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# Genesis AI Code Bench |
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**Developed by: Within Us AI** |
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Generated: 2026-01-01 |
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A lightweight evaluation harness for Genesis-style datasets that focuses on the signals |
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developers care about in practice: |
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- **Structure validity** (JSON parsing, required fields, schema consistency) |
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- **Tool-trace validity** (JSON array of tool calls with `tool` + `args`) |
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- **Diff validity** (`patch_diff` blocks contain recognizable unified-diff markers) |
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- **Self-grade validity** (score bounds, confidence bounds, presence of notes) |
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- **Governance presence** (audit/tests flags when expected) |
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- **Economics presence** (cost budgets + latency targets) |
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This bench is intentionally fast and offline-friendly. It does not execute repo tests; it |
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scores dataset quality and readiness for downstream training workflows. |
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## Quick start |
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```bash |
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python bench.py --jsonl path/to/train.jsonl --max_rows 5000 |
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``` |
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## Metrics produced |
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- `format_valid_rate` |
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- `required_fields_rate` |
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- `tool_trace_valid_rate` |
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- `patch_diff_valid_rate` |
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- `self_grade_valid_rate` |
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- `governance_present_rate` |
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- `economics_present_rate` |
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- `uniqueness_rate` (hash-based) |
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## Recommended use |
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- Run before upload to ensure Viewer-ready consistency |
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- Run after merges to confirm schema stability |
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- Compare v1.0 vs v1.1 addon impact |
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