lamda-nesy/chinatravel/agent-env

Claude Code and agent skill by lamda-nesy/chinatravel

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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.

Install

npx vskill@latest install lamda-nesy/chinatravel/agent-env

SKILL.md

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:

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

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

Common Lookups

List query splits:

bash
python -m agent_env.cli --lang en call china_travel_list_splits

Load query metadata:

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

Inspect available columns:

bash
python -m agent_env.cli --lang en call attractions_keys '{"city":"Shanghai"}'
python -m agent_env.cli --lang en call restaurants_keys '{"city":"Shanghai"}'
python -m agent_env.cli --lang en call accommodations_keys '{"city":"Shanghai"}'

Filter resources:

bash
python -m agent_env.cli --lang en call attractions_select '{"city":"Shanghai","key":"name","op":"contains","value":"Museum"}'
python -m agent_env.cli --lang en call restaurants_select '{"city":"Shanghai","key":"cuisine","op":"contains","value":"Chinese"}'
python -m agent_env.cli --lang en call accommodations_select '{"city":"Shanghai","key":"price","op":"le","value":500}'

Find nearby resources:

bash
python -m agent_env.cli --lang en call attractions_nearby '{"city":"Shanghai","point":"Shanghai Disneyland","topk":5,"dist":5}'
python -m agent_env.cli --lang en call restaurants_nearby '{"city":"Shanghai","point":"Shanghai Disneyland","topk":5,"dist":2}'
python -m agent_env.cli --lang en call accommodations_nearby '{"city":"Shanghai","point":"Shanghai Disneyland","topk":5,"dist":5}'

Check transport:

bash
python -m agent_env.cli --lang en call intercity_transport_select '{"start_city":"Beijing","end_city":"Shanghai","intercity_type":"train","earliest_leave_time":"07:00"}'
python -m agent_env.cli --lang en call goto '{"city":"Shanghai","start":"Shanghai Railway Station","end":"Shanghai Disneyland","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:

bash
python agent_env/scripts/solve_script_with_harness.py --lang en --split easy

Useful options:

bash
python agent_env/scripts/solve_script_with_harness.py --lang en --split easy --uid <uid>
python agent_env/scripts/solve_script_with_harness.py --lang en --split easy --harness opencode --model dashscope/qwen3.6-27b
python agent_env/scripts/solve_script_with_harness.py --lang en --split easy --harness codex --model gpt-5.5
python agent_env/scripts/solve_script_with_harness.py --lang en --split easy --timeout 900
python agent_env/scripts/solve_script_with_harness.py --lang en --split easy --limit 1
python agent_env/scripts/solve_script_with_harness.py --lang en --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.

Security

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Source

Popularity

Repository stars
116
Repository forks
20
7-day trend score
24

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