# Browser-BC

**Repository Path**: shifeipython/Browser-BC

## Basic Information

- **Project Name**: Browser-BC
- **Description**: No description available
- **Primary Language**: JavaScript
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-08-01
- **Last Updated**: 2026-08-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Journey Forge Local

[![arXiv](https://img.shields.io/badge/arXiv-2606.32014-b31b1b.svg)](https://arxiv.org/abs/2606.32014)

> Productized companion to our paper **[Scalable Behaviour Cloning on Browser Using via Skill Distillation](https://arxiv.org/abs/2606.32014)** (arXiv:2606.32014). See [Paper & citation](#paper--citation).

Record your own browser tasks → each website accumulates a **bucket of
capabilities**, and every capability distills into one reusable skill → use them
in **Claude Desktop** (and Claude Code). A single-user, local product. You bring
your own LLM API key. Pure Python at runtime (no Node needed). Not a benchmark.

```
extension (record free-form task)
   └─► localhost server :8099   /v1/traces/init → chunks → finalize
          └─ assemble → data/traces/<id>/trace.json  (intent + events)
          └─ background harness pipeline:
               atomize → classify (capability) → bucket (per domain+capability)
               → distill bucket → SKILL.md + TRACE_GUIDE.md  → install
                 ├─ Claude Code:    ~/.claude/skills/<domain>-<capability>/SKILL.md (auto)
                 └─ Claude Desktop: <name>.zip → upload via Settings → Skills
   /api/buckets  exposes per-site capability buckets + skills
```

Each website is a folder of capability buckets, e.g.
`github.com/{login-with-credentials, signup-with-email, …}` — a new recording is
atomized into segments, each segment classified into a capability, and segments
of the same capability pooled into one bucket that distills into one skill.

## Layout

| Path | What |
|---|---|
| `entry/main.py` | Desktop entry — starts the server, opens the panel in your browser |
| `app/dist/index.html` | Control panel (zero-build, served by the server) |
| `extension/` | The recorder (product fork: points at localhost, free-form only) |
| `server/server.py` | Local ingestion + control API |
| `harness/` | Distillation pipeline: atomize / classify / bucket / distill / install (pure Python) |
| `harness/main.py` | CLI: `ingest` / `distill` / `status` / `consolidate` / `query` |
| `scripts/start.sh` | Headless dev launcher |
| `docs/` | Trace schema + Claude Desktop setup |

State lives under `data/harness/`: `buckets.json`, `registry.json`, and
`skills/<domain>/<capability>/{SKILL.md,TRACE_GUIDE.md,meta.json,evidence.jsonl}`.

## Quick start

1. **Configure & run the server**
   ```bash
   cp config.example.env .env.local      # then set SF_LLM_KEY=sk-ant-...
   ./scripts/start.sh                     # or: python entry/main.py  (native window)
   ```
   The panel is at <http://127.0.0.1:8099/>. The server seeds a default key
   (`jfl-local-dev-key`) the extension ships with, so it connects automatically.

2. **Build & load the extension**
   ```bash
   cd extension && pnpm install && pnpm build
   ```
   Chrome → `chrome://extensions` → enable Developer mode → **Load unpacked** →
   select `extension/dist/chrome-mv3`. It's pre-pointed at the local server.

3. **Record → finalize.** Record a short task with the extension, stop, label,
   upload. With auto-distill on, a skill appears within ~1–3 min:
   - **Claude Code**: already installed under your skills root.
   - **Claude Desktop**: download the `.zip` from the panel (Trajectories) and
     upload it in Settings → Skills. See [`docs/claude-desktop-setup.md`](docs/claude-desktop-setup.md).

4. **(Optional) real browser execution.** Panel → *Browser execution* →
   *Configure Playwright MCP* so Claude Desktop can actually click/type/navigate.
   Restart Claude Desktop after.

## Notes

- **Skills don't grant tools.** A `SKILL.md` is injected instructions. To
  *execute* its steps in a browser you must configure a browser MCP (Playwright)
  separately — the panel automates this for Claude Desktop.
- **LLM:** the distiller speaks the Anthropic Messages API natively (default
  `claude-opus-4-8`). Point `SF_LLM_BASE` at an OpenAI-compatible gateway to use
  that path instead.
- Data lives under `data/` (git-ignored). Nothing leaves your machine except
  the distillation calls to your configured LLM.

## Paper & citation

This tool is the productized companion to our research on distilling reusable
browser skills from human interaction traces — turning scalable human browsing
into a library of per-site, per-capability skills that browser agents can reuse:

> **Scalable Behaviour Cloning on Browser Using via Skill Distillation**
> Kaisen Yang, Zheng Jiang, Yuzhao Peng, Houde Qian, Boshi Zhang, Youjie Zheng,
> Shijin Hong, Qingle Liu, Ruoyu Han, Bohan Lyu, Bingxiang He, Eren Cai,
> Calvin Xiao, Qinhuai Na.
> arXiv:2606.32014, 2026. <https://arxiv.org/abs/2606.32014>

If you use this project or the ideas behind it, please cite:

```bibtex
@article{yang2026scalable,
  title   = {Scalable Behaviour Cloning on Browser Using via Skill Distillation},
  author  = {Yang, Kaisen and Jiang, Zheng and Peng, Yuzhao and Qian, Houde and
             Zhang, Boshi and Zheng, Youjie and Hong, Shijin and Liu, Qingle and
             Han, Ruoyu and Lyu, Bohan and He, Bingxiang and Cai, Eren and
             Xiao, Calvin and Na, Qinhuai},
  journal = {arXiv preprint arXiv:2606.32014},
  year    = {2026}
}
```