AutoBrowse — Self-Improving Browser Skill

Build reliable browser automation skills through iterative experimentation. An inner agent browses the site (evaluate.ts). You — the outer agent — read what happened and improve the instructions (strategy.md). Repeat until it passes consistently.

Entry Points

Invocation is flexible — both explicit flags and free-form natural language work:

/autobrowse --task google-flights
/autobrowse --task google-flights --iterations 10 --env remote
/autobrowse --tasks google-flights,amazon-add-to-cart
/autobrowse --all

# Also fine — parse freely:
/autobrowse https://flights.google.com/
/autobrowse book a flight on delta.com
/autobrowse fix the existing google-flights skill

When the user drops a URL or free-form instruction instead of --task <name>:

• If an existing task in ${WORKSPACE}/tasks/ clearly matches the site/intent, use it.
• Otherwise, pick a short kebab-case name, create ${WORKSPACE}/tasks/<name>/task.md from ${CLAUDE_SKILL_DIR}/references/example-task.md, fill in the URL/goal based on what the user said, and proceed. Tell the user the chosen name in one line.

---

How to run

Step 1 — Parse arguments and orient

Check what was passed:

• --task <name> → single task mode
• --tasks a,b,c or --all → multi-task mode (spawn sub-agents)
• --iterations N → how many evaluate → improve cycles (default: 5)
• --env local|remote → browser environment (default: local; use remote for bot-protected sites)

If the user passed free-form text instead, map it to one of the above before continuing.

Step 2 — Set up the workspace

All training artifacts (task definitions, strategy iterations, traces, reports) live in a workspace directory in the current working directory — NOT inside ~/.claude/skills/. This keeps the inner agent's file writes out of Claude's home dir and away from permission friction.

Default workspace: ${CWD}/autobrowse/

mkdir -p ./autobrowse/tasks ./autobrowse/traces ./autobrowse/reports

If the task directory (./autobrowse/tasks/<task>/task.md) doesn't exist yet, scaffold it:

mkdir -p ./autobrowse/tasks/<task>
cp ${CLAUDE_SKILL_DIR}/references/example-task.md ./autobrowse/tasks/<task>/task.md
# Then edit task.md to describe the URL, inputs, steps, and expected JSON output

The skill source at ${CLAUDE_SKILL_DIR} stays read-only — only ./autobrowse/ in CWD gets written to during training. Graduation (final step) writes a single file to ~/.claude/skills/<task>/SKILL.md.

List available tasks:

ls ./autobrowse/tasks/

Step 3 — Multi-task: spawn parallel sub-agents

If running multiple tasks, use the Agent tool to spawn one sub-agent per task simultaneously. Each sub-agent receives a self-contained prompt to run the full autobrowse loop for its task:

"You are running the autobrowse skill for task <name>. Workspace: <absolute-path-to-workspace> (e.g. /path/to/project/autobrowse). Run <N> iterations of: evaluate → read trace → improve strategy.md → repeat. Use --env <env>. Pass --workspace <workspace> to every evaluate.mjs invocation. Follow the autobrowse loop instructions exactly.

When graduating, install the skill to ~/.claude/skills/<task-name>/SKILL.md with proper agentskills frontmatter (name + description). Do not just copy strategy.md — write a self-contained skill.

At the end, output a structured summary with: task name, pass/fail on final run, total cumulative cost, iterations completed, per-iteration table (iter number, turns, cost, status, hypothesis tested), and 2-3 bullet key learnings."

Spawn all sub-agents in parallel, wait for all to complete, then collect their summaries and write the session report.

For single task, skip this step and run the loop directly below.

---

The Loop (run this for each task)

Iteration start

Check that ./autobrowse/tasks/<task>/task.md exists (scaffold it from the template if not — see Step 2). strategy.md is auto-created empty by the harness on first run.

Requirements

• ANTHROPIC_API_KEY must be in the environment (or in a .env file in CWD — evaluate.mjs auto-loads it). If missing, the harness prints a clear error and exits; don't hunt for keys in other paths.

Run the inner agent

node ${CLAUDE_SKILL_DIR}/scripts/evaluate.mjs --task <task-name> --workspace ./autobrowse
# or for bot-protected sites:
node ${CLAUDE_SKILL_DIR}/scripts/evaluate.mjs --task <task-name> --workspace ./autobrowse --env remote

This runs the browser session and writes a full trace to ./autobrowse/traces/<task>/latest/.

Read the trace

cat ./autobrowse/traces/<task-name>/latest/summary.md

The summary has duration, cost, turns, the decision log, and the final JSON output.

If the agent failed or got stuck, look deeper:

• Read ./autobrowse/traces/<task-name>/latest/trace.json — search for the failure turn
• Read screenshots around the failure point with the Read tool

Form one hypothesis

Find the exact turn where things went wrong. What single heuristic would have prevented it?

Examples:

• "After clicking the dropdown, wait 1s — options animate in before they're clickable"
• "Navigate directly to /pay-invoice/ — skip the landing page entirely"
• "Use browse fill #field_3 value not browse type — this field clears on focus"
• "The page shows a spinner at turn 8 — add browse wait timeout 2000 before snapshot"

Update strategy.md

Edit ./autobrowse/tasks/<task-name>/strategy.md. Keep everything that worked. Fix the specific failure. Add a concrete heuristic.

Good strategies have:

• Fast path: direct URL or shortcuts to skip exploration
• Step-by-step workflow: exact sequence with timing notes
• Site-specific knowledge: selector IDs, form field names, success indicators
• Failure recovery: what to do when X goes wrong

Judge the result

Read the new summary. Did it pass? Make clear progress?

• Pass or progress → keep, next iteration
• No progress or regression → revert strategy.md to the previous version and try a different hypothesis

After all iterations — publish if ready

If the task passed on 2+ of the last 3 iterations or has reached the max iteration limit, install it as a Claude Code skill. Do not just copy strategy.md — the skill must be self-contained and useful to someone who has never seen this codebase. If graduating at max iterations without a clean pass, note the known failure point but still document everything learned.

Install by writing to ~/.claude/skills/<task-name>/SKILL.md:

mkdir -p ~/.claude/skills/<task-name>

Use this structure for the SKILL.md:

---
name: <task-name>
description: <1-2 sentences describing what this skill does and when to use it. Include trigger keywords.>
---

# <Task Title> — Browser Skill

## Purpose
<1-2 sentences: what this automates and why it exists.>

## When to Use
<When should someone reach for this skill.>

## Browse CLI Reference
The inner agent uses the `browse` CLI. Key commands for this task:
- `browse stop` — kill existing session (always run before switching to remote)
- `browse env remote` — start a fresh Browserbase cloud session
- `browse newpage <url>` — open URL in a new tab (required in remote mode — `browse open` fails with "no page available")
- `browse open <url>` — navigate existing tab (local mode only)
- `browse wait load` — wait for page to finish loading
- `browse wait timeout <ms>` — wait a fixed amount of time for spinners or animations
- `browse wait selector "<selector>"` — wait for an element to become visible
- `browse get title` — verify you're on the right page
- `browse get text body` — extract all visible text (preferred for content extraction)
- `browse snapshot` — get accessibility tree; each node has a ref in `[X-Y]` format (e.g. `[0-5]`, `[2-147]`)
- `browse click [X-Y]` — click element by ref from the latest snapshot (include the brackets)

**Never use `--session <name>` flags in SKILL.md.** Named sessions are a parallel-run workaround — they contaminate skills with infrastructure concerns. Skills must work in isolation with the default session.

## Workflow

### Step 1 — Start session
<exact browse commands in order>

### Step 2 — Navigate
<exact URL and verification steps>

### Step 3 — Extract
<exact extraction commands>

### Step 4 — Output
<what JSON to emit, referencing the schema below>

## Site-Specific Gotchas
<Bullet list of every hard-won heuristic from the iterations. This is the core value of the skill.>

## Failure Recovery
<What to do when navigation fails, session is contaminated, or extraction returns garbage>

## Expected Output
```json
<paste the exact expected output schema from task.md>

After writing the SKILL.md, confirm it's installed:
```bash
ls ~/.claude/skills/<task-name>/SKILL.md

The skill is now available as /<task-name> in Claude Code.

---

Final report (multi-task mode)

After all sub-agents complete, print a markdown table:

Task	Iterations	Final Status	Graduated	Cost
google-flights	5	✅ pass	yes	$0.42
amazon-add-to-cart	5	❌ fail	no	$1.20

Then write a persistent session report to ./autobrowse/reports/ so there's a durable record of the run inside the workspace:

mkdir -p ./autobrowse/reports

Write the file ./autobrowse/reports/YYYY-MM-DD-HH-MM-<tasks>.md with:

# AutoBrowse Session Report
**Date:** <ISO date>
**Tasks:** <comma-separated list>
**Environment:** remote|local
**Total cost:** $X.XX

## Results

| Task | Iterations | Pass Rate | Final Status | Graduated | Cost |
|------|-----------|-----------|--------------|-----------|------|
| ... | ... | X/5 | ✅/❌ | yes/no | $X.XX |

## Per-Task Learnings

### <task-name>
- **Key insight 1:** <what the agent learned>
- **Key insight 2:** <another heuristic>
- **Failure mode fixed:** <what was failing and how it was resolved>

## Iteration Log

### <task-name>
| Iter | Turns | Cost | Status | Hypothesis tested |
|------|-------|------|--------|-------------------|
| 1 | 79 | $18.75 | ❌ fail | baseline |
| 2 | 9 | $0.26 | ✅ pass | session contamination fix |
| ... | ... | ... | ... | ... |

---

Rules

• Only edit strategy.md — never touch task.md (unless creating it from the template) or evaluate.mjs
• Stay in the workspace — all training writes go to ./autobrowse/, never to ~/.claude/skills/autobrowse/. The skill source is read-only.
• One hypothesis per iteration — test one change at a time
• Build on wins — keep what worked, add to it
• Trust the trace — the inner agent shows exactly what it saw and did
• Graduate to ~/.claude/skills/ — the only file you write there is the final graduated SKILL.md