Browser Bench Task

Browser benchmark task author

Why this skill exists

You are building tasks for data/real_world_bench.json — a browser-agent benchmark where each task is graded checkpoint-by-checkpoint (per-rubric). A good task is real-world, completable, and free of any risk-control block the autonomous run can't get past: an agent driving ego-browser must be able to actually finish it, and a grader must be able to verify each checkpoint from screenshots, trajectory, or final output.

The one rule that matters most, and the reason this skill exists:

Never write a task you have not run through ego-browser first. Web research and model priors lie about bot-friendliness — sites that look "low risk" on paper hard-block real automation (Product Hunt → Cloudflare "Just a moment"; Etsy → DataDome

• forced login at checkout). The only proof is a live ego-browser probe. Then calibrate the rubrics to what you actually observed, not to what you imagined.

This operationalizes the repository's core authoring rule: calibrate every task from a real ego-browser run.

The loop

Do one task at a time. Read the referenced file the first time you need its detail.

1. Load context. Read data/real_world_bench.json (tail for the live schema; scan all website + task_id values to avoid duplicating a site or play you already have). Skim references/schema.md for the schema and references/authoring.md for rubric discipline. Recall the bot-friendliness memory if present.

2. Pick a candidate. Honor the user's constraints (US mainstream site, required level, cross-site wanted?, a mandated site such as reddit?). Avoid sites already in the dataset unless the play is genuinely new. To cover breadth fast you may fan out research with a Workflow (parallel web-search lanes → shortlist ranked by bot-risk), but research only prioritizes probe order — it never replaces the probe. → references/site-selection.md

3. Probe with ego-browser — this is the gate. Drive the EXACT interactions the task will need: reach the start page, confirm the autonomous run won't be walled off, apply every filter/sort/form step, and confirm the target data is actually extractable. Capture the real trajectory and concrete values. An automated hard wall (Cloudflare / DataDome / PerimeterX that won't clear) or an unreachable key interaction → drop it and pick another, do not write the task. A human-verification / captcha is the one carve-out: pause and hand off to the user to solve it once — if it then clears and does not return on repeated probing, treat it as a one-time trust gate (not a per-request wall) and the site is usable; if the SAME site re-challenges after that solve, skip it — a per-entry captcha can't be driven by the agent at run time. Probes share one real browser, so run them sequentially, never in parallel. → references/ego-browser-testing.md

4. Design + write confirmed_task. Fit a proven, gradeable pattern (aggregate-and-compute / form-fill-then-stop / calculator / cross-site or cross-app), with concrete filters, fields, computations, and stop-boundaries. Write it in natural user voice — first person, the motivation woven in, like the odysseys medium/hard tasks. No operator meta-instructions ("read-only", "don't log in", "must use old.reddit.com") — a real user would not say those; put any such rationale in the note field. → references/authoring.md

5. Write rubrics (3–6). Each rubric = one independently verifiable checkpoint; verification is state-based (what a grader sees in a screenshot / final output, not an action sequence); operation rubrics must be "applied AND confirmed"; all of it calibrated to your real run. → references/authoring.md

6. Append + validate. Append the object surgically (do not reformat or rewrite existing entries — keep the diff to an append). Then validate:

   uv run python -c "import json; json.load(open('data/real_world_bench.json')); print('json ok')"
   uv run python -c "from ego_bench.datasets import _ADAPTERS; ts=_ADAPTERS['real-world-bench']().load(); t=[x for x in ts if x.task_id==NEW_ID][0]; print(len(ts), 'rubrics:', len(t.metadata['rubrics']))"
   

   Missing/empty rubrics fail-loud in Stage B, so confirm the new entry carries them.

7. Capture learnings. Update the bot-friendliness memory with any new pass/fail site and its evidence.

Definition of done

• Ran end-to-end through ego-browser — no automated hard wall, and any human-verification cleared on a single handoff and did not recur; key interactions reached; data extracted.
• confirmed_task reads like a real user request — first person, motivated, zero operator meta-instructions.
• 3–6 rubrics, each one verifiable checkpoint, calibrated to the real run (not invented).
• JSON valid, adapter loads, new entry has rubrics.
• level / site_mode / site_count / categories / reference_length filled; note carries operator hints + the calibration record.

Reference files

• references/schema.md — the Odysseys schema fields, semantics, and a worked JSON example.
• references/ego-browser-testing.md — the gate: risk-control signals, copy-paste probe recipes, special cases (React inputs, cross-origin editors, captcha handoff), and the known bot-friendly / bot-hostile site table.
• references/authoring.md — the proven task patterns, the natural-phrasing rules (with before/after), rubric discipline with examples, and the anti-patterns to avoid.