Aeo Optimization

> Turn Claude Code into a self-reviewing, test-enforced engineering system that remembers context across sessions — then route work across 13 models from a single dashboard.

Claude Bootstrap is an installable config pack (skills, hooks, rules, templates) for Claude Code. Maggy is the optional local server that adds multi-model routing, a web dashboard, intent-driven protocols, and plugin orchestration. Both live in this repo. Start with Bootstrap; add Maggy when you need the harness.

1100+ tests. 73 skills. 15 MCP tools. Used daily across production codebases.

Who This Is For

• Solo engineers using Claude Code who want TDD enforcement, quality gates, and memory that survives context compaction — without changing their workflow
• Teams routing work across Claude, DeepSeek, Kimi, Gemini, and Codex from a single dashboard with cost-aware model selection
• Platform engineers building AI-assisted developer tooling who need a reference implementation with intent tracking, protocol execution, and plugin architecture

Choose Your Path

Bootstrap — 30-second install

git clone https://github.com/alinaqi/maggy.git
cd maggy && ./install.sh

Your next Claude Code session picks it up automatically.

Full Harness — zero-config

pipx install maggy-harness   # or: pip install maggy-harness
maggy bootstrap              # installs skills, hooks, ~/bin model wrappers, plugins
maggy serve                  # auto-configures from your local repos,
                             # then opens the dashboard at localhost:8080

(or from source: cd maggy && ./install.sh && maggy serve)

No API keys required to start — Maggy runs in local mode and, on first launch, discovers your local git repos and opens the dashboard pointed at them. Add GITHUB_TOKEN / ANTHROPIC_API_KEY later only if you want GitHub sync or API-model features. See GETTING_STARTED.md for details.

What It Looks Like in Practice

Routing a task:

You: "review the auth middleware for timing attacks"
→ Blast score: 8/10 (security + architecture)
→ Routed to: Claude (Tier 11)
→ ADR gate: found docs/adr/0003-jwt-strategy.md → injected as context
→ Review runs with full architectural context

Skill Protocol execution:

You: "push to git"
→ Intent matched: git-push protocol
→ ✅ lint       (2.1s)
→ ✅ typecheck   (4.3s)
→ ✅ tests       (11.2s)
→ ✅ stage
→ ✅ commit      [AI-generated: "fix: resolve token refresh race condition"]
→ ✅ push

Fatigue-aware memory:

Session fatigue: 0.61 (PRE-SLEEP)
→ Mnemos: auto-checkpoint written
→ Micro-consolidation: 3 ResultNodes compressed
→ iCPG context injected: 2 ReasonNodes, 1 constraint
→ Context freed: ~18k tokens

The Problem This Solves

You're using Claude Code. It's impressive — but:

• It picks the most expensive model for everything, including trivial tasks
• Context fills up, state is lost, you re-explain yourself every session
• There's no enforcement: code quality, test coverage, and ADR compliance only happen if you remember to ask
• Running multiple agents on the same repo causes file conflicts
• You have no visibility into what Claude is actually doing inside your codebase

What Bootstrap Gives You

What Maggy Adds

Model Routing

Every message is scored 1–10 for complexity and classified by task type. The cheapest capable model wins.

Routing is semantic (Qwen3 as local classifier), fatigue-aware, budget-capped, and cascading.

Gateway routing with srooter — www.srooter.ai

We've added first-class support for srooter, an Anthropic/OpenAI-compatible LLM gateway that routes your requests across models (Claude, MiniMax, DeepSeek, Kimi, Gemini, Grok, local Qwen) transparently — intent-based routing, budget caps, fallbacks, and a usage dashboard, without changing your tools.