vardoger

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+

pipx

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

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

How It Works

1. Read — Parses supported conversation files already stored on disk, including opted-in Devin ATIF exports
2. Analyze — The host AI model identifies patterns in your communication style, tech stack, workflow, and preferences
3. Generate — Produces a system prompt addition tailored to you
4. 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

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:

1. Fork dstrupl/vardoger on GitHub and clone your fork.
2. uv sync and create a topic branch.
3. Make your changes with tests and run the quality-gate one-liner above.
4. 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:

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