mcpField
R
reporting-pipelines

reporting-pipelines

github.com/bobmatnycData & databases78358 installsUpdated 15w ago
Repository

About reporting-pipelines

A skill for building repeatable reporting pipelines that turn CLI output or JSON artifacts into timestamped CSV, JSON, and Markdown reports. It covers normalization, summaries, file naming, and output storage.

Key features

  1. 01Collect data from CLI or JSON
  2. 02Normalize data into records
  3. 03Export CSV, JSON, and Markdown
  4. 04Add timestamps to report filenames
  5. 05Summarize metrics and store outputs

Use cases

  1. 01Create reports from GitFlow Analytics runs
  2. 02Export EDGAR results from JSON to CSV
  3. 03Standardize report output across repositories

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill reporting-pipelines

After installing, type/reporting-pipelinesin Claude Code to confirm it loaded.

Bundled filesmetadata.json

Details

From the project README

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

Comments

Markdown supported