Research-mind universal-data-reporting-pipelines

Reporting Pipelines

install
source · Clone the upstream repo
git clone https://github.com/MacPhobos/research-mind
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/MacPhobos/research-mind "$T" && mkdir -p ~/.claude/skills && cp -r "$T/.claude/skills/universal-data-reporting-pipelines" ~/.claude/skills/macphobos-research-mind-universal-data-reporting-pipelines && rm -rf "$T"
manifest: .claude/skills/universal-data-reporting-pipelines/skill.md
source content

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