| name | glr-qa |
| description | Run goal-driven GameLearningRuntime QA against an authorized game, training adapter, replay, or live probe and produce a dated JSON plus self-contained HTML report. Use when a player asks whether a game works well, wants bug discovery, regression checks, or evidence from bounded training runs. |
| license | MIT |
GLR QA
Turn a plain-language objective into bounded, inspectable QA evidence. Preserve the
boundary between deterministic checks, scripted replay, training metrics, and
live-host acceptance; a passing smoke command is not proof that the whole game is
complete.
- Restate the goal and identify the authorized project/adapter and evidence scope.
- Choose finite checks (for example adapter doctor, deterministic regression,
replay, and an explicitly bounded training probe). Never invent credentials,
game internals, or unrestricted automation.
- Run the checks with
python -m game_learning_runtime.qa or call
game_learning_runtime.qa.run_qa. Use --project for the adapter working
directory and one or more --check NAME COMMAND... arguments.
- Inspect
result.json and open index.html from the generated
.glr-qa/YYYY-MM-DD/<time>/ directory. Report failures with their command
output, duration, and likely next investigation; report missing live evidence
as an evidence gap.
Example:
$env:PYTHONPATH = "src"
python -m game_learning_runtime.qa "inspect the whole game for bugs" `
--project . `
--check doctor glr --project . doctor `
--check regression python -m pytest tests/test_runtime_integration.py -q `
--check training python -m your_adapter.train --steps 1000
Do not claim release quality from this report alone. Keep proprietary traces and
secrets out of artifacts; publish only evidence the project owner authorized.
