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