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_
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