Mayo 🦾🤖

The Autonomous Triple-AI Maintainer

Mayo is a Self-Improving Autonomous Maintenance Engine integrated directly into your GitHub ecosystem. It uses a Triple-AI Pipeline — three specialized AI models working in concert — to produce high-value, validated code improvements across all your repositories.

---

🧬 Triple-AI Pipeline

Every improvement goes through 3 AI models before it becomes a PR:

flowchart TD
    A["Hourly Cron Trigger"] --> R0["REVIEWER: Audit pending PR statuses"]
    R0 --> B["SCANNER: Deep codebase analysis"]
    B -->|"Text-only summary + plan"| C["EXECUTOR: Generate surgical edits"]
    C -->|"Proposed search/replace JSON"| D["REVIEWER: Validate edits"]
    D -->|"APPROVE"| E["Create PR"]
    D -->|"CORRECT"| F["Apply corrected edits then Create PR"]
    D -->|"REJECT + feedback"| C2["EXECUTOR: Retry with feedback"]
    C2 --> D2["REVIEWER: Validate retry"]
    D2 -->|"APPROVE"| E
    D2 -->|"REJECT"| SKIP["Skip, save failure to memory"]
    E --> MEM["All 3 AIs save lessons to Global Memory"]
    F --> MEM

Loading

Role	Model	Purpose
🔭 Scanner	Gemini 2.5 Flash	Reads full codebase → text-only analysis (zero compaction risk)
⚡ Executor	Llama 3.3 70B (Groq)	Receives plan → produces surgical search/replace edits
🛡️ Reviewer	Gemini 2.5 Flash	Validates edits, corrects mistakes, audits PR review history

---

🧠 Cross-Repo Global Memory

Unlike standard AI bots, Mayo has persistent memory:

• Tracks successes, failures, and "lessons learned" across all repositories.
• Insights from Repo A directly improve work on Repo B.
• The Reviewer audits real PR states (merged/closed/commented) and updates memory automatically.

---

🩺 Surgical Precision

The Executor uses a Search/Replace block system (max 10 lines per block). This guarantees:

• 100% preservation of your original code structure.
• Zero hallucination of unrelated code.
• Validated PRs — every edit is reviewed by the Reviewer before creation.

---

🏗️ Analysis Depth

The Scanner performs a rigorous multi-layered analysis:

1. Security: Injections, hardcoded secrets, missing validation
2. Logic: Edge cases, null checks, error handling
3. DX: Missing READMEs, build guides, setup docs
4. Performance: Redundant calls, memory leaks
5. Consistency: Naming, patterns, style
6. Creative: Proactive "expert touches"

---

⚙️ Setup & Deployment

⚠️ FORK BEFORE USING

This repo contains hardcoded references to my personal accounts, API keys, GitHub App configuration, and other credentials scattered throughout the codebase. Do not use this repo directly.

To use Mayo:

1. Fork this repo
2. Search and replace all personal references:
   • HOLYKEYZ → your GitHub username
   • ayandajoseph390@gmail.com → your email
   • joe-gemini-bot → your bot name
   • mayo → your bot repo name
   • All API keys/env vars → your own keys
3. Set up your own GitHub App and add secrets to your repo
4. Update the workflow file (.github/workflows/cron.yml) with your secrets

Environment Variables

Variable	Purpose
GEMINI_API_KEY	Scanner (Gemini A)
GEMINI2_API_KEY	Reviewer (Gemini B)
GROK_API_KEY	Executor (Llama 3.3 70B via Groq)
APP_ID / PRIVATE_KEY	GitHub App authentication
CRON_SECRET	Hourly trigger authorization

Deployment

1. Deploy as a GitHub App on Vercel.
2. Point webhook to https://your-app.vercel.app/webhook.
3. Install on your repositories.
4. The hourly cron (.github/workflows/cron.yml) handles the rest.

---

ℹ️ Author

Created by Joseph (@HOLYKEYZ). Advanced agentic engineering for autonomous codebase maintenance.

Happy coding! 🚀 (v3.0 — Triple-AI)