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Hash AI Coding Persona

How one person ships serious software with AI agents — without the repo turning into a landfill of half-finished code and leaked secrets.

MIT license 5 habits 2 templates works with any agent including local Ollama signed commits


AI can write code faster than one person can review it. That is exactly the problem. Speed without a system produces impressive demos and unmaintainable repos: secrets in history, final_v3 files everywhere, commits nobody can read, and features that were never actually run. This is the system that keeps a solo builder's output serious — every change small, signed, verified, and traceable — while still moving at agent speed.

It is not a framework and not a tool. It is five habits, each written down so an agent can follow them as strictly as a human would. It's just Markdown, so it works with any coding agent and any model — Claude Code, Cursor, Cline, Continue, Aider, or a local model you run with Ollama. No vendor, no lock-in.

Contents

Quickstart

Two copy-paste files do most of the work — no install, no dependency:

# 1. Give your agent a commit contract it follows every time (keep it git-ignored)
curl -o COMMIT_RULES.local.md https://raw.githubusercontent.com/Hash-7777/Hash-AI-Coding-persona/main/templates/COMMIT_RULES.template.md
echo "*.local.md" >> .gitignore

# 2. Start a persistent memory your agent reads each session
mkdir -p memory && curl -o memory/MEMORY.md https://raw.githubusercontent.com/Hash-7777/Hash-AI-Coding-persona/main/templates/MEMORY.template.md

Then tell your agent, once: "Follow COMMIT_RULES.local.md for every commit, and read memory/MEMORY.md at the start of each session." This works with any agent — including a local model via Ollama (feed the two files as context). Fill in the <PROJECT> / <DEVELOPER> placeholders, and read the five habits to get the rest. A live example of both files is in examples/; per-agent recipes are in docs/integrations.md.

The five habits

# Habit The one line
01 Memory that persists A file-based memory the agent reads every session, so context survives across days and chats.
02 Work-trees for parallel focus One checkout per line of work, so a broken experiment never blocks a shipping fix.
03 Commit discipline Explicit staging, readable outcome-focused messages, signed commits, only real files.
04 Verify before you trust Drive the actual change end-to-end and watch it work. Tests passing is not the feature working.
05 The agent loop Plan, build, verify, commit, record — the rhythm that ties the other four together.

The agent loop

The four habits become a way of working when you run every change through the same five steps.

The agent loop: plan, build, verify, commit, record — then loop

Why it works

Each habit closes a specific failure mode of building with AI:

Failure mode The habit that prevents it
The agent forgets everything you agreed yesterday Persistent memory
A risky experiment breaks the branch you were about to ship Work-trees
A secret or a scratch file lands in git history forever Commit discipline
"It's done" — but it was never actually run Verify before you trust
Work drifts, no record of why a decision was made The agent loop

Templates

Ready-to-copy starting points live in templates/:

Template Use it for
COMMIT_RULES.template.md Drop into any repo as a local, git-ignored contract for how commits happen.
MEMORY.template.md The shape of a persistent memory index — one line per fact, loaded every session.

See it in action

examples/ has a working memory/ folder (a real index with user / feedback / project memories), a commit message that follows the discipline, and a before/after of the same afternoon of agent work — messy without the system, clean with it.

Works with any agent

The whole system is Markdown an agent reads — so it's tied to no vendor and no model:

Agent / setup How you apply it
Claude Code Point it at COMMIT_RULES.local.md and memory/; reference the habit docs in your prompt
Cursor · Cline · Continue · Aider Paste the commit contract into the rules field; keep the memory folder in-repo
Local models via Ollama Feed the two template files as context, or bake the commit rules into a Modelfile SYSTEM block
Any chat model Give it the habit doc for the task and the commit contract before it writes a commit

Because it runs on local models too, the whole workflow works offline, on private code that never leaves your machine. Recipes for each are in docs/integrations.md.

Repository layout

Hash-AI-Coding-persona/
├── README.md
├── LICENSE                          MIT
├── CONTRIBUTING.md                  how to propose a habit
├── ROADMAP.md                       what's coming next
├── docs/
│   ├── FAQ.md
│   └── integrations.md              per-agent recipes (incl. Ollama)
├── assets/
│   ├── hero.svg                     banner
│   ├── loop.svg                     the agent-loop diagram
│   └── social-card.svg              link-preview card
├── system/
│   ├── 01-memory.md
│   ├── 02-worktrees.md
│   ├── 03-commit-discipline.md
│   ├── 04-verification.md
│   └── 05-agent-loop.md
├── templates/
│   ├── COMMIT_RULES.template.md
│   └── MEMORY.template.md
└── examples/
    ├── README.md                    live memory folder, a real commit, before/after
    └── memory/                      a working memory index with sample facts

Who this is for

A solo developer, or a very small team, using coding agents to build real products — not toys. If you have ever force-pushed to purge a leaked secret, spent an afternoon untangling two features committed together, or shipped a "fix" that never worked, this system is the fix.

License

MIT — take it, adapt it, make it yours.

About

A written-down system for shipping serious software with AI coding agents — persistent memory, git work-trees, signed-commit discipline, and verify-before-you-trust, with copy-paste templates. Move at agent speed without the mess.

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