Sec Edgar Pipeline

Name: Sec Edgar Pipeline
Author: bobmatnyc

bobmatnyc/claude-mpm-skills

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

Ingest, parse, and normalize SEC EDGAR filings into queryable datasets for fintech dashboards, compliance research, and fundamental equity analysis pipelines.

About

Documents building SEC EDGAR data pipelines: accessing Commission feeds, parsing regulatory filings into structured records, handling incremental updates, and exposing clean financial datasets for research, compliance, and quantitative analysis applications.

SEC EDGAR API and bulk feed access
Filing type parsing (10-K, 10-Q, 8-K)
XBRL and HTML extraction patterns
Incremental sync and deduplication
Normalized tables for analytics queries

Sec Edgar Pipeline by the numbers

235 all-time installs (skills.sh)
Ranked #386 of 1,106 Finance & Trading 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 sec-edgar-pipeline

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Installs	235
repo stars	★ 63
Last updated	July 18, 2026
Repository	bobmatnyc/claude-mpm-skills ↗

What it does

Ingest, parse, and normalize SEC EDGAR filings into queryable datasets for fintech dashboards, compliance research, and fundamental equity analysis pipelines.

Files

SKILL.mdMarkdownGitHub ↗

SEC EDGAR Pipeline

Overview

This pipeline is centered on edgar-analyzer and the EDGAR data sources. The core loop is: configure credentials, create a project with examples, analyze patterns, generate code, run extraction, and export reports.

Setup (Keys + User Agent)

Use the setup wizard to configure required keys:

python -m edgar_analyzer setup
# or
edgar-analyzer setup

Required entries:

OPENROUTER_API_KEY
(Optional) JINA_API_KEY
EDGAR user agent string ("Name email@example.com")

End-to-End CLI Workflow

# 1. Create project
edgar-analyzer project create my_project --template minimal

# 2. Add examples + project.yaml
# projects/my_project/examples/*.json

# 3. Analyze examples
edgar-analyzer analyze-project projects/my_project

# 4. Generate extraction code
edgar-analyzer generate-code projects/my_project

# 5. Run extraction
edgar-analyzer run-extraction projects/my_project --output-format csv

Outputs land in projects/<name>/output/.

EDGAR-Specific Conventions

CIK values are 10-digit, zero-padded (e.g., 0000320193).
Rate limit: SEC API allows 10 requests/sec. Scripts use ~0.11s delays.
User agent is mandatory; include name + email.

Scripted Example (Apple DEF 14A)

edgar/scripts/fetch_apple_def14a.py shows the direct flow:

1. Fetch latest DEF 14A metadata 2. Download HTML 3. Parse Summary Compensation Table (SCT) 4. Save raw HTML + extracted JSON + ground truth

Recipe-Driven Extraction

edgar/recipes/sct_extraction/config.yaml defines a multi-step pipeline:

Fetch DEF 14A filings by company list
Extract SCT tables with SCTAdapter
Validate with sct_validator
Write results to output/sct

Report Generation

edgar/scripts/create_csv_reports.py converts JSON results into:

executive_compensation_<timestamp>.csv
top_25_executives_<timestamp>.csv
company_summary_<timestamp>.csv

Troubleshooting

No filings found: confirm CIK formatting and filing type (DEF 14A vs DEF 14A/A).
API errors: slow down requests and confirm user-agent is set.
Extraction errors: regenerate code or use manual ground truth in POC scripts.

Related Skills

universal/data/reporting-pipelines
toolchains/python/testing/pytest

{
  "name": "sec-edgar-pipeline",
  "version": "1.0.0",
  "category": "universal",
  "tags": [
    "sec",
    "edgar",
    "filings",
    "def14a",
    "extraction"
  ],
  "entry_point_tokens": 144,
  "full_tokens": 796,
  "related_skills": [
    "../reporting-pipelines"
  ],
  "author": "Claude MPM Team",
  "license": "MIT",
  "updated": "2025-12-31",
  "source_path": "universal/data/sec-edgar-pipeline/SKILL.md",
  "repository": "https://github.com/bobmatnyc/claude-mpm-skills"
}