- [P20260217-082050] 2026-02-17 08:20:50 +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 Data Flow Structure Analysis (数据流结构分析)

**STATUS**: in-progress

**USER QUERIES**: User's single request to execute a two-phase data flow analysis

**DETAILS**:
The user requested execution of a data flow structure analysis for the feiqiu (飞球) connector with two phases:

**Phase 1 - Data Collection**: Run `python scripts/ops/analyze_dataflow.py` to collect data including json_trees, db_schemas, field_mappings, bd_descriptions, and collection_manifest.json

**Phase 2 - Report Generation**: Run `python scripts/ops/gen_dataflow_report.py` to generate a Markdown report with enhanced content (field comparison diffs, coverage tables with business descriptions, multi-sample values, bidirectional anchor links)

**What was attempted**:
- The command `python scripts/ops/analyze_dataflow.py` was executed but returned Exit Code -1 with no meaningful output (just noise from previous pytest commands in the terminal buffer)
- No further debugging or retry was attempted before context limit was hit

**What was NOT done**:
- Phase 1 data collection did not complete successfully
- Phase 2 report generation was not attempted
- No investigation into why the script failed (could be missing .env config, API credentials, database connection, or other runtime issues)

**NEXT STEPS**:
1. Investigate why `python scripts/ops/analyze_dataflow.py` failed with exit code -1:
   - Check if `.env` file has required config (API credentials, database DSN, store_id)
   - Try running with `--help` flag first to understand CLI options
   - Check if the script needs specific environment variables set
   - Consider running with verbose/debug output to see the actual error
2. If the script requires database/API access that isn't available, check if there's a way to run with cached/existing data
3. Once Phase 1 succeeds, run `python scripts/ops/gen_dataflow_report.py` for Phase 2
4. Report output file path and key statistics summary

**FILEPATHS**:
- `scripts/ops/analyze_dataflow.py` - Core data collection module
- `scripts/ops/gen_dataflow_report.py` - Report generator
- `scripts/ops/dataflow_analyzer.py` - Analyzer module (imported by analyze_dataflow.py)
- `export/dataflow_analysis/` - Output directory for analysis results
- `.env` - Environment config (credentials, DSN)

**CONTEXT - Project Structure**:
- This is the NeoZQYY Monorepo - a billiard hall business data platform
- ETL pipeline at `apps/etl/pipelines/feiqiu/` processes 23 ODS tables from upstream SaaS API
- The dataflow analyzer collects JSON samples from API, queries PostgreSQL ODS/DWD table structures, builds three-layer field mappings
- Previous successful reports exist at `export/dataflow_analysis/dataflow_2026-02-17_015528.md` (4838 lines)
- The `ods-dedup-standardize` spec is fully completed (all tasks [x] checked) - this is background context, not the current task

**CONTEXT - Existing Specs (completed, for reference only)**:
- `.kiro/specs/ods-dedup-standardize/` - ODS dedup & soft-delete standardization (all 9 tasks completed)
- The ODS tasks use `SnapshotMode` enum, `skip_unchanged`, payload-based content_hash
- 23 ODS tables with `(business_pk, fetched_at DESC)` indexes added

**USER CORRECTIONS AND INSTRUCTIONS**:
- All output text must be in Simplified Chinese (语言规范)
- When modifying PostgreSQL schema, must sync docs/database/ with change notes, compatibility, rollback strategy, and 3+ verification SQLs
- Python scripts preferred over PowerShell for complex operations
- The analysis currently only covers the feiqiu connector; future connectors should be auto-discovered
- Audit required for high-risk path changes (governed by governance.md steering)

## Files to read
- `scripts/ops/analyze_dataflow.py` - The main script that needs to be executed (was truncated in context, need to see full CLI entry point)
- `scripts/ops/dataflow_analyzer.py` - Core module with `AnalyzerConfig`, `collect_all_tables`, `fetch_records` etc.
- `scripts/ops/gen_dataflow_report.py` - Report generator to run in Phase 2
- `.env.template` - To understand required environment variables
- `.env` - To check if credentials are configured (if accessible)

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
```