chinatravel-agent-env

name	chinatravel-agent-env
description	Use when solving ChinaTravel benchmark queries with the local agent_env CLI, including loading benchmark queries, inspecting attractions/restaurants/hotels/transport, producing itinerary JSON, and evaluating one solved query.

ChinaTravel Agent Environment

Use this skill when a task asks you to solve or inspect ChinaTravel benchmark queries with the local agent_env command-line interface.

Core Rule

Use the structured CLI tools first. Use raw world commands only when the structured tool catalog does not cover the lookup.

Run commands from the repository root:

python -m agent_env.cli tools
python -m agent_env.cli splits
python -m agent_env.cli call <tool_name> '<json_arguments>'
python -m agent_env.cli world "<WorldEnv command>"

The CLI returns JSON. Check success before relying on data.

Common Lookups

List query splits:

python -m agent_env.cli call china_travel_list_splits

Load query metadata:

python -m agent_env.cli call china_travel_load_query '{"split":"easy"}'
python -m agent_env.cli call china_travel_load_query '{"split":"easy","uid":"<uid>"}'

Inspect available columns:

python -m agent_env.cli call attractions_keys '{"city":"上海"}'
python -m agent_env.cli call restaurants_keys '{"city":"上海"}'
python -m agent_env.cli call accommodations_keys '{"city":"上海"}'

Filter resources:

python -m agent_env.cli call attractions_select '{"city":"上海","key":"name","op":"contains","value":"博物馆"}'
python -m agent_env.cli call restaurants_select '{"city":"上海","key":"cuisine","op":"eq","value":"本帮江浙菜"}'
python -m agent_env.cli call accommodations_select '{"city":"上海","key":"price","op":"le","value":500}'

Find nearby resources:

python -m agent_env.cli call attractions_nearby '{"city":"上海","point":"上海迪士尼度假区","topk":5,"dist":5}'
python -m agent_env.cli call restaurants_nearby '{"city":"上海","point":"上海迪士尼度假区","topk":5,"dist":2}'
python -m agent_env.cli call accommodations_nearby '{"city":"上海","point":"上海迪士尼度假区","topk":5,"dist":5}'

Check transport:

python -m agent_env.cli call intercity_transport_select '{"start_city":"北京","end_city":"上海","intercity_type":"train","earliest_leave_time":"07:00"}'
python -m agent_env.cli call goto '{"city":"上海","start":"上海站","end":"上海迪士尼度假区","start_time":"09:00","transport_type":"metro"}'

Use the exact values returned by the tools for prices, IDs, names, times, distances, and transport segments. Do not invent POI names or transport details.

Output Contract

Return only a JSON itinerary matching chinatravel/evaluation/output_schema.json. The top-level object must include:

• people_number
• start_city
• target_city
• itinerary

Each activity must include:

• type
• start_time
• end_time
• price
• cost
• transports

Intercity activities also need start, end, tickets, and TrainID or FlightID. Attraction activities need position and tickets. Accommodation activities need position, room_type, and rooms.

Split Automation

Use the bundled harness to load a configured split, call a harness non-interactively, save plans under results/<method>/<uid>.json, and evaluate them:

python agent_env/scripts/solve_script_with_harness.py --split easy

Useful options:

python agent_env/scripts/solve_script_with_harness.py --split easy --uid <uid>
python agent_env/scripts/solve_script_with_harness.py --split easy --harness opencode --model dashscope/qwen3.6-27b
python agent_env/scripts/solve_script_with_harness.py --split easy --harness codex --model gpt-5.5
python agent_env/scripts/solve_script_with_harness.py --split easy --timeout 900
python agent_env/scripts/solve_script_with_harness.py --split easy --limit 1
python agent_env/scripts/solve_script_with_harness.py --split easy --no-run-harness

The harness hides oracle verifier fields from the selected model, but keeps them internally for hard-constraint evaluation. Result directories default to <model>-<split>-<harness> unless a method override is supplied.