/autobrowse

AutoBrowse

Self-improving browser automation via the auto-research loop. Build reliable, production-ready navigation skills for any website — overnight, autonomously.

How it works

An inner agent browses your target site and attempts the task. An outer agent (you, via /autobrowse) reads what went wrong and improves the instructions. Repeat until it passes consistently.

The output is a skill.md — a site-specific playbook any agent can follow. Once mature, it replaces expensive LLM exploration with deterministic, cached navigation. Typical cost reduction: 80%+.

Requirements

• Node.js 18+
• Claude Code
• browse CLI: npm install -g browse
• ANTHROPIC_API_KEY in your environment
• For bot-protected sites: BROWSERBASE_API_KEY

Setup

git clone <this-repo>
cd autobrowse
npm install
touch .env             # add ANTHROPIC_API_KEY (and BROWSERBASE_API_KEY if needed)

Your project structure

Create this in your working directory before running /autobrowse:

your-project/
├── tasks/
│   └── my-portal/
│       ├── task.md        ← describe what the agent should do
│       └── strategy.md    ← auto-created and improved each iteration
└── traces/                ← auto-created at runtime, add to .gitignore

See references/example-task.md for the task.md format.

Usage

Open Claude Code in your project directory and run:

/autobrowse --task my-portal

The skill runs the inner agent, reads the trace, improves strategy.md, and repeats. When the task passes consistently, a skill.md is written alongside strategy.md — that's your shippable output.

For multiple tasks in parallel:

/autobrowse --all --iterations 5 --env remote

Graduated skills

When a task's skill.md is ready, copy it into any agent's system prompt. It gives the agent precise, site-specific instructions — no more blind exploration on every run.

See references/example-skill.md for the format of a finished skill.

Environment modes

Use --env remote for sites with bot detection or when running multiple tasks simultaneously.

Architecture

Inspired by Karpathy's autoresearch — the same loop that optimizes ML experiments, applied to browser automation.

outer agent (Claude Code + /autobrowse skill)
  └── reads trace → improves strategy.md → repeats

inner agent (scripts/evaluate.mjs → Anthropic API)
  └── browse open → snapshot → click → snapshot → ...
  └── writes traces/ with summary, full trace, screenshots