- [P20260217-040144] 2026-02-17 04:01:44 +0800
  - summary: CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary: --- ## TASK 1: Execute D…
  - prompt:
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CONTEXT TRANSFER: We are continuing a conversation that had gotten too long. Here is a summary:

---
## TASK 1: Execute Dataflow Structure Analysis (数据流结构分析)

**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 a two-phase dataflow structure analysis for the feiqiu (飞球) connector:

**Phase 1 - Data Collection**: Ran `analyze_dataflow.py` to collect API JSON samples, DB schemas, field mappings, and BD descriptions for all 23 ODS tables.

**Phase 2 - Report Generation**: Ran `gen_dataflow_report.py` to generate a comprehensive Markdown report with enhanced content (field diffs, coverage stats, business descriptions, sample values, cross-layer anchor links).

**Key challenge**: The shell's cwd was stuck at `C:\NeoZQYY\apps\etl\pipelines\feiqiu` and couldn't be changed (cd is forbidden). Both scripts require running from project root `C:\NeoZQYY` because they use relative paths for `.env` files and `TABLE_MAP` source parsing.

**Solution**: Created wrapper scripts (`scripts/ops/_run_dataflow.py` and `scripts/ops/_run_report.py`) that use `os.chdir(root)` to set the correct working directory before importing and calling the main functions.

**Results**:
- Phase 1: 23 tables all successful, 3405 total records collected
- Phase 2: Report generated at `export/dataflow_analysis/dataflow_2026-02-17_040043.md` (576.6 KB)
- All intermediate data landed in `export/dataflow_analysis/` subdirectories: `json_trees/`, `db_schemas/`, `field_mappings/`, `bd_descriptions/`, `collection_manifest.json`

**FILEPATHS**:
- `scripts/ops/analyze_dataflow.py` — CLI entry point for data collection
- `scripts/ops/dataflow_analyzer.py` — Core collection module (flatten_json_tree, collect_all_tables, dump_collection_results, build_field_mappings, parse_table_map, parse_fact_mappings)
- `scripts/ops/gen_dataflow_report.py` — Markdown report generator
- `scripts/ops/_run_dataflow.py` — Wrapper script for Phase 1 (created to solve cwd issue)
- `scripts/ops/_run_report.py` — Wrapper script for Phase 2 (created to solve cwd issue)
- `export/dataflow_analysis/dataflow_2026-02-17_040043.md` — Latest generated report
- `export/dataflow_analysis/collection_manifest.json` — Collection manifest

**NOTE**: The task is complete. The two temporary wrapper scripts (`_run_dataflow.py`, `_run_report.py`) were created as utility helpers and remain in the repo. No high-risk paths were modified, so no audit is required.

## CONTEXT: ODS Dedup Standardize Spec (Background)

**STATUS**: not directly relevant to current task, but provides important context

**DETAILS**: The workspace has an existing spec at `.kiro/specs/ods-dedup-standardize/` with tasks mostly completed (tasks 1-7 done, task 8 in-progress at subtasks 8.4-8.5, task 9 queued). This spec covers ODS dedup and soft-delete standardization. The dataflow analysis task was independent of this spec work.

**USER CORRECTIONS AND INSTRUCTIONS**:
- Shell cwd is `C:\NeoZQYY\apps\etl\pipelines\feiqiu` — cannot use `cd` command, must use absolute paths or wrapper scripts
- Scripts that need project root context should use `os.chdir()` in a wrapper
- Follow `tech.md` script execution rules: prefer Python scripts over complex PowerShell
- All output in simplified Chinese per `language-zh.md`
- This was a read-only analysis task (no code changes to high-risk paths), so no `/audit` needed

## Files to read
- `scripts/ops/_run_dataflow.py`
- `scripts/ops/_run_report.py`
- `scripts/ops/analyze_dataflow.py`
- `scripts/ops/gen_dataflow_report.py`
- `export/dataflow_analysis/collection_manifest.json`

USER QUERIES(most recent first):
1. <source-event>
    The user manually invoked this action
    The user is focued on the following file: No file focused
    The user has the following paths open: \apps\etl\pipelines\feiqiu\config\env_parser.py, \apps\etl\pipelines\feiqiu\config\env_parser.py
</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
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）

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：
   - 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
You are working on the implementation plan. 
Ensure each task is actionable, references specific requirements, and focuses only on coding activities. 
Once approved, inform the user that the spec is complete and they can begin implementing the tasks by opening the tasks.md file.</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
```