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Claude Code Configuration - Claude Flow V3

MEMORY FIRST (Reinforced)

Before ANY task: search memory. After ANY success: store pattern. Non-negotiable.

// Search before working
mcp__claude-flow__memory_search({query: "[task keywords]", namespace: "patterns", limit: 10})
// Store after success
mcp__claude-flow__memory_store({namespace: "patterns", key: "[name]", value: "[what worked]"})

ONLY MCP tools: mcp__claude-flow__memory_store/search/list/retrieve/stats NEVER claude-flow memory * CLI.

Auto-Learning Protocol

Before Starting Any Task

mcp__claude-flow__memory_search({query: "[task keywords]", namespace: "patterns", limit: 10})
mcp__claude-flow__memory_search({query: "[task type]", namespace: "tasks", limit: 5})
Bash("claude-flow hooks route --task '[task description]'")

After Completing Any Task

mcp__claude-flow__memory_store({namespace: "patterns", key: "[pattern-name]", value: "[what worked]"})
Bash("claude-flow hooks post-edit --file '[main-file]' --train-neural true")
Bash("claude-flow hooks post-task --task-id '[id]' --success true --store-results true")

Memory-Enhanced Development

Check memory before: new features, debugging, refactoring, performance work Store after: bug fixes, completions, performance fixes, security discoveries

Improvement Workers

Trigger Worker
Major refactor optimize
New features testgaps
Security changes audit
API changes document
5+ file changes map
Complex debug deepdive

Intelligent Skill Selection

Before starting any task, select the optimal orchestration approach. The decision tree below routes tasks to the right skill based on scope, complexity, and domain.

Quick Decision Tree

Don't know which skill? --> /route [describe your task]  (unified dispatcher)

Single file, quick fix? --> Direct Edit (no skill needed)
Game dev (Godot/Unity/Unreal)? --> /game-dev
VR/AR (Meta Quest, WebXR, hand tracking, passthrough)? --> /meta-xr-sdk
Bug/feature/review (single agent)? --> lazy-fetch blueprints
Multi-file feature with TDD? --> /build-with-quality
Large codebase structural analysis (call graphs, arch, diff)? --> codebase-memory
Need current library docs while coding? --> context7
Swarm (3+ agents)? --> /swarm-advanced or hive-mind
GitHub ops (PR/release/CI)? --> github-* skills
Docs/reports? --> /report-builder, /docs-alignment
Media (image/video/3D)? --> /imagemagick, /ffmpeg, /blender, /comfyui
Browser automation? --> /playwright, /browser-automation, /qe-browser (QE-grade: typed assertions, visual-diff, injection scan)
AI/ML (PyTorch, CUDA, notebooks)? --> /pytorch-ml, /cuda, /jupyter-notebooks
Memory/AgentDB? --> /agentdb-*, /lazy-fetch
Wardley maps / strategic analysis? --> /wardley-maps, /report-builder
UI/UX design? --> /ui-ux-pro-max-skill, /bencium-*, /design-audit, /typography
Architecture review? --> /vanity-engineering-review, /renaissance-architecture, /human-architect-mindset
Research + NotebookLM? --> /notebooklm, /perplexity-research, /gemini-url-context
Deep cited research? --> /deep-research (parallel agents, provenance, verification)
Optimize a metric iteratively? --> /autoresearch (experiment loop, keep/discard)
Add source verification? --> /provenance-tracking (.provenance.md sidecar)
Security / compliance? --> /defense-security
AEC (building architecture)? --> /studio [task]
SEO / content optimisation? --> /toprank

Full routing with all 92 active skills: see multi-agent-docker/skills/SKILL-DIRECTORY.md

Skill Capabilities Matrix

Skill Agents Scope Memory Best For
lazy-fetch 1 (self) Single session RuVector bridge Context discovery, plan tracking, blueprints, security scan
game-dev 48 Game project Session state files Game design, engine-specific code, team orchestration
ruflo/claude-flow 1-15 Multi-agent RuVector native Swarm coordination, complex features, cross-module work
build-with-quality 111+ QE pipeline RuVector native Testing, quality gates, coverage analysis
sparc-methodology 5-8 Full lifecycle RuVector native Specification through completion

Combining Skills

Skills compose. A single task may use multiple skills in sequence:

  1. lazy gather "combat system" -- discover relevant files first
  2. /game-dev team-combat "melee attacks" -- orchestrate the game dev team
  3. lazy check -- validate the implementation
  4. lazy remember "combat" "melee uses hitbox detection, not raycasts" -- persist

For ruflo swarms, each spawned agent uses worktree isolation:

  • Agent spawns in worktree (wt-add <name> for Ruflo, isolation: "worktree" for Agent tool)
  • Runs lazy init + lazy gather for its task scope
  • Implements using lazy-fetch plan tracking
  • Results merge back via ruflo coordination or git merge

For massive parallelisable work (migrations, bulk refactors), use /batch — it fans out to N worktree agents automatically.

Session Start Protocol

  1. Search RuVector memory for task context
  2. Run lazy read if working in a lazy-fetch-initialised project
  3. Check hooks route for skill recommendation
  4. Select skill from decision tree above
  5. Begin work

Swarm Orchestration

3-Tier Model Routing (ADR-026)

Tier Handler Latency Cost Use Cases
1 Agent Booster <1ms $0 Simple transforms -- Skip LLM
2 Haiku ~500ms $0.0002 Simple tasks, low complexity
3 Sonnet/Opus 2-5s $0.003-0.015 Complex reasoning, security

[AGENT_BOOSTER_AVAILABLE] -> Edit tool directly [TASK_MODEL_RECOMMENDATION] -> use recommended model in Task tool

Agent Routing

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

Task Complexity

Swarm: 3+ files, new features, cross-module, API+tests, security, performance, DB schema Skip: single file, 1-2 line fix, docs, config, questions

Project Config

Setting Value
Topology hierarchical-mesh
Max Agents 15
Strategy specialized
Consensus raft
Memory hybrid
HNSW Enabled
Neural Enabled

Tool Delineation

Task tool: ALL execution (agents, files, code, git) CLI (Bash): claude-flow swarm init, claude-flow hooks *, claude-flow agent spawn MCP (MANDATORY): mcp__claude-flow__memory_{store,search,list,retrieve}

RuVector PostgreSQL

See multi-agent-docker/CLAUDE.md for full RuVector connection details, schema, SQL examples, and extension capabilities. Summary:

  • Host: ruvector-postgres:5432 | DB: ruvector | Connection: $RUVECTOR_PG_CONNINFO
  • ALWAYS use MCP tools (mcp__claude-flow__memory_*), never CLI
  • Tables: memory_entries (1.17M+), patterns, reasoning_patterns, session_state

Hive-Mind

Topologies: hierarchical, mesh, hierarchical-mesh (recommended), adaptive Strategies: byzantine (f<n/3), raft (f<n/2), gossip, crdt, quorum

Session Persistence

claude-flow session restore --latest                                              # Start
claude-flow hooks session-end --generate-summary true --persist-state true        # End

Codebase Memory MCP (ACTIVE — USE FIRST)

codebase-memory-mcp is indexed for this project (home-devuser-workspace-project, 48,159 nodes, 95,766 edges). Use these tools BEFORE Grep/Glob/Read for all structural queries.

Query Type Tool Instead of
Who calls function X? trace_path(function_name, direction="callers") Grep
Module/route architecture get_architecture(project, aspects=[...]) Manual file reading
Find struct/fn by name search_graph(project, query) Glob
Git diff impact + risk score detect_changes(project) Manual inspection
Fetch function source get_code_snippet(qualified_name) Read
Check if index is current index_status(project) —
Re-index after large changes index_repository(repo_path) —

Project ID: home-devuser-workspace-project Session start: run index_status — if needs_reindex: true, run index_repository. Token savings: ~99.2% vs grep (3,400 vs 412,000 tokens for 5 structural queries).