reporting-pipelines

What this skill does

# Reporting Pipelines

## Overview

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/` or `tests/results/`.

## GitFlow Analytics Pattern

```bash
# Basic run
gitflow-analytics -c config.yaml --weeks 8 --output ./reports

# Explicit analyze + CSV
gitflow-analytics analyze -c config.yaml --weeks 12 --output ./reports --generate-csv
```

Outputs include CSV + markdown narrative reports with date suffixes.

## EDGAR CSV Export Pattern

`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.

## Standard Pipeline Steps

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

## Naming Conventions

- 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_`)

## Troubleshooting

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

## Related Skills

- `universal/data/sec-edgar-pipeline`
- `toolchains/universal/infrastructure/github-actions`