autobrowse

name	autobrowse
description	Self-improving browser automation via the auto-research loop. Iteratively runs a browsing task, reads the trace, and improves the navigation skill (strategy.md) until it reliably passes. Supports parallel runs across multiple tasks using sub-agents. Use when you want to build or improve browser automation skills for specific website tasks.
license	MIT
compatibility	Requires Node.js 18+, browse CLI, and ANTHROPIC_API_KEY. Run from the autobrowse app directory.
allowed-tools	Bash Read Write Edit Glob Grep Agent
metadata	{"author":"browserbase","homepage":"https://github.com/browserbase/skills"}

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 --task google-flights --browser-trace
/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

--browser-trace (default off, remote-only): pairs each iteration with the sibling browser-trace skill — wraps the inner agent in a CDP capture for per-page network/console/page-lifecycle evidence. Implies --env remote; errors if combined with --env local. Requires the sibling browser-trace skill present at ${CLAUDE_SKILL_DIR}/../browser-trace/, and the BROWSERBASE_API_KEY env var.

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)
• --browser-trace → opt in to the browser-trace integration (default off). Implies --env remote. If --env local --browser-trace are both passed explicitly, error with: browser-trace requires Browserbase; drop --env local or drop --browser-trace.

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. If the parent invocation used --browser-trace, you MUST use the traced-path block of the SKILL.md loop for every iteration (pre-create session, attach bb-capture, pass --connect-url to evaluate.mjs, stop+bisect, release) — do not fall back to the default single-command path. 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

Default path (no --browser-trace) — single command, no orchestration:

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/.

Traced path (--browser-trace, remote only) — the outer harness pre-creates a Browserbase session, attaches bb-capture as a passive observer, and passes the session's connectUrl to evaluate.mjs so every inner browse call uses --cdp $connectUrl --session autobrowse-main (the canonical browser-trace pattern that gives observers full Network/Console events). Run this block once per iteration with $N set to the 1-indexed iteration number:

# Preflight — fail fast if browser-trace isn't installed alongside autobrowse.
BT_DIR="${CLAUDE_SKILL_DIR}/../browser-trace"
if [ ! -f "$BT_DIR/scripts/bb-capture.mjs" ]; then
  echo "ERROR: --browser-trace requires the browser-trace skill at $BT_DIR." >&2
  echo "Install it by cloning github.com/browserbase/skills and copying skills/browser-trace/" >&2
  echo "into the same parent directory as autobrowse (e.g. ~/.claude/skills/browser-trace/)." >&2
  exit 1
fi

# a. SESSION SETUP — pre-create the keep-alive session and derive its connectUrl
sid=$(browse cloud sessions create --keep-alive --verified --proxies \
  | node -e "let s='';process.stdin.on('data',c=>s+=c).on('end',()=>process.stdout.write(JSON.parse(s).id))")
connect_url=$(browse cloud sessions get "$sid" \
  | node -e "let s='';process.stdin.on('data',c=>s+=c).on('end',()=>process.stdout.write(JSON.parse(s).connectUrl))")

RUN_ID="run-$(printf '%03d' "$N")"
TRACE_ROOT="./autobrowse/traces/<task-name>/$RUN_ID"
mkdir -p "$TRACE_ROOT"
export O11Y_ROOT="$TRACE_ROOT/.o11y"   # park browser-trace output inside the autobrowse run dir
export O11Y_RUN_ID="$RUN_ID"           # tells the browse CLI which run dir to write descriptors.ndjson into

# b. ATTACH BROWSER-TRACE — passive observer; runs in background
node ${CLAUDE_SKILL_DIR}/../browser-trace/scripts/bb-capture.mjs   &
 2



node /scripts/evaluate.mjs \
  --task <task-name> --workspace ./autobrowse -- remote \
  --connect-url  --run-number 




node /../browser-trace/scripts/stop-capture.mjs 
node /../browser-trace/scripts/bisect-cdp.mjs 
node /scripts/unify-trace.mjs \
  --trace-dir  \
  --o11y-dir 


browse cloud sessions update  --status REQUEST_RELEASE

This writes the inner-agent trace to ./autobrowse/traces/<task-name>/latest/ and the CDP bisect to ./autobrowse/traces/<task-name>/latest/.o11y/<run-id>/. The traced browse CLI also emits per-command rich node descriptors to .o11y/<run-id>/cdp/descriptors.ndjson (one JSON object per page-driving call: target tag/id/role/accessibleName/attributes/xpath/bounding-rect). The descriptors file feeds downstream codegen; it is not required for hypothesis formation — skip it when reading the trace.

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

When --browser-trace was used — start with unified-events.jsonl. The harness joins the agent's turn log and the browser's CDP firehose into one time-ordered NDJSON stream at the run root. One file, source-tagged (source: "agent" | "browser"), interleaved by wall-clock timestamp. Skim it top-to-bottom; the failure cause is usually one or two adjacent lines (the agent issued command X, the browser responded with Y).

cat ./autobrowse/traces/<task-name>/latest/unified-events.jsonl

The structured files (trace.json, .o11y/<run-id>/cdp/*) are also agent-consumable as drill-downs when the unified stream points at something you need more of:

Need	Drill-down file or command
Per-page totals + timing (events, network counts, errors by page)	.o11y/<run-id>/cdp/summary.json
All failed network requests in one place	.o11y/<run-id>/cdp/network/failed.jsonl
Full console exception payloads (stacktraces, etc.)	.o11y/<run-id>/cdp/console/exceptions.jsonl
Per-page slice (only events on page N)	.o11y/<run-id>/cdp/pages/<pid>/
Full reasoning text / untruncated tool outputs for a specific turn	trace.json (filter by turn === N)
Ad-hoc grouped query (e.g. top hosts, errors-by-page)	O11Y_ROOT=./autobrowse/traces/<task-name>/latest/.o11y node ${CLAUDE_SKILL_DIR}/../browser-trace/scripts/query.mjs <run-id> <cmd>

The unified stream is the default; drill into structured files only when you need a grouped query, a full-text payload, or filtering the stream can't give you.

Form one hypothesis

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

Under --browser-trace, the hypothesis must cite a specific event from unified-events.jsonl (line number or timestamp) — or name the drill-down file if you had to descend into one. This keeps updates evidence-grounded rather than vibes-driven. A hypothesis based only on the agent's commands might say "the click didn't work"; grounded in the unified stream, it can say "line 47 of unified-events.jsonl: browse open was followed by Network.responseReceived status 403 on /api/checkout — switch to --verified --proxies."

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"
• (with --browser-trace) "At line 47 of unified-events.jsonl, 3 consecutive Network.responseReceived events on /api/availability returned 403 right after browse open — the site is fingerprinting; the next iter needs --verified --proxies."

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

Generate a runnable script (optional)

Once the task has converged, you can produce a deterministic, runnable script in one or more frameworks via scripts/codegen.mjs. This is one shot of an LLM call per framework, cached by content hash, with optional verify-against- fresh-session and rewrite-on-failure.

node ${CLAUDE_SKILL_DIR}/scripts/codegen.mjs \
  --task <name> \
  --workspace ./autobrowse \
  --frameworks playwright,stagehand \
  --verify

Each framework gets its own subdirectory under tasks/<name>/<framework>/ with the emitted script and a self-contained scaffold (package.json, tsconfig.json). The directory is runnable standalone with cd tasks/<name>/playwright && npm install && npx tsx <name>.ts — the only runtime requirement is BROWSERBASE_API_KEY (plus ANTHROPIC_API_KEY for the Stagehand target).

Builtin frameworks: playwright, stagehand. Add a custom framework with --prompt-template <path> --frameworks custom (and provide your own runner or pass --no-verify).

Common flags:

Flag	Purpose
--frameworks a,b,...	Comma-separated; default playwright
--verify / --no-verify	Run the produced script against a fresh BB session; default --verify
--max-retries N	Rewrite-on-verify-failure cap; default 2
--cache-only	Error if cache miss (CI-friendly)
--force	Bust the cache
--dry-run	Estimate prompt size + cost; don't call the LLM
--run <id>	Force a specific run-NNN (default: latest passing)

Output is one JSON line per framework on stdout. Non-zero exit if any selected framework's final state is passed: false.

See references/playwright-cdp-bridge.md for the canonical connectOverCDP patterns the emitted scripts follow.

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 open <url> --remote` — start a fresh Browserbase cloud session and navigate
- `browse open <url> --local` — start a clean local browser and navigate
- `browse tab new <url>` — open URL in a new tab
- `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]`)
  — click element by ref from the latest snapshot (include the brackets)

 Named sessions are a parallel-run workaround — they contaminate skills with infrastructure concerns. Skills must work in isolation with the default session.

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
• Don't release before bisecting — under --browser-trace, the order at the end of each iteration is non-negotiable: stop-capture → bisect-cdp → browse cloud sessions update REQUEST_RELEASE. Bisect depends on the session still existing when the trace stops.