reporting-pipelines

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English

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Chinese

Reporting Pipelines

报告流水线

Overview

概述

Your reporting pattern is consistent across repos: run a CLI or script that emits structured data, then export CSV/JSON/markdown reports with timestamped filenames into

reports/

tests/results/

你的报告模式在各个代码库中保持一致：运行CLI或脚本生成结构化数据，然后将带有时间戳文件名的CSV/JSON/Markdown报告导出到

reports/

或

tests/results/

目录中。

GitFlow Analytics Pattern

GitFlow Analytics模式

bash

undefined

bash

undefined

Basic run

基础运行命令

gitflow-analytics -c config.yaml --weeks 8 --output ./reports

Explicit analyze + CSV

显式分析并生成CSV

gitflow-analytics analyze -c config.yaml --weeks 12 --output ./reports --generate-csv


Outputs include CSV + markdown narrative reports with date suffixes.

gitflow-analytics analyze -c config.yaml --weeks 12 --output ./reports --generate-csv


输出内容包括带日期后缀的CSV和Markdown叙述性报告。

EDGAR CSV Export Pattern

EDGAR CSV导出模式

edgar/scripts/create_csv_reports.py

reads a JSON results file and emits:

```
executive_compensation_<timestamp>.csv
```
```
top_25_executives_<timestamp>.csv
```
```
company_summary_<timestamp>.csv
```

This script uses pandas for sorting and percentile calculations.

edgar/scripts/create_csv_reports.py

读取JSON结果文件并生成：

```
executive_compensation_<timestamp>.csv
```
```
top_25_executives_<timestamp>.csv
```
```
company_summary_<timestamp>.csv
```

该脚本使用pandas进行排序和百分位数计算。

Standard Pipeline Steps

标准流水线步骤

Collect base data (CLI or JSON artifacts)
Normalize into rows/records
Export CSV/JSON/markdown with timestamp suffixes
Summarize key metrics in stdout
Store outputs in
```
reports/
```
or
```
tests/results/
```

收集基础数据（CLI或JSON产物）
标准化为行/记录格式
导出带时间戳后缀的CSV/JSON/Markdown文件
在标准输出中汇总关键指标
存储输出文件到
```
reports/
```
或
```
tests/results/
```
目录

Naming Conventions

命名规范

Use
```
YYYYMMDD
```
or
```
YYYYMMDD_HHMMSS
```
suffixes
Keep one output directory per repo (
```
reports/
```
or
```
tests/results/
```
)
Prefer explicit prefixes (e.g.,
```
narrative_report_
```
,
```
comprehensive_export_
```
)

使用
```
YYYYMMDD
```
或
```
YYYYMMDD_HHMMSS
```
作为后缀
每个代码库保留一个输出目录（
```
reports/
```
或
```
tests/results/
```
）
优先使用明确的前缀（例如：
```
narrative_report_
```
、
```
comprehensive_export_
```
）

Troubleshooting

故障排查

Missing output: ensure output directory exists and is writable.
Large CSVs: filter or aggregate before export; keep summary CSVs for quick review.

输出缺失：确保输出目录存在且可写入。
大型CSV文件：在导出前进行过滤或聚合；保留摘要CSV以便快速查看。