Metadata-Version: 2.4
Name: vardoger
Version: 0.2.0
Summary: Personalize AI coding assistants by analyzing conversation history
Project-URL: Homepage, https://github.com/dstrupl/vardoger
Project-URL: Repository, https://github.com/dstrupl/vardoger
Project-URL: Issues, https://github.com/dstrupl/vardoger/issues
Project-URL: Documentation, https://github.com/dstrupl/vardoger#readme
Project-URL: Changelog, https://github.com/dstrupl/vardoger/blob/main/CHANGELOG.md
Author-email: David Strupl <dstrupl@gmail.com>
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: ai,claude,codex,coding-assistant,cursor,openclaw,personalization
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
Classifier: Topic :: Software Development :: Quality Assurance
Classifier: Typing :: Typed
Requires-Python: >=3.11
Requires-Dist: mcp>=1.0.0
Requires-Dist: pydantic>=2.0
Requires-Dist: pyyaml>=6.0.3
Description-Content-Type: text/markdown

# vardoger

A cross-platform plugin for AI coding assistants (Cursor, Claude Code, OpenAI Codex, OpenClaw, GitHub Copilot CLI, Windsurf, Cline) 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](https://brew.sh/)) or [python.org/downloads/macos](https://www.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](https://www.python.org/downloads/windows/) |

### pipx

Recommended for installing vardoger as an isolated CLI tool. Full instructions at [pipx.pypa.io/stable/installation](https://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

```bash
pipx install vardoger
vardoger setup cursor        # or claude-code, codex, openclaw, copilot, windsurf, cline
```

Then tell your assistant: **"Personalize my assistant."**

> Looking for the in-app plugin listings? Track review status for each
> marketplace (Cursor, Claude Code, Codex, ClawHub) in
> [`docs/marketplace-status.md`](./docs/marketplace-status.md).

> **Previous pre-releases.** `pipx install vardoger` now resolves to the stable
> `0.1.0` line. 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 <platform>` | Register vardoger with a platform (`cursor`, `claude-code`, `codex`, `openclaw`, `copilot`, `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. |

## 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 |
| **GitHub Copilot CLI** | `~/.copilot/session-state/*.jsonl` | `~/.copilot/copilot-instructions.md` (global) or `<project>/.github/copilot-instructions.md` (project) — managed inside a `<!-- vardoger:start -->` fenced section | CLI-only |
| **Windsurf** | `~/.codeium/windsurf/**/*.jsonl` | `~/.codeium/windsurf/memories/global_rules.md` (global, fenced section) or `<project>/.windsurf/rules/vardoger.md` (project, dedicated file) | CLI-only |
| **Cline** | VS Code `globalStorage/.../tasks/*/api_conversation_history.json` | `<project>/.clinerules/vardoger.md` if `.clinerules` is a directory, otherwise a fenced section in `<project>/.clinerules` (project-only) | CLI-only |

## Development

Requires [uv](https://docs.astral.sh/uv/getting-started/installation/) (Python package manager):

```bash
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](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:

```bash
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](CONTRIBUTING.md) for the full walkthrough and [AGENTS.md](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](https://github.com/dstrupl/vardoger/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](https://docs.pypi.org/trusted-publishers/) (no API tokens needed). Once complete, `pipx install vardoger` will pull the new version.

## Status

Early development. See [PRD.md](PRD.md) for the full product requirements document.

## Privacy and security

- [PRIVACY.md](PRIVACY.md) — what vardoger reads, writes, and (importantly) does not send anywhere.
- [SECURITY.md](SECURITY.md) — how to report a vulnerability privately.

## License

Licensed under the [Apache License, Version 2.0](LICENSE).
