# Offline quick start [中文版](quickstart.zh-CN.md) · [Main README](../README.md) This guide runs the two deterministic reference scripts in the 0.5.0 release. It uses synthetic data, the Python standard library, and no network or model service. ## Requirements - Python 3.9 or later. - Either the repository checkout or the extracted `loyaltyops-bench-0.5.0/` trial archive. - A new output directory for each run. The builder writes the archive, SHA256 sidecar, and build summary to its selected output directory. After obtaining a reviewed archive, verify the supplied digest and use the commands below from the extracted root. ## Create a local environment and inspect data From the extracted package root (or source root), on macOS/Linux: ```sh python3 -m venv --without-pip .venv .venv/bin/python --version ``` No activation, dependency download, or installation is needed. On Windows use `.venv\Scripts\python.exe` instead of `.venv/bin/python`. If your Python lacks `venv`, use a local Python installation with that standard module available; the guide does not download it. Read the [fixed data guide](data-guide.en.md): the two tasks share member/order inputs but start with different campaign and approval records. The JSON exports are maintainer inspection material, not a new runtime input or an answer to feed to an agent. ## Run both tasks in English ```sh .venv/bin/python scripts/run_demo.py \ --task draft_for_approval \ --agent reference \ --task-language en \ --report-language en \ --output ./results/draft-en .venv/bin/python scripts/run_demo.py \ --task execute_approved \ --agent reference \ --task-language en \ --report-language en \ --output ./results/execute-en ``` The first task should create the correct synthetic campaign, submit one approval request, and stop. The second should verify the existing approval and perform the simulated coupon issuance and message send. Each output directory contains: - `__reference.json`: structured source evidence, including state, trace, score, language metadata, and explanation fields. - `__reference.md`: the reader-facing report in the selected report language. Without `--dataset`, both commands use `baseline`. Check `dataset_id` and `data_version` in the JSON before comparing results. ## Select another fixed data instance There is one business template, two task states and three data instances. `partial_refund` changes one full refund into a partial refund with positive net payment. `contact_kinds` separates service contacts from marketing contacts. Policy is unchanged: coupon value is 1000 cents per eligible member and 3000 cents is a ceiling, not a spending target. Run both tasks on an additional instance: ```sh .venv/bin/python scripts/run_demo.py --dataset partial_refund \ --task draft_for_approval --agent reference --task-language en \ --output ./results/partial-draft_for_approval-en .venv/bin/python scripts/run_demo.py --dataset partial_refund \ --task execute_approved --agent reference --task-language en \ --output ./results/partial-execute_approved-en ``` Repeat with `--dataset contact_kinds` and new output directories to inspect the contact variant. Baseline filenames remain `__.json/.md`; other instances use `____.json/.md`. To inspect a targeted fault, run: ```sh .venv/bin/python scripts/run_demo.py --dataset partial_refund \ --task draft_for_approval --agent ignore_partial_refund --task-language en \ --output ./results/partial_refund-fault-en .venv/bin/python scripts/run_demo.py --dataset contact_kinds \ --task draft_for_approval --agent service_as_marketing --task-language en \ --output ./results/contact_kinds-fault-en ``` Both fault commands should exit 1. `ignore_partial_refund` wrongly ignores every refunded order; `service_as_marketing` wrongly treats service contacts as marketing. Inspect `/evaluation/findings`, `/trace`, `/snapshot` and `/failure_explanation`; do not infer the cause from exit code alone. A faulty script passing on baseline does not prove it handles variants correctly. See the [first-use exercise](user-trial/task.en.md) and [version notes](user-trial/versions.en.md). Integration review in this exercise is read-only; it does not require a model service or a model plan/preflight command. ## Confirm Chinese compatibility The default task language remains `zh-CN`, and the report follows the task language unless overridden: ```sh .venv/bin/python scripts/run_demo.py \ --task draft_for_approval \ --agent reference \ --output ./results/draft-default-zh ``` Use separate output directories when comparing languages. The language switch changes localized task and report text; it does not change field names, rule values, synthetic state, allowed actions, or scoring. ## Inspect a failure explanation ```sh .venv/bin/python scripts/run_demo.py \ --task execute_approved \ --agent always_refuse \ --task-language en \ --report-language en \ --output ./results/execute-refusal-en ``` This is an intentional engineering failure: an already approved task remains incomplete. Exit code 1 is expected. In JSON, check `/failure_explanation/observations`, `/evaluation/business_complete`, `/snapshot/campaigns`, `/snapshot/sends`, and `/snapshot/coupons`: the approved campaign is unexecuted and no sends or coupons were created. Read the Markdown explanation, then inspect the same-stem JSON for the recorded actions and scoring source. See the [report guide](report-guide.en.md). ## Inspect the public agent input ```sh .venv/bin/python scripts/run_demo.py \ --task draft_for_approval \ --task-language en \ --describe-task ``` The agent-facing specification contains the task, policy, role, and tool schemas. It must not contain the maintainer answer, scorer state, or report explanation. ## Reading the result A passing reference script confirms the local benchmark loop for that fixture. It does not establish model quality or production safety. A failing fixture may be intentionally checking a business or infrastructure condition. Always read `outcome`, `failure_categories`, the explanation, the trace, and `verified / failed / not_run / uncertain` together.