vardoger

Name: vardoger
Author: dstrupl

by dstruplversion 0.4.0i★ 5iRuns on your computer

Registry nameio.github.dstrupl/vardoger

Install

Overview

Description from the maker (original English)

Personalize AI coding assistants by analyzing local conversation history. Runs on your machine.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi2 of 2 passedChecked from public information; code not scanned yet
UsageiVery new↓ 22/wk · ★ 5

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Developer tools

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 2 of 2 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: VARDOGER_MCP_PLATFORM

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "vardoger": {
      "command": "uvx",
      "args": [
        "vardoger",
        "mcp"
      ],
      "env": {
        "VARDOGER_MCP_PLATFORM": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add vardoger --env VARDOGER_MCP_PLATFORM=your-value -- uvx vardoger mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "vardoger": {
      "command": "uvx",
      "args": [
        "vardoger",
        "mcp"
      ],
      "env": {
        "VARDOGER_MCP_PLATFORM": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "vardoger": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "vardoger",
        "mcp"
      ],
      "env": {
        "VARDOGER_MCP_PLATFORM": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

VARDOGER_MCP_PLATFORM env
Override the host platform identifier (cursor, claude-code, codex, copilot, devin, windsurf, cline, openclaw). When unset, vardoger uses the calling host's defaults.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

Which apps can use it

Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.

Run on your computer in

Claude Goose LM Studio Msty Studio Cherry Studio Cursor VS Code + GitHub Copilot Visual Studio Windsurf (Devin Desktop)Zed Kiro Cline Continue JetBrains AI Assistant Claude Code Codex CLI Gemini CLI OpenCode nodecode Warp LibreChat Raycast

All apps, with pros and cons →

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 11, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account dstrupl, which owns the repo
✓ Pass
Still maintained
Code last changed 44 days ago
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Developer toolsi

More stars than 84% of the other 5739 servers in this category
Published to the registry more recently than 44% of the other 5739 servers in this category
Basic checks equal to or better than 100% of the other 5739 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is vardoger safe?

It passed 2 of 2 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed 44 days ago, and the latest version (0.4.0) reached the registry 44 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: REA, Netdata, Front-End Checklist

Alternatives

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Front-End Checklist

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

vardoger

A cross-platform plugin for AI coding assistants (Cursor, Claude Code, OpenAI Codex, OpenClaw, GitHub Copilot CLI, Devin Local, legacy Windsurf, and Cline) that reads supported local conversation-history formats, extracts behavioral patterns, and generates personalized system prompt additions — making the assistant progressively better suited to how you work. Current format limitations are called out per platform below rather than silently reading undocumented stores.

History discovery, parsing, checkpoints, and generated rules stay on your machine. Analysis is performed by the host assistant you invoke, so selected conversation excerpts follow that assistant and model provider's data policy. Vardoger operates no backend and sends no telemetry.

Prerequisites

Python 3.11+

Platform	Command
macOS	`brew install python@3.13` (install Homebrew) or python.org/downloads/macos
Debian / Ubuntu	`sudo apt install python3`
Fedora	`sudo dnf install python3`
Windows	`winget install Python.Python.3.13` or python.org/downloads/windows

pipx

Recommended for installing vardoger as an isolated CLI tool. Full instructions at pipx.pypa.io/stable/installation.

Platform	Command
macOS	`brew install pipx && pipx ensurepath`
Debian / Ubuntu	`sudo apt install pipx && pipx ensurepath`
Fedora	`sudo dnf install pipx && pipx ensurepath`
Windows	`scoop install pipx` or `pip install --user pipx && pipx ensurepath`

Quick Start

pipx install vardoger

vardoger setup cursor # or claude-code, codex, openclaw, copilot, devin, windsurf, cline

Then tell your assistant: "Personalize my assistant."

Looking for the in-app plugin listings? Track review status for each

marketplace (PyPI, Cursor, Claude Code, Codex, Copilot CLI, Devin Desktop,

Cline,

ClawHub) in MARKETPLACE_STATUS.md.

Vardoger is currently live on PyPI, the Claude Code community catalog,

self-hosted Codex and Copilot marketplaces, McpMux, and the Official MCP

Registry. The former Cursor listing is currently unavailable. ClawHub has

regressed to 0.3.1 and is intentionally frozen because its mandatory MIT-0

terms conflict with this Apache-2.0 project. Current OpenClaw history has a

live-accepted, opt-in full-read Gateway CLI path through the direct install.

Previous pre-releases. pipx install vardoger now resolves to the stable

0.4.0 release. The beta install paths below stay here for anyone still pinning

an earlier release; new installs should not need them.

`bash

# opt into future pre-releases (0.2.0bN, etc.):

pipx install --pip-args="--pre" vardoger

# or pin an older pre-release:

pipx install vardoger==0.1.0b3

# or run without installing:

uvx vardoger --help

`

CLI Commands

Command	Purpose
`vardoger setup`	Register vardoger with a platform (`cursor`, `claude-code`, `codex`, `openclaw`, `copilot`, `devin`, `windsurf`, `cline`).
`vardoger status [--platform X] [--json]`	Report whether each personalization is fresh or stale.
`vardoger prepare --platform X [--batch N] [--synthesize]`	Produce the batched prompts used by the AI-driven skill pipeline.
`vardoger write --platform X`	Read synthesized personalization from stdin and write it to the platform's rules file (supports YAML-frontmatter confidence metadata).
`vardoger feedback accept\	reject --platform X [--reason TEXT]`	Record whether you kept or rejected the last generation. `reject` auto-reverts to the prior generation.
`vardoger compare --platform X \	--all [--window DAYS] [--json]`	Compare heuristic conversation-quality metrics before vs. after the latest personalization.
`vardoger profile sources [--platform X] [--json]`	List saved generations and their stable one-based selection IDs.
`vardoger profile preview --source X:N [--source Y:latest]`	Compile explicitly selected generations into a normalized cross-host profile and show the portable `AGENTS.md` diff without writing.
`vardoger profile write --source X:N --target ./AGENTS.md [--apply]`	Show the same diff; update only Vardoger's fenced block when the user explicitly adds `--apply`.

How It Works

Read — Parses supported conversation files already stored on disk, including opted-in Devin ATIF exports
Analyze — The host AI model identifies patterns in your communication style, tech stack, workflow, and preferences
Generate — Produces a system prompt addition tailored to you
Deliver — Writes the addition to the platform's native config (.cursor/rules/, .claude/rules/, AGENTS.md, etc.)

First run vs. incremental runs. By default vardoger does not apply a

time window — the first run reads your full local history (that is when the

signal is richest and a windowed default would silently drop older sessions

you never get a second chance to feed in). After that, a per-conversation

checkpoint store at ~/.vardoger/state.json ensures every subsequent run

only reprocesses new or changed conversations, so refreshes stay fast.

If you have very large local history and want to cap the first-run cost,

pass --since DAYS (e.g. vardoger prepare --platform cursor --since 90);

use --full to force a full re-crawl that bypasses the checkpoint.

Cross-host profile compiler

The profile compiler combines only existing Vardoger generations that you select; it does not silently reopen raw transcripts. Generation indexes are one-based, while latest resolves to the newest saved generation for that platform:

# Discover the reviewed generations available for selection.

vardoger profile sources

Read-only: render the profile, audit summary, and exact AGENTS.md diff.

vardoger profile preview \ --source claude-code:latest \ --source codex:2 \ --target ./AGENTS.md

Optional controls are applied before rendering and included in the audit.

vardoger profile preview \ --source codex:latest \ --min-confidence medium \ --max-age 180 \ --redact '@example\.com' \ --target ./AGENTS.md

Still preview-only without --apply. This is the explicit write step.

vardoger profile write \ --source claude-code:latest \ --source codex:2 \ --target ./AGENTS.md \ --apply

The normalized JSON audit is available with profile preview --json. It records source platform, generation, output hash/path, observation time, recency, confidence, conflicts, supersession, and exclusions. Opposing rules such as “Prefer tabs” and “Avoid tabs” are withheld from active instructions until reviewed. Writes preserve everything outside and .

Supported Platforms

Platform	History Source	Prompt Delivery	Integration
Cursor	Agent transcript JSONL	`.cursor/rules/vardoger.mdc`	Marketplace plugin + MCP
Claude Code	Session JSONL	`.claude/rules/vardoger.md`	Community/custom plugin
OpenAI Codex	Session rollout JSONL	`~/.codex/AGENTS.md`	Repository marketplace plugin
OpenClaw	Legacy JSONL or opt-in full read through official Gateway CLI; private SQLite is never queried	`~/.openclaw/skills/vardoger-personalization/SKILL.md`	Analyzer + Gateway compatibility status
GitHub Copilot CLI	`~/.copilot/session-state/ /events.jsonl` plus legacy flat JSONL	`~/.copilot/copilot-instructions.md` (global) or `/.github/copilot-instructions.md` (project) — managed inside a fenced section	Custom plugin + published skill
Devin Local	User-enabled Devin CLI `--export` ATIF JSON under `~/.vardoger/imports/devin/`	`~/.config/devin/AGENTS.md` (global, fenced section) or `/.devin/rules/vardoger.md`	ATIF + skill + rules integration
Legacy Windsurf / Cascade	`~/.codeium/windsurf/*/.jsonl`	`~/.codeium/windsurf/memories/global_rules.md` (global, fenced section) or `/.windsurf/rules/vardoger.md` (project, dedicated file)	Legacy Cascade skill + CLI + MCP
Cline	VS Code `globalStorage/.../tasks/*/api_conversation_history.json`	`~/Documents/Cline/Rules/vardoger.md` (global) or the existing `.clinerules` project layouts	CLI + MCP

Development

Requires uv (Python package manager):

git clone https://github.com/dstrupl/vardoger.git

cd vardoger uv sync .venv/bin/vardoger --help

Project Layout

src/vardoger/          # shared core — history reading, analysis, prompt generation

.agents/plugins/ # repository-level Codex marketplace catalog plugins/_shared/ # shared analysis/personalization skill authored once plugins/cursor/ # Cursor MCP server config, install script plugins/claude-code/ # Claude Code plugin manifest, skills plugins/codex/ # Codex plugin manifest, skills plugins/openclaw/ # OpenClaw skill plugins/copilot/ # GitHub Copilot CLI plugin manifest, skills plugins/devin/ # Devin Local ATIF export, skill, and rules integration plugins/windsurf/ # Legacy Cascade install snippet and rules delivery plugins/cline/ # Cline integration and marketplace install guidance tests/ # all tests, mirroring src/ structure

Platform-agnostic logic lives under src/vardoger/.
Platform-specific integration (manifests, skills, install scripts) lives under plugins/ /.
Tests live in tests/, mirroring the source tree.

See AGENTS.md for full coding standards and quality checks.

Quality gates

CI enforces a combined quality bar on every push and pull request:

ruff check / ruff format --check — lint (incl. complexity, pylint, return, pathlib, tryceratops rules) and formatting.
mypy src/ — strict type checking.
pytest --cov=vardoger --cov-fail-under=80 — tests across Python 3.11–3.13 with a combined 80% coverage floor.
A parallel security job runs bandit -r src/ and pip-audit --skip-editable to catch common code smells and dependency CVEs.

Run the full bundle locally before pushing:

uv run ruff check . && uv run ruff format --check . && uv run mypy src/ && uv run pytest --cov=vardoger --cov-fail-under=80

Contributing

Contributions are welcome. Short version:

Fork dstrupl/vardoger on GitHub and clone your fork.
uv sync and create a topic branch.
Make your changes with tests and run the quality-gate one-liner above.
Push to your fork and open a PR against main.

CI (test on Python 3.11/3.12/3.13 plus a security job) will run automatically on the PR. First-time contributors may need a maintainer to click Approve and run before the first workflow execution.

See CONTRIBUTING.md for the full walkthrough and AGENTS.md for coding standards and commit-message conventions.

Releasing to PyPI

CI runs automatically on every push and PR (lint, type check, tests across Python 3.11–3.13). To publish a new version:

Bump the package, generated setup manifests, platform manifests, and registry metadata together; close the matching section in CHANGELOG.md.
Run the complete quality, security, plugin-validation, and build checks.
Commit and push to main, then wait for all required checks.
Create an annotated vX.Y.Z tag and publish the matching [GitHub release](https://github.com/dstru

Only the beginning is shown; read the rest in the repo

vardoger

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

Which apps can use it

Related guides

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Developer toolsi

Common questions

Alternatives

REA

Netdata

Front-End Checklist

README from the maker

vardoger

Prerequisites

Python 3.11+

pipx

Quick Start

CLI Commands

How It Works

Cross-host profile compiler

Read-only: render the profile, audit summary, and exact AGENTS.md diff.

Optional controls are applied before rendering and included in the audit.

Still preview-only without --apply. This is the explicit write step.

Supported Platforms

Development

Project Layout

Quality gates

Contributing

Releasing to PyPI