ditto

Your real coding-agent sessions already contain the rules you never wrote down: what “done” means, what you reject on sight, how you debug, how you design UI, and how you write when you are actually working.

Ditto mines selected evidence from those sessions — Claude Code, Codex, Copilot CLI, OpenCode, and Google Antigravity logs out of the box — into a private working profile your agent reads before every task. Separate layers for work, design, and writing mean the right part of you loads for the right task.

Not memory

Memory is what you explicitly told the model.

Ditto mines what your work already proved about you: what you reject, what "done" means, when you ask for proof, how you talk when you're actually working, and the agent behaviors that make you stop the task.

That's why it reads raw session logs, not your CLAUDE.md or rules file.

A real run

One line to the same agent, twice: "help me post ditto on reddit." Once cold, once with the Ditto profile loaded.

Here's how I'd play it.

Subreddits: r/ClaudeAI and r/ChatGPTCoding are your core audience. r/LocalLLaMA fits the local angle. r/cursor, r/SideProject, r/opensource for reach. Don't blast all at once — post to one, tune the title, repost over a few days.

Title: I built a local tool that reads your AI coding logs and writes a "you.md" your agents read before every task

Titles (pick per sub):

• I built a local tool that reads your AI coding logs and writes a "you.md" your agents read before every task
• ditto: mine your own Claude Code history into a profile so your agent codes like you (local, open source)

Body:

Every fresh agent session starts from zero — it doesn't know how you work, so you re-explain yourself every time. All of that is already in your session logs. So I built ditto: it reads your history, mines how you decide and talk, and writes a you.md your agents read before every task.

• runs locally, your logs never leave your machine
• redacts secrets before processing
• open source: [link]

Also: open with the pain not the product, reply fast in the first hour, add a demo gif, and flair it correctly. Want me to write tuned variants for each subreddit?

the insight first: the mining number is your whole post. "i read 1,656 of my own sessions" is a confession, not a pitch, and nobody scrolls past a confession. lead with the mirror, not the mechanism.

one sub, not five — a skills sub, where people come looking for this instead of arguing about it.

Title: I built a Claude skill from 8 months of my own Claude Code sessions

body: I use claude code and codex every day. 8 months, solo. every session gets logged and i never really looked back at them.

then it hit me that those logs are probably the most honest record of how i actually work. not my CLAUDE.md, not my docs, not the rules i remembered to write down. the real sessions.

so i built ditto.

What I did: pulled every message i typed from my local claude/codex logs. stripped tool output, pasted errors, file dumps, and assistant replies. kept only my words. came out to around 1,656 sessions and almost 3M tokens of just me.

then i split it into chunks and had agents read each slice. each one pulled patterns like how i define done, what kind of code i reject, when i ask for proof, what makes me stop a task, and how i talk when i am actually working.

then i merged the repeated traits into one you.md.

the useful part is that it installs as a claude skill. so now instead of starting every task cold, claude reads my profile first and already knows how i work.

not memory exactly. memory is what you told the model. this is more like mining what your work already proved about you.

I posted it on r/ClaudeSkills, as written:

What it finds

The kind of rules a mine pulls out, each backed by dated verbatim receipts from real sessions:

done means it runs live. never trust "done" off a code edit. show it working first.

fix the one thing. rewriting or "cleaning up" code that isn't the problem gets rejected every time.

builds faster than they understand what they built — then asks the agent to explain their own system back.

gets frustrated by repeating the same ask until it lands, not by escalating.

Nobody wrote those rules down. They came out of one person's own history, with receipts.

This is an example. Yours is mined from your logs and will read nothing like it.

The card

After mining, python ditto.py --card renders your profile as a shareable card: archetype, top laws ranked by distinct supporting session receipts, coverage stats, and one sharp truth.

Share the card or one short trait, never your full profile.

Quickstart

Install the cross-agent bootstrap — runs in Claude Code and Codex, and installs profiles for Cursor and Gemini through the explicit adapters:

npx skills add ohad6k/ditto@ditto

Then tell your agent:

run ditto

That installs the bootstrap and creates a read-only full-history mining plan. Your agent must show the cost and wait for approval before model work. It does not install native namespaced routing.

Native Codex plugin

The native plugin adds ditto:mine, ditto:work, ditto:design, and ditto:write:

codex plugin marketplace add ohad6k/ditto --ref v0.3.8 --json
codex plugin add ditto@ditto --json

The plugin-install command itself scans no logs, writes no private profile state, and schedules zero mining model calls. Asking an agent to install, run, or update Ditto still consumes that host interaction plus its normal system and tool overhead.

Native Claude Code plugin

The Claude Code plugin exposes the same four skills. Install it from inside Claude Code:

/plugin marketplace add ohad6k/ditto
/plugin install ditto@ditto

MCP server

Ditto also ships a Model Context Protocol (MCP) server, so any MCP client — Claude Desktop, Cursor, and other agents — can load your profile before a task. The server implements MCP over stdio and exposes one tool, load_ditto_profile, which returns your mined work, design, or writing profile over the Model Context Protocol.

Run it from the published package with uvx ditto-cli mcp, or from a checkout with python ditto.py mcp, and point an MCP client at it:

{
  "mcpServers": {
    "ditto": { "command": "uvx", "args": ["ditto-cli", "mcp"] }
  }
}

The MCP server is stdlib-only and serves the profile you already mined locally; it makes no network calls of its own.

What happens when you run it

Ditto first prints a read-only plan:

{
  "valid_sessions": "--",
  "post_dedupe_source_tokens": "--",
  "mode": "full",
  "profile_scope": "full_profile",
  "quality_default": true,
  "candidate_index": null,
  "selected_source_tokens": "--",
  "planned_worker_calls": "--",
  "planned_reducer_calls": "--"
}

The full-history quality default reads all eligible history. Ditto shows the exact plan first and waits for approval before any worker or reducer runs. Cached reports are reused, so the displayed remaining cost can fall over time.

If you explicitly want a cheaper first look, ask for run ditto quick preview or use --preview:

python ditto.py plugin preflight --preview

Quick preview creates a starter profile from selected history, not the full profile.

The quick-preview ladder is:

The frozen calibration recovered only 5 of 22 required traits at the widest bounded candidate. Quick preview therefore cannot be described as the quality default unless a future run passes all 22 frozen requirements. The permanent non-private baseline is in tests/fixtures/bounded-calibration-baseline.json.

The first real full-history release mine recovered 12 of the same 22 frozen requirements: work 5/10, design 5/5, and writing 2/7. Full histo