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Reporting pipelines

Skill ulpi-io/plugin-marketplace/plugins/bobmatnyc/skills/reporting-pipelines

Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing.From its SKILL.md

Install
npx -y skills add ulpi-io/plugin-marketplace --skill reporting-pipelines

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SKILL.md

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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

  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

What ships with it: 1 file

487 B alongside SKILL.md

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