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

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

• 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/

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