bench-fixture-gen (benchmark-internal, NOT a user skill)

Generates the shared per-task fixture that every competing arm starts from: a realistic dataset plus an image asset base. Fairness rule: fixtures are generated ONCE per task by this preprocessor and sealed (sha256) before any arm runs; arms never generate their own.

Workflow

1. Author a fixture spec (fixture-spec.json):
   • task_id
   • dataset: { global_key (window global), entities: [{ name, count, fields: [{name, kind, enum?, range?}], relations? }], disclosure }
   • images: generate-task-assets item list ({file, prompt, style})
2. Dataset: node scripts/generate-task-dataset.mjs --spec <spec> --out <dir>
   • grok-4.6 authors realistic records for the spec (headless, isolated); the script VALIDATES structure (entity counts, field presence, enum membership, relation integrity), retries once on failure, then writes data.json + data.js (window global) + prints aggregate seeds.
3. Images: node scripts/generate-task-assets.mjs --spec <images-spec> --out <assets-dir>
   • grok image_gen, paced, self-healing auth sync, per-file sha manifest.
4. Seal: record data.json sha + assets-manifest in the task fixture and in every prepared cell's cell.json.

Rules

• Deterministic where possible; all sample data labeled fictional in a disclosure string carried inside the dataset itself.
• Never real brands/people; Korean-locale tasks use Korean-plausible fictional names.
• Aggregate expectations for data-fidelity audits are DERIVED from the sealed dataset at audit time, never hardcoded.
• KPI terms used in briefs must be DEFINED in the brief (lesson: the "open cases" ambiguity in wholesale-2026-08-18).