vardoger 0.1.0b1

Personalize AI coding assistants by analyzing conversation history

vardoger

A cross-platform plugin for AI coding assistants (Cursor, Claude Code, OpenAI Codex, OpenClaw) that reads your conversation history, extracts behavioral patterns, and generates personalized system prompt additions — making the assistant progressively better suited to how you work.

All processing happens locally. No data ever leaves your machine.

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

Then tell your assistant: "Personalize my assistant."

CLI Commands

Command	Purpose
vardoger setup <platform>	Register vardoger with a platform (cursor, claude-code, codex, openclaw).
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.

How It Works

1. Read — Parses conversation history already stored on disk by each platform
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.)

Supported Platforms

Platform	History Source	Prompt Delivery	Integration
Cursor	Agent transcript JSONL	.cursor/rules/vardoger.md	MCP server
Claude Code	Session JSONL	.claude/rules/vardoger.md	Plugin with skill
OpenAI Codex	Session rollout JSONL	~/.codex/AGENTS.md	Plugin with skill
OpenClaw	Session JSONL	~/.openclaw/skills/vardoger-personalization/SKILL.md	Skill

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
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
tests/                 # all tests, mirroring src/ structure

• Platform-agnostic logic lives under src/vardoger/.
• Platform-specific integration (manifests, skills, install scripts) lives under plugins/<platform>/.
• 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 version in pyproject.toml
2. Commit and push to main
3. Go to Releases > Create a new release
4. Create a new tag matching the version (e.g. v0.1.0), add a title and description
5. Click Publish release

The publish.yml workflow builds the package and uploads it to PyPI via trusted publishers (no API tokens needed). Once complete, pipx install vardoger will pull the new version.

Status

Early development. See PRD.md for the full product requirements document.

License

Licensed under the Apache License, Version 2.0.