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. 67 skills. 15 MCP tools. Used daily across production codebases.

• 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

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

Your next Claude Code session picks it up automatically.

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.

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

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

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.

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.

Recommended with Maggy, Claude Code, or Codex. Point any of them at the gateway and your traffic is routed for you — no per-tool config:

# Claude Code (or Codex) → srooter
export ANTHROPIC_BASE_URL="https://www.srooter.ai/anthropic"   # or your local gateway
export ANTHROPIC_API_KEY="<your-srooter-key>"
claude        # now routed through srooter

Pick the model you "follow" once with /model-config — Maggy, the route-task hooks, and srooter all honor the same choice. Trivial asks stay on the cheap/local tier; real coding goes to your primary model (e.g. MiniMax-M2.5).

Standard TDD tells you if your code passes tests. Telos tells you if your code fulfills its intent.

IFS (Intent Fidelity Scale) = F1 × F2 × F3

F1 — Conformance:  passed / total tests            (pytest / vitest)
F2 — Validation:   drift severity                  (Cortex drift_events)
F3 — Integrity:    IF-3 orphan symbols              (no reason edges)
                   IF-4 empty contracts             (no pre/post/invariants)
                   IF-6 stale reasons               (proposed >7d, never fulfilled)
                   IF-7 scope sprawl                (reason scopes >10 files)

A zero in any plane collapses IFS to zero. 100% test pass rate with severe architectural drift = score of 0. This is intentional. See the Telos RFC.

.claude/
  skills/       # 67 skills — Python, TS, React, security, mobile, databases
  hooks/        # TDD enforcement, quality gates, Mnemos lifecycle
  rules/        # Conditional rules by file glob
  templates/    # settings.json, CLAUDE.md, ADR template, PR template

maggy/
  maggy/
    pipeline/   # Unified ChatPipeline orchestrator
    skills/     # Skill injection + YAML protocol engine
    api/        # REST API (chat, routing, plugins, pipeline logs)
    static/     # Web dashboard (vanilla JS, no build step)
    services/   # Routing, memory, execution, Mnemos

cortex-mcp/     # Code intelligence MCP server
  src/cortex/
    structure/  # AST extraction, edge types, complexity
    storage/    # SQLite graph store, FTS5 index

plugins/        # Drop-in plugins (build-in-public, telos, providers)

cd maggy && python3 -m pytest tests/ -x -q        # 900+ tests
cd cortex-mcp && python3 -m pytest tests/ -q       # 207 tests

• Skill Protocols — YAML intent-driven workflows. "Push to git" runs lint → test → commit → push automatically
• Unified Pipeline — single ChatPipeline orchestrator with real-time streaming, fallback, per-request logging
• Telos — intent-grounded testing with IFS scoring on every project open
• Cortex MCP — modular edge extraction (Python AST, TypeScript, Git co-change). Elixir support
• 13-Tier Routing — AGY, Gemini CLI, and Grok added to the routing ladder

See CHANGELOG.md for full history.

Skill PRs welcome. All skills run through the linter before merge:

PYTHONPATH=scripts python3 -m skill_lint --fail-on error skills/your-skill/

See CONTRIBUTING.md for the quality gate checklist.

MIT — See LICENSE

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