{
  "checker": {
    "claims": [
      {
        "claim": "The quickstart scores a new menu using the trained model.",
        "command": "<guest-path> runs/synthetic.pt --context 'Choose the exact badge amber badger. Badge: amber badger.' --option 'azure crane' --option 'amber badger' --option 'gold heron'",
        "evidence": "\"option\": \"amber badger\",\n    \"probability\": 1.0\n  },\n  {\n    \"option\": \"gold heron\",\n    \"probability\": 4.981465195896817e-08\n  }\n]\n",
        "expected": "amber badger has the highest probability",
        "input": "Choose the exact badge amber badger; options azure crane, amber badger, gold heron.",
        "kind": "claim",
        "outcome": "Real input produced the expected result: \"option\": \"amber badger\"",
        "returncode": 0,
        "source": "Run these commands from the repository root. They create local synthetic data, train on it, evaluate the saved model and score one new menu.",
        "source_checked": true,
        "source_file": "README.md",
        "verdict": "yes"
      }
    ],
    "cycle_checks": [
      {
        "claim": "Scheduled or looping main behavior",
        "command": "",
        "evidence": "Inspected README and package entry points: this package provides data generation, training, evaluation, and prediction commands; no product scheduler, webhook, or recurring cycle is documented.",
        "expected": "identify the product trigger",
        "input": "source inspection",
        "kind": "cycle",
        "outcome": "Inspected README and package entry points: this package provides data generation, training, evaluation, and prediction commands; no product scheduler, webhook, or recurring cycle is documented.",
        "source": "repository entry points",
        "verdict": "yes"
      }
    ],
    "details": {
      "argv": [
        "<guest-path>",
        "runs/synthetic.pt",
        "--context",
        "Choose the exact badge amber badger. Badge: amber badger.",
        "--option",
        "azure crane",
        "--option",
        "amber badger",
        "--option",
        "gold heron"
      ],
      "kind": "cli",
      "needs_browser": false,
      "needs_credentials": false,
      "returncode": 0,
      "scope": "product operation",
      "stderr": "",
      "stdout": "[\n  {\n    \"option\": \"azure crane\",\n    \"probability\": 1.0349754830940583e-08\n  },\n  {\n    \"option\": \"amber badger\",\n    \"probability\": 1.0\n  },\n  {\n    \"option\": \"gold heron\",\n    \"probability\": 4.981465195896817e-08\n[excerpt truncated]",
      "working": true
    },
    "evidence_strength": "readme_checked",
    "example_checks": [
      {
        "claim": "README Quickstart: generate data, train, evaluate and predict",
        "command": "/bin/bash -c 'source .venv/bin/activate && jevlike-eval runs/synthetic.pt data/synthetic/test.jsonl && jevlike-predict runs/synthetic.pt --context '\"'\"'Choose the exact badge amber badger. Badge: amber badger.'\"'\"' --option '\"'\"'azure crane'\"'\"' --option '\"'\"'amber badger'\"'\"' --option '\"'\"'gold heron'\"'\"''",
        "deferred": false,
        "evidence": "{\n  \"model\": {\n    \"top1\": 0.9975,\n    \"top3\": 1.0,\n    \"ece\": 0.004109145622351207,\n    \"examples\": 400\n  },\n  \"shuffled_context\": {\n    \"top1\": 0.2275,\n    \"top3\": 0.6825,\n    \"ece\": 0.6250184109667316,\n    \"examples\": 400\n  },\n  \"device\": \"cpu\"\n}\n[\n  {\n    \"option\": \"azure crane\",\n    \"probability\": 1.0349754830940583e-08\n  },\n  {\n    \"option\": \"amber badger\",\n    \"probability\": 1.0\n  },\n  {\n    \"option\": \"gold heron\",\n    \"probability\": 4.981465195896817e-08\n  }\n]",
        "expected": "evaluation and prediction results printed",
        "input": "Synthetic menus with amber badger choice",
        "kind": "example",
        "outcome": "command completed successfully",
        "returncode": 0,
        "source": "README.md / Quickstart",
        "verdict": "yes"
      }
    ],
    "status": "PASS"
  },
  "deviations": [
    "sudo apt-get install -y uv python3-venv python3-pip failed because Ubuntu could not locate package uv; pip package not present in system Python. Installed uv with curl -LsSf https://astral.sh/uv/install.sh | sh; subsequent documented operation succeeded in uv venv."
  ],
  "fault_owner": null,
  "main_blocker": null,
  "operation": "README synthetic text quickstart: data generation, training, evaluation and scoring a new menu",
  "operation_scope": "Python 3.12.3 with uv-managed .venv and CPU; editable install with dev extras",
  "outcome": "working",
  "plain_failure": null,
  "steps": [
    "Installed uv using the README-compatible Astral installer after apt had no uv package.",
    "uv venv",
    "uv pip install -e '.[dev]'",
    "jevlike-data synthetic --output data/synthetic",
    "jevlike-train data/synthetic/train.jsonl --validation data/synthetic/validation.jsonl --output runs/synthetic.pt",
    "jevlike-eval runs/synthetic.pt data/synthetic/test.jsonl",
    "jevlike-predict runs/synthetic.pt --context 'Choose the exact badge amber badger. Badge: amber badger.' --option 'azure crane' --option 'amber badger' --option 'gold heron'"
  ],
  "stuck_step": null,
  "suggested_fix": null
}