About

Imported from bobmatnyc/claude-mpm-skills (universal/data/sec-edgar-pipeline/SKILL.md). Install upstream with npx skills add bobmatnyc/claude-mpm-skills --skill sec-edgar-pipeline. Copyright stays with the author (MIT).

SEC EDGAR Pipeline

Name: sec-edgar-pipeline
Author: bobmatnyc

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:

Fetch latest DEF 14A metadata
Download HTML
Parse Summary Compensation Table (SCT)
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

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/bobmatnyc-claude-mpm-skills-sec-edgar-pipeline/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

bobmatnyc-claude-mpm-skills-sec-edgar-pipeline.ocm.jsonjson

{
  "ocm": "1",
  "id": "bobmatnyc-claude-mpm-skills-sec-edgar-pipeline",
  "kind": "skill",
  "name": "sec-edgar-pipeline",
  "description": "SEC EDGAR extraction pipeline: setup, filing discovery by CIK, recipe-driven extraction, and report generation.",
  "publisher": "bobmatnyc",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "general"
    ],
    "tags": [
      "skill-md",
      "sec",
      "edgar",
      "filings",
      "cik",
      "def14a",
      "extraction",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "SEC EDGAR extraction pipeline: setup, filing discovery by CIK, recipe-driven extraction, and report generation."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/bobmatnyc/claude-mpm-skills",
      "path": "universal/data/sec-edgar-pipeline/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/bobmatnyc/claude-mpm-skills/blob/HEAD/universal/data/sec-edgar-pipeline/SKILL.md",
      "key": "bobmatnyc/claude-mpm-skills/universal/data/sec-edgar-pipeline/SKILL.md"
    },
    "license": "MIT"
  },
  "instructions": "# SEC EDGAR Pipeline\n\n## Overview\n\nThis 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.\n\n## Setup (Keys + User Agent)\n\nUse the setup wizard to configure required keys:\n\n```bash\npython -m edgar_analyzer setup\n# or\nedgar-analyzer setup\n```\n\nRequired entries:\n\n- `OPENROUTER_API_KEY`\n- (Optional) `JINA_API_KEY`\n- `EDGAR` user agent string (\"Name email@example.com\")\n\n## End-to-End CLI Workflow\n\n```bash\n# 1. Create project\nedgar-analyzer p",
  "cost": {
    "context_tokens": 571
  }
}

Fetch it by URL: GET /api/v1/registry/bobmatnyc-claude-mpm-skills-sec-edgar-pipeline/manifest?version=1.0.0

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