- [P20260220-002416] 2026-02-20 00:24:16 +0800
  - summary: CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary: --- ## TASK 1: Execute D…
  - prompt:
```text
CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary:

---
## TASK 1: Execute Dataflow Structure Analysis (Two-Phase Pipeline)

**STATUS**: done

**USER QUERIES**: User query 1 ("执行数据流结构分析，按以下步骤完成")

**DETAILS**:
The user requested running the full dataflow structure analysis pipeline for the feiqiu (飞球) connector. This involved two existing scripts that were already fully implemented.

**Phase 1 — Data Collection** (`analyze_dataflow.py`):
- Successfully ran `C:\ProgramData\miniconda3\python.exe C:\NeoZQYY\scripts\ops\analyze_dataflow.py`
- Had shell working directory issues (kept defaulting to `C:\NeoZQYY\apps\etl\connectors\feiqiu`), resolved by using absolute paths for both Python and script
- Script performed 3 rounds of adaptive date expansion (30→60→90 days) for 11 tables with insufficient records
- Final date range: 2025-11-22 ~ 2026-02-20
- Results: 23 tables, all successful, 3395 total records
- All collection artifacts confirmed on disk:
  - `json_trees/` — 23 JSON structure trees (with multi-sample values)
  - `db_schemas/` — 85 ODS/DWD table structure JSONs (23 ODS + 62 DWD)
  - `field_mappings/` — 23 three-layer mapping files
  - `bd_descriptions/` — 23 business description files
  - `collection_manifest.json` — with json_field_count, date_from, date_to

**Phase 2 — Report Generation** (`gen_dataflow_report.py`):
- Successfully ran `C:\ProgramData\miniconda3\python.exe C:\NeoZQYY\scripts\ops\gen_dataflow_report.py`
- Output: `export/SYSTEM/REPORTS/dataflow_analysis/dataflow_2026-02-20_002258.md` (568.6 KB)
- Report confirmed to contain all required enhanced content:
  - Report header with API date range and JSON data volume
  - Overview table with API JSON field count column (totals: 1067 JSON fields, 980 ODS columns, 892 DWD columns)
  - Section 1.1: API↔ODS↔DWD field comparison diff report with per-table detail and anchor links
  - Coverage tables with business description column
  - API source field tables with business descriptions + multi-sample values
  - ODS/DWD table structures with bidirectional mapping anchor links

**Notable findings from the report**:
- `settlement_ticket_details` has 0 sample records (fetched per-orderSettleId, not via standard pagination)
- `goods_stock_summary` and `goods_stock_movements` have no DWD target tables (C-class, pending creation)
- No code changes were made — this was purely a script execution task

**FILEPATHS**:
- `scripts/ops/analyze_dataflow.py` — Phase 1 CLI entry
- `scripts/ops/gen_dataflow_report.py` — Phase 2 report generator
- `scripts/ops/dataflow_analyzer.py` — Core collection module
- `scripts/ops/_env_paths.py` — Shared path utility
- `export/SYSTEM/REPORTS/dataflow_analysis/collection_manifest.json` — Collection manifest
- `export/SYSTEM/REPORTS/dataflow_analysis/dataflow_2026-02-20_002258.md` — Generated report

---

## PROJECT CONTEXT (for new agent orientation)

**What this project is**: NeoZQYY Monorepo — a full-stack data platform for billiard hall operations. ETL Connector extracts from upstream SaaS API through ODS → DWD → DWS layers. Includes FastAPI backend, WeChat mini-program, admin web console.

**Key infrastructure**:
- Python 3.10+, uv workspace, PostgreSQL (4 databases: etl_feiqiu, test_etl_feiqiu, zqyy_app, test_zqyy_app)
- All output paths via `.env` environment variables → `export/` directory tree
- Scripts in `scripts/ops/` use `_env_paths.get_output_path()` for path resolution
- Shell quirk: PowerShell working directory often stuck at `C:\NeoZQYY\apps\etl\connectors\feiqiu`; use absolute paths for Python executable and script paths

**Existing specs**: `.kiro/specs/dataflow-structure-audit/` has completed requirements.md, design.md, tasks.md (all tasks marked done)

**Related reports in workspace**:
- `export/SYSTEM/REPORTS/field_audit/field_investigation_report.md` — Deep field investigation (identifies 31 fields needing DWD additions)
- `export/SYSTEM/REPORTS/field_audit/field_review_for_user.md` — User review document for field completion
- `docs/deployment/LAUNCH-CHECKLIST.md` — WeChat mini-program launch checklist

---

## USER CORRECTIONS AND INSTRUCTIONS
- All responses must be in simplified Chinese (language-zh.md steering)
- All file output must go through `export/` directory via `.env` variables (export-paths.md steering)
- Use Python scripts for complex operations, not PowerShell (tech.md steering)
- High-risk path changes require audit (governance.md steering)
- Currently only analyzing feiqiu connector; future connectors should be auto-discovered

---

## Files to read
- `export/SYSTEM/REPORTS/dataflow_analysis/collection_manifest.json`
- `export/SYSTEM/REPORTS/dataflow_analysis/dataflow_2026-02-20_002258.md`
- `.kiro/specs/dataflow-structure-audit/requirements.md`
- `.env`

USER QUERIES(most recent first):
1. <source-event>
    The user manually invoked this action
    The user is focued on the following file: export/SYSTEM/REPORTS/field_audit/field_review_for_user.md
    The user has the following paths open: export/SYSTEM/REPORTS/field_audit/field_review_for_user.md
</source-event>

执行数据流结构分析，按以下步骤完成：

第一阶段：数据采集
1. 运行 `python scripts/ops/analyze_dataflow.py` 完成数据采集（如需指定日期范围，加 --date-from / --date-to 参数）
2. 确认采集结果已落盘，包括：
   - json_trees/（含 samples 多示例值）
   - db_schemas/
   - field_mappings/（三层映射 + 锚点）
   - bd_descriptions/（BD_manual 业务描述）
   - collection_manifest.json（含 json_field_count、date_from、date_to）

第二阶段：报告生成
3. 运行 `python scripts/ops/gen_dataflow_report.py` 生成 Markdown 报告
4. 报告包含以下增强内容：
   - 报告头含 API 请求日期范围（date_from ~ date_to）和 JSON 数据总量
   - 总览表含 API JSON 字段数列
   - 1.1 API↔ODS↔DWD 字段对比差异报告
   - 2.3 覆盖率表含业务描述列
   - API 源字段表含业务描述列 + 多示例值（枚举值解释）
   - ODS 表结构含业务描述列 + 上下游双向映射锚点链接
   - DWD 表结构含业务描述列 + ODS 来源锚点链接
5. 输出文件路径和关键统计摘要

注意：当前仅分析飞球（feiqiu）连接器。未来新增连接器时，应自动发现并纳入分析范围。
2. <implicit-rules>## Implicit Rules
Focus on creating a new spec file or identifying an existing spec to update. 
If starting a new spec, create a requirements.md file in the .kiro/specs directory with clear user stories and acceptance criteria. 
If working with an existing spec, review the current requirements and suggest improvements if needed. 
Do not make direct code changes yet. First establish or review the spec file that will guide our implementation.</implicit-rules>
---

METADATA:
The previous conversation had 2 messages.

INSTRUCTIONS:
Continue working until the user query has been fully addressed. Do not ask for clarification - proceed with the work based on the context provided.
IMPORTANT: you need to read from the files to Read section
```
