
• Swarm status: npx @claude-flow/cli@latest swarm status
• Agent spawn: npx @claude-flow/cli@latest agent spawn -t <type> --name <name>
• Memory store: npx @claude-flow/cli@latest memory store --key "mykey" --value "myvalue" --namespace patterns
• Memory search: npx @claude-flow/cli@latest memory search --query "search terms"
• Memory list: npx @claude-flow/cli@latest memory list --namespace patterns
• Memory retrieve: npx @claude-flow/cli@latest memory retrieve --key "mykey" --namespace patterns
• Hooks: npx @claude-flow/cli@latest hooks <hook-name> [options]

📝 Memory Commands Reference (IMPORTANT)

Store Data (ALL options shown)

# REQUIRED: --key and --value
# OPTIONAL: --namespace (default: "default"), --ttl, --tags
npx @claude-flow/cli@latest memory store --key "pattern-auth" --value "JWT with refresh tokens" --namespace patterns
npx @claude-flow/cli@latest memory store --key "bug-fix-123" --value "Fixed null check" --namespace solutions --tags "bugfix,auth"

Search Data (semantic vector search)

# REQUIRED: --query (full flag, not -q)
# OPTIONAL: --namespace, --limit, --threshold
npx @claude-flow/cli@latest memory search --query "authentication patterns"
npx @claude-flow/cli@latest memory search --query "error handling" --namespace patterns --limit 5

List Entries

# OPTIONAL: --namespace, --limit
npx @claude-flow/cli@latest memory list
npx @claude-flow/cli@latest memory list --namespace patterns --limit 10

Retrieve Specific Entry

# REQUIRED: --key
# OPTIONAL: --namespace (default: "default")
npx @claude-flow/cli@latest memory retrieve --key "pattern-auth"
npx @claude-flow/cli@latest memory retrieve --key "pattern-auth" --namespace patterns

Initialize Memory Database

npx @claude-flow/cli@latest memory init --force --verbose

KEY: CLI coordinates the strategy via Bash, Claude Code's Task tool executes with real agents.

📚 Full Capabilities Reference

For a comprehensive overview of all Claude Flow V3 features, agents, commands, and integrations, see:

.claude-flow/CAPABILITIES.md - Complete reference generated during init

This includes:

• All 60+ agent types with routing recommendations
• All 26 CLI commands with 140+ subcommands
• All 27 hooks + 12 background workers
• RuVector intelligence system details
• Hive-Mind consensus mechanisms
• Integration ecosystem (agentic-flow, agentdb, ruv-swarm, flow-nexus, agentic-jujutsu)
• Performance targets and status

Support

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

---

Remember: Claude Flow CLI coordinates, Claude Code Task tool creates!

important-instruction-reminders

Do what has been asked; nothing more, nothing less. NEVER create files unless they're absolutely necessary for achieving your goal. ALWAYS prefer editing an existing file to creating a new one. NEVER proactively create documentation files (*.md) or README files. Only create documentation files if explicitly requested by the User. Never save working files, text/mds and tests to the root folder.

🚨 SWARM EXECUTION RULES (CRITICAL)

1. SPAWN IN BACKGROUND: Use run_in_background: true for all agent Task calls
2. SPAWN ALL AT ONCE: Put ALL agent Task calls in ONE message for parallel execution
3. TELL USER: After spawning, list what each agent is doing (use emojis for clarity)
4. STOP AND WAIT: After spawning, STOP - do NOT add more tool calls or check status
5. NO POLLING: Never poll TaskOutput or check swarm status - trust agents to return
6. SYNTHESIZE: When agent results arrive, review ALL results before proceeding
7. NO CONFIRMATION: Don't ask "should I check?" - just wait for results

Example spawn message:

"I've launched 4 agents in background:
- 🔍 Researcher: [task]
- 💻 Coder: [task]
- 🧪 Tester: [task]
- 👀 Reviewer: [task]
Working in parallel - I'll synthesize when they complete."