Mine your local AI coding session logs into a profile your agent loads before every task.
io.github.ohad6k/ditto (MCP Server)
Ditto mines your local AI coding session logs into a profile your agent loads before every task. It serves as a Model Context Protocol (MCP) server to provide persistent, local-first context for agents, enabling memory-informed decision-making.
🛠️ Key Features
Local-first model context management via MCP
Profiles built from coding session logs
Integrates with agent-memory and personalization workflows
Supports memory/context engineering for LLM agents
🚀 Use Cases
Personalization of AI agents with developer session history
Context-aware task execution in coding environments
Memory-driven agent behavior across tasks and sessions
⚡ Developer Benefits
Grounded context provisioning for agents before task runs
Reusable MCP server component for local-context pipelines
Facilitates memory and personalization integration with Claude/OpenAI tooling
⚠️ Limitations
Based on local logs; privacy considerations for sensitive data
Requires proper data handling to avoid overfitting or leakage
Documentation samples may reference broader MCP concepts and tooling
Your AI agents act like they just met you. Ditto fixes that.
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.
🡒 agent
🡒 agent + ditto
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
full reply
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
the full post it wrote
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:
200 upvotes · 32 comments · 102K views from an account with no followers. The cold plan was reasonable. The loaded plan knew its user's voice — and it worked.
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:
bash
npx skills add ohad6k/ditto@ditto
Then tell your agent:
text
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:
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:
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:
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:
bash
python ditto.py plugin preflight --preview
Quick preview creates a starter profile from selected history, not the full profile.
The quick-preview ladder is:
Candidate
New source text
Maximum planned passes
4 × 25K
100K tokens
4 workers + 1 reducer
6 × 25K
up to 150K tokens
up to 6 workers + 1 reducer
8 × 25K
160K-token hard cap
up to 8 workers + 1 reducer
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 history remains the quality default because it materially improves recall over preview, not because it guarantees a complete personal model. The validated pack keeps only supported rules; missing traits require future mining improvements rather than a softened score.
On update, unchanged segment and evidence hashes are reused. An identical update plans zero additional Ditto mining passes. New history plans only affected full-history work plus one reducer.
These are selected source tokens and planned worker/reducer passes, not provider billing events. Ditto cannot measure provider system prompts, tool traffic, orchestration overhead, or a percentage of a proprietary subscription allowance.
Experimental adaptive recall
The receipt-salience and scout pipeline remains available to developers through explicit --stage A, but it is experimental and is not used by the Plugin release, quality-default setup, updates, or calibration.
What makes the result trustworthy
Only real user-authored .jsonl messages are mined. AGENTS.md, CLAUDE.md, memory files, and typed self-descriptions are rejected as source evidence.
Every bounded worker covers work, design, and writing in one validated report.
Quotes must be short, dated, verbatim receipts from known session IDs.
Inferred rules require at least two distinct sessions and, when available, two source/time strata.
One uncontradicted explicit instruction may survive as low-frequency evidence.
Ditto's extractor, redaction, caches, and generated profiles stay local. Selected redacted text is processed by the model provider you choose. With a local model, the entire mining flow can remain local.
ditto.py itself is one stdlib-only file and makes no network calls. The skills.sh command downloads the selected bootstrap. Outside a repository checkout, that bootstrap downloads only ditto.py and MINING_PROMPT.md from the exact release tag after SHA-256 verification. Those downloads happen before log discovery and read no session data.
Redaction is best-effort and runs before selected text is written to Ditto caches. Inspect private output before sharing it. Share the card or one short trait, never your full profile or receipt appendix.
Both directions verified live: sessions mined from its SQLite store and legacy JSON layout (--source opencode), profile installed to its global rules (--target opencode)
Google Antigravity
Mining verified live against a real local install (--source antigravity): typed prompts extracted from ~/.gemini/antigravity/brain transcripts, harness envelopes stripped. Antigravity only writes transcripts when interaction logging is enabled in its privacy settings
Run update ditto to reuse stable caches and plan only changed work.
A GitHub star bookmarks the repository but does not subscribe you to releases. To receive release notifications, choose Watch → Custom → Releases on GitHub.
Limits
Ditto models how you work, design, and write. It does not make the underlying model smarter.
Sparse or repetitive histories can leave design or writing inactive. Ditto reports the exact targeted-deepen instruction instead of inventing a persona.
Benchmarks, leaderboard results, and proof videos are a separate later release.
FAQ
The three things people push back on, answered once.
"Why not just ask Claude to summarize my logs?"
One pass can't do it. My history is 1,656 sessions, about 3M tokens after extraction, and the raw logs are mostly tool output, file dumps, and pasted errors. A single summarize call burns the window on that noise. Ditto keeps only the words you typed, gives each validated segment its own evidence pass, and requires distinct supporting sessions before an inferred rule can survive. The resulting profile keeps session receipts instead of an obsolete worker-count score.
"Claude already has memory. Why do I need this?"
Use both. Memory is what you told the model: curated notes, CLAUDE.md, and it stays inside one tool. Ditto reads supported raw sessions from Codex, Claude Code, Copilot CLI, OpenCode, and Google Antigravity and pulls out what you never wrote down: what you reject, what "done" means to you, and when you demand proof. The output is plain files you own and can load through supported agents.
"Claude only keeps 30 days of logs. Where did 9 months come from?"
Claude Code's retention is a setting (cleanupPeriodDays, 30 by default), and my longer history combines Claude Code, Codex, and Copilot CLI sessions plus archives. If you keep the default retention, older Claude sessions can roll off before Ditto sees them. Raise the retention, then mine what's left.
Roadmap
See ROADMAP.md for what is intentionally deferred.
