reporting-pipelines

Original：🇺🇸 English

Translated

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

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Sourcebobmatnyc/claude-mpm-skills

Added on2026-02-19

NPX Install

npx skill4agent add bobmatnyc/claude-mpm-skills reporting-pipelines

SKILL.md Content

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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

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

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