reporting-pipelines

$npx mdskill add bobmatnyc/claude-mpm-skills/reporting-pipelines

Generate timestamped CSV, JSON, and Markdown reports from structured data.

  • Export analytics results as CSV, JSON, or Markdown with timestamped filenames.
  • Depends on CLI tools like gitflow-analytics and custom Python scripts.
  • Selects output format based on user request or default pipeline configuration.
  • Saves reports to reports/ or tests/results/ directories for easy access.

SKILL.md

.github/skills/reporting-pipelinesView on GitHub ↗
---
name: reporting-pipelines
description: Reporting pipelines for CSV/JSON/Markdown exports with timestamped outputs, summaries, and post-processing.
user-invocable: false
disable-model-invocation: true
version: 1.0.0
category: universal
author: Claude MPM Team
license: MIT
progressive_disclosure:
  entry_point:
    summary: "Generate CSV/JSON/markdown reports with timestamped filenames and summary outputs."
    when_to_use: "Building reporting flows, exporting analytics results, or standardizing CSV/JSON/markdown outputs across projects."
    quick_start: "1. Run the CLI that produces base data 2. Export CSV/JSON/markdown with timestamps 3. Save to reports/"
tags:
  - reporting
  - csv
  - json
  - markdown
  - analytics
---

# 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

```bash
# 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`

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