CLAUDEMAX

A persistent cognitive operating system for Claude Code.

CLAUDEMAX transforms Claude Code from a stateless terminal assistant into a context-aware, self-routing, self-healing execution environment. Every prompt is classified, enriched, and executed through a defined pipeline. Memory accumulates across sessions via NotebookLM and LightRAG. Safety guards run on every write. The system operates without user intervention.

Architecture

CLAUDEMAX is composed of seven layers:

┌─────────────────────────────────────────────────────────────┐
│  LAYER 1 — Cognitive Router (UserPromptSubmit)              │
│  Classifies prompt against 25 task types. Computes          │
│  complexity score. Selects model tier. Emits routing        │
│  directives: EXECUTE / SPAWN / THINK / AUTOCHAIN.           │
│  Planning Gate enforces 5-step structured thinking.         │
├─────────────────────────────────────────────────────────────┤
│  LAYER 2 — Safety Guards (PreToolUse)                       │
│  PII redactor: blocks API keys, tokens, wallet addresses.   │
│  Code quality gate: rejects hardcoded secrets, empty catch. │
│  Runs on every Write / Edit / Bash invocation.              │
├─────────────────────────────────────────────────────────────┤
│  LAYER 3 — Event Accumulator (PostToolUse)                  │
│  Writes structured tool events to turn-events.jsonl.        │
│  Tool-specific failure detection (replaces blind regex).    │
│  Forwards to daemon for long-term session memory.           │
├─────────────────────────────────────────────────────────────┤
│  LAYER 4 — Completion Feedback (Stop)                       │
│  Reads accumulated events. Renders DONE diagram.            │
│  Writes structured session summary to daemon.               │
├─────────────────────────────────────────────────────────────┤
│  LAYER 5 — Session Context (SessionStart)                   │
│  Reads project memory from daemon + NLM notebook.           │
│  Session intent prediction. Injects NLM-synthesized         │
│  briefing. Starts Ruflo swarm engine. Status bar.           │
├─────────────────────────────────────────────────────────────┤
│  LAYER 6 — NotebookLM + LightRAG (Core Memory)             │
│  Per-project NLM notebooks auto-created on first session.   │
│  LightRAG semantic search (sentence-transformers,           │
│  all-MiniLM-L6-v2, 384-dim dense embeddings).               │
│  NLM deep recall fallback when LightRAG returns weak.       │
│  Cross-project knowledge graph. NLM auth auto-refresh       │
│  via Chrome CDP.                                            │
├─────────────────────────────────────────────────────────────┤
│  LAYER 7 — Anti-Laziness & Token Optimization               │
│  NLM generates aggressive, task-specific directives.        │
│  CLAUDE.md per-task segments (16 types, ~500 tokens).       │
│  Master progress accumulator (infinite memory via NLM).     │
│  10-step precompute pipeline on session end.                │
└─────────────────────────────────────────────────────────────┘

Core Components

• Ripple Autopilot — always-on router, prompt enrichment, model selection
• NotebookLM CLI — core memory layer, per-project notebooks, deep recall fallback
• LightRAG — semantic search with sentence-transformers (384-dim dense embeddings)
• Planning Gate — 5-step structured thinking on every prompt
• THINK directive — deep reasoning for complex tasks (>=50% complexity)
• AUTOCHAIN — full autopilot task execution without user intervention
• Anti-laziness enforcement — NLM-generated, aggressive, per-task-type directives
• Session intent prediction — predicts what the user will need before they ask
• Self-healing — tool-specific failure detection, 3-retry with learned strategies
• Status bar — model, context%, weekly limit%, real cost vs API cost

Key Features (v0.7.0)

Memory

• Per-project NLM notebooks auto-created on first session
• Master progress file accumulates decisions/patterns/failures across sessions
• Cross-project knowledge graph (scans gstack + Claude memory)
• NLM auth auto-refresh via Chrome CDP (no silent failures)
• Content-based vector dedup (eliminated 48% duplicates)
• 500-doc index cap with oldest-first pruning
• Type-aware memory pruning (50 sessions, 30 prompts, 10 decisions)

Token Optimization

• CLAUDE.md per-task segments (16 types, ~500 tokens vs ~6,000 full)
• Session briefing synthesized by NLM (87% token reduction)
• Learnings synthesized into 5 rules (96% token reduction)
• Prompt deduplication (stops echoing user prompt)
• Average tokens/prompt: ~478 (down from ~1,200-2,750)

Infrastructure

• 10-step precompute pipeline (background, on session end)
• Tool-specific failure detection (replaces blind regex)
• Shell injection fix (execSync to execFileSync with stdin)
• Session intent prediction
• Status bar with live metrics

Task Taxonomy

The cognitive router classifies prompts against 25 task types. See CLAUDE.md for the full taxonomy.

Entrepreneur: brain-dump, write-content, brainstorm, decide, research, strategy, pitch, fundraise, hire

Engineering: bug-fix, new-feature, deploy-ship, design, security, refactor, performance, investigate, planning, code-review, autoplan

Complexity scoring adjusts dynamically: repeat task types get +15%, large projects get +5%.

Install

curl -fsSL https://raw.githubusercontent.com/Blockchainpreneur/CLAUDEMAX/main/install.sh | bash

Or clone and run locally:

git clone https://github.com/Blockchainpreneur/CLAUDEMAX ~/claudemax
cd ~/claudemax && bash install.sh

Hook Pipeline

Event             File                          Function
─────────────────────────────────────────────────────────────────────
PreToolUse        pii-redactor.mjs              Block secrets on Write/Edit/Bash
PreToolUse        code-quality-gate.mjs         Block hardcoded creds, warn on any/empty-catch
UserPromptSubmit  rational-router-apex.mjs      Classify → route → Planning Gate → directives
PostToolUse       post-tool-use-apex.mjs        Accumulate tool events, failure detection
Stop              task-complete.mjs             DONE diagram + structured session summary
Stop              session-stop.mjs              Post session end to memory daemon
SessionStart      session-start.mjs             Welcome panel + status bar
SessionStart      session-start-daemon.mjs      Inject NLM-synthesized project context
SessionStart      ruflo daemon                  Start swarm engine (60+ agents)

All hooks exit 0 unconditionally. Claude never waits on them.

gstack — AI Software Factory (28 Skills)

Sprint workflow: /office-hours → /plan-ceo-review → /plan-eng-review → /plan-design-review → /design-consultation → /review → /investigate → /design-review → /qa → /qa-only → /cso → /ship → /land-and-deploy → /canary → /benchmark → /document-release → /retro

Power tools: /browse, /autoplan, /codex, /careful, /freeze, /unfreeze, /guard, /setup-deploy, /gstack-upgrade

Non-negotiable: never ship without /review + /qa + /cso. After deploy: /canary then /retro.

Memory System

Session memory stored at ~/.claudemax/contexts/{project-slug}.md. NotebookLM notebooks at ~/.claudemax/nlm/{project-slug}/. LightRAG index at ~/.claudemax/lightrag/.

The 10-step precompute pipeline runs on session end:

1. Accumulate tool events → 2. Synthesize session summary → 3. Update NLM notebook → 4. Rebuild LightRAG index → 5. Deduplicate vectors → 6. Prune by type limits → 7. Generate anti-laziness directives → 8. Compress learnings → 9. Build per-task CLAUDE.md segments → 10. Update cross-project knowledge graph

MCP Servers

11 servers available. Use CLI tools first; MCP only when no CLI equivalent exists.

• context7 — live framework/library docs
• shadcn — UI component registry
• supabase — database, auth, storage
• github — PRs, issues, releases (prefer gh CLI)
• sentry — error monitoring
• figma — design file reading
• n8n — workflow automation
• magicuidesign — Magic UI components
• playwright — browser automation (prefer CLI)
• chrome-devtools — Chrome DevTools Protocol
• sequential-thinking — structured reasoning

Requirements

• macOS or Linux
• Node.js >= 18
• Claude Code CLI — npm install -g @anthropic-ai/claude-code
• Bun — curl -fsSL https://bun.sh/install | bash
• Python 3.10+ (for sentence-transformers / LightRAG)

License

MIT