Reporting Pipelines

Name: Reporting Pipelines
Author: bobmatnyc

bobmatnyc/claude-mpm-skills

248 installs
63 repo stars
Updated July 18, 2026
bobmatnyc/claude-mpm-skills

Design and implement reporting pipelines that aggregate product, revenue, or operational metrics into dashboards, scheduled exports, and stakeholder-ready summaries.

About

The reporting-pipelines skill helps Claude architect and implement analytics reporting flows—from event ingestion and warehouse transforms through scheduled jobs and export formats—so teams can track KPIs and share consistent operational reports.

End-to-end metric pipelines
Scheduled and on-demand reports
ETL-style data aggregation
Dashboard-ready outputs
Stakeholder reporting workflows

Reporting Pipelines by the numbers

248 all-time installs (skills.sh)
Ranked #614 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
Data as of Aug 1, 2026 (Skillselion catalog sync)

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

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Installs	248
repo stars	★ 63
Last updated	July 18, 2026
Repository	bobmatnyc/claude-mpm-skills ↗

What it does

Design and implement reporting pipelines that aggregate product, revenue, or operational metrics into dashboards, scheduled exports, and stakeholder-ready summaries.

Files

SKILL.mdMarkdownGitHub ↗

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

{
  "name": "reporting-pipelines",
  "version": "1.0.0",
  "category": "universal",
  "tags": [
    "reporting",
    "csv",
    "json",
    "markdown",
    "analytics"
  ],
  "entry_point_tokens": 98,
  "full_tokens": 549,
  "related_skills": [
    "../sec-edgar-pipeline"
  ],
  "author": "Claude MPM Team",
  "license": "MIT",
  "updated": "2025-12-31",
  "source_path": "universal/data/reporting-pipelines/SKILL.md",
  "repository": "https://github.com/bobmatnyc/claude-mpm-skills"
}