{"data":{"kind":"file","path":"README.md","version_id":"qojmrn11hqemu8iol6wv853g","entry":{"name":"README.md","path":"README.md","is_directory":false,"size":1516,"modified_at":"2026-05-11T13:33:29.177000","content_hash":"5db1739ad376dc9ef1ce8154a5946d9e2455747b66172c824b57593d03fa797c"},"entries":[],"content":"# supply-chain-optimizer\n\n> Replace the placeholders below, then remove this callout.\n\n### Overview\n- **Environment ID**: `supply-chain-optimizer`\n- **Short description**: <one-sentence description>\n- **Tags**: <comma-separated tags>\n\n### Datasets\n- **Primary dataset(s)**: <name(s) and brief description>\n- **Source links**: <links>\n- **Split sizes**: <train/eval counts>\n\n### Task\n- **Type**: <single-turn | multi-turn | tool use>\n- **Output format expectations (optional)**: <e.g., plain text, XML tags, JSON schema>\n- **Rubric overview**: <briefly list reward functions and key metrics>\n\n### Quickstart\nRun an evaluation with default settings:\n\n```bash\nprime eval run supply-chain-optimizer\n```\n\nConfigure model and sampling:\n\n```bash\nprime eval run supply-chain-optimizer   -m openai/gpt-4.1-mini   -n 20 -r 3 -t 1024 -T 0.7   -a '{\"key\": \"value\"}'  # env-specific args as JSON\n```\n\nNotes:\n- Use `-a` / `--env-args` to pass environment-specific configuration as a JSON object.\n\n### Environment Arguments\nDocument any supported environment arguments and their meaning. Example:\n\n| Arg | Type | Default | Description |\n| --- | ---- | ------- | ----------- |\n| `foo` | str | `\"bar\"` | What this controls |\n| `max_examples` | int | `-1` | Limit on dataset size (use -1 for all) |\n\n### Metrics\nSummarize key metrics your rubric emits and how they’re interpreted.\n\n| Metric | Meaning |\n| ------ | ------- |\n| `reward` | Main scalar reward (weighted sum of criteria) |\n| `accuracy` | Exact match on target answer |\n\n","encoding":"utf-8","truncated":false,"total_bytes":1516},"status":null}