---
name: reporting-pipelines
description: Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing.
user-invocable: false
disable-model-invocation: true
version: 1.0.0
category: universal
author: Claude MPM Team
license: MIT
progressive_disclosure:
entry_point:
summary: "Generate CSV/JSON/markdown reports with timestamped filenames and summary outputs."
when_to_use: "Building reporting flows, exporting analytics results, or standardizing CSV/JSON/markdown outputs across projects."
quick_start: "1. Run the CLI that produces base data 2. Export CSV/JSON/markdown with timestamps 3. Save to reports/"
tags:
- reporting
- csv
- json
- markdown
- analytics
---
# 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`