run-benchmark

name	run-benchmark
description	Run the WebBench browser agent benchmark — main entry point and orchestrator. Triggers: "run benchmark", "run WebBench", "start benchmark", "benchmark browser agent", "web bench", "execute WebBench", "run web-bench". Parses the requested configuration (category filter, sample size, resume), delegates to load-dataset, execute-task, evaluate-task, and generate-report skills. This is the interactive orchestrator that ties the full benchmark pipeline together.
allowed-tools	Read Write Edit Glob Grep Bash Task

Run WebBench Benchmark

Main entry point for running the WebBench browser agent benchmark. This skill is used in interactive (single-session) mode. For multi-session workflow execution, see the system prompt.

Input

Parse configuration from: $ARGUMENTS

Supported flags:

Flag	Description	Default
--category <CAT>	Filter tasks by category (READ, CREATE, UPDATE, DELETE, FILE_MANIPULATION)	All categories
--sample <N>	Random sample of N tasks (deterministic seed=42)	Full dataset
--resume	Resume from existing web-bench-results.jsonl, skip completed task IDs	Fresh run
--report-only	Skip execution, just generate report from existing results	Full run

Examples:

• run-benchmark --category READ --sample 50 — 50 random READ tasks
• run-benchmark --resume — continue from where last run stopped
• run-benchmark --report-only — just aggregate existing results

Interactive Execution Protocol

When run interactively (not via the workflow loop), this skill executes the full pipeline in a single session:

1. Setup

1. Parse arguments
2. Check for existing state (web-bench-tasks.jsonl, web-bench-results.jsonl)
3. If --resume and results exist: determine completed task IDs, skip them
4. If not resuming: load the load-dataset skill to download and prepare the dataset
5. Report configuration and task count

2. Execute Tasks

For each task in web-bench-tasks.jsonl (skipping completed if resuming):

1. Read the task line
2. Record start time: date +%s%3N
3. Load execute-task methodology and have the browser-capable calling context perform the browser automation
4. Load evaluate-task methodology and score the result
5. Record end time: date +%s%3N, compute duration
6. Append result to web-bench-results.jsonl:

   {"id": 42, "url": "...", "category": "READ", "task": "...", "score": 1.0, "verdict": "PASS", "reasoning": "...", "error": null, "duration_ms": 34200, "tokens_used": {"input": 12450, "output": 3200}, "timestamp": "2026-03-19T14:30:00Z"}
   

7. Print progress: [42/2454] PASS (1.0) — acehardware.com — READ — 34.2s

3. Generate Report

After all tasks are processed (or if --report-only):

1. Load generate-report methodology
2. Aggregate web-bench-results.jsonl into web-bench-report.md
3. Print summary statistics to console

Token Tracking

Token usage should be tracked per task. The agent should estimate tokens consumed during task execution by recording:

• Input tokens: Approximate from the size of prompts, page snapshots, and tool responses received during execution
• Output tokens: Approximate from the size of responses and tool calls generated

If exact token counts are available from the session metadata, prefer those over estimates.

Progress Display

After each task, print a status line:

[1/50] PASS  (1.0)  acehardware.com          READ    34.2s   15,650 tokens
[2/50] FAIL  (0.0)  airbnb.com               CREATE  12.1s    8,200 tokens  [auth_required]
[3/50] PARTIAL(0.5) amazon.com               READ    45.8s   22,100 tokens

Guardrails

• Always append, never overwrite results. The JSONL file is append-only.
• Respect the dataset. Do not modify task descriptions or skip tasks without recording a FAIL.