Update README

README-ZH.md

@@ -43,6 +43,7 @@
 ```text
 OracleProto/
 ├── forecast_eval_set_example.db   # SQLite 数据库文件（数据集本体；约 52 KB）
+├── forecast_eval_set_example.csv  # 行表的 CSV 导出；80 行 + 表头（约 18 KB）
 ├── README.md                      # 本文件
 ├── LICENSE                        # MIT
 └── .gitattributes                 # HF 标准二进制属性
@@ -50,6 +51,8 @@ OracleProto/
 
 数据集以单个 SQLite 文件（而非 Parquet 或 JSONL）发布，因为提示重建配方与逐行 provenance 与题目行同住一个文件中（位于 `dataset_metadata.features_json`）。`datasets.Dataset` 的 loader 与 Parquet 转换示例见 §6。
 
+CSV 是 `forecast_eval_set_example` 行表的导出，不含 `dataset_metadata`，因此提示模板仅能从 SQLite 文件中获取。下游流水线只需要这 80 行（pandas、电子表格、`grep`）并自行重建提示时使用 CSV。`options` 列保留为 JSON 编码的数组字符串，按 RFC 4180 转义。
+
 ---
 
 ## 3. 数据库 schema
@@ -350,6 +353,21 @@ def render_prompt(row, meta):
 
 完整参考 renderer（含 >26 选项的反引号规则与可选的 reflection / belief-elicitation 尾部）位于 [`forecast_eval/prompts.py`](https://github.com/MaYiding/OracleProto/blob/main/forecast_eval/prompts.py)；复用它即可获得 byte-identical 提示。
 
+### 6.5 使用预生成的 CSV（标准库 `csv`，不含提示模板）
+
+```python
+import csv, json
+
+with open("forecast_eval_set_example.csv", encoding="utf-8", newline="") as f:
+    rows = [
+        {**r, "options": json.loads(r["options"])}
+        for r in csv.DictReader(f)
+    ]
+print(f"loaded {len(rows)} rows; first event: {rows[0]['event']!r}")
+```
+
+CSV 路径完全绕过 `dataset_metadata`。要把行与提示模板配对，要么按 §5 手工渲染，要么回到 §6.1 的 SQLite 路径。
+
 ---
 
 ## 7. 推荐评估协议

README.md

@@ -64,6 +64,7 @@ A SQLite-packaged evaluation set of 80 hand-curated forecasting questions on rea
 ```text
 OracleProto/
 ├── forecast_eval_set_example.db   # SQLite database file (the dataset; ~52 KB)
+├── forecast_eval_set_example.csv  # CSV export of the rows table; 80 rows + header (~18 KB)
 ├── README.md                      # this file
 ├── LICENSE                        # MIT
 └── .gitattributes                 # standard HF binary attributes
@@ -71,6 +72,8 @@ OracleProto/
 
 The dataset ships as one SQLite file, not Parquet or JSONL, because the prompt-reconstruction recipe and per-row provenance live in the same file as the rows (in `dataset_metadata.features_json`). A loader for `datasets.Dataset` and Parquet conversion appears in §6.
 
+The CSV is a row-table export of `forecast_eval_set_example`; it does not carry `dataset_metadata`, so the prompt template is reachable only via the SQLite file. Use the CSV when a downstream pipeline needs only the 80 rows (pandas, spreadsheet, `grep`) and reconstructs prompts on its own. The `options` column is preserved as a JSON-encoded array string, escaped per RFC 4180.
+
 ---
 
 ## 3. Database schema
@@ -371,6 +374,21 @@ def render_prompt(row, meta):
 
 The full reference renderer (with the >26-option backtick rule and an optional reflection / belief-elicitation tail) lives at [`forecast_eval/prompts.py`](https://github.com/MaYiding/OracleProto/blob/main/forecast_eval/prompts.py); reusing it gives byte-identical prompts.
 
+### 6.5 With the prebuilt CSV (stdlib `csv`, no prompt template)
+
+```python
+import csv, json
+
+with open("forecast_eval_set_example.csv", encoding="utf-8", newline="") as f:
+    rows = [
+        {**r, "options": json.loads(r["options"])}
+        for r in csv.DictReader(f)
+    ]
+print(f"loaded {len(rows)} rows; first event: {rows[0]['event']!r}")
+```
+
+The CSV path skips `dataset_metadata` entirely. To pair the rows with the prompt template, either render by hand from §5 or fall back to the SQLite path in §6.1.
+
 ---
 
 ## 7. Recommended evaluation protocol