AgentSkillsCN

sec-edgar-pipeline

SEC EDGAR 提取流水线：包括设置、按 CIK 查找申报文件、基于配方的提取流程，以及报表生成。

SKILL.md
--- frontmatter
name: sec-edgar-pipeline
description: "SEC EDGAR extraction pipeline: setup, filing discovery by CIK, recipe-driven extraction, and report generation."
version: 1.0.0
category: universal
author: Claude MPM Team
license: MIT
progressive_disclosure:
  entry_point:
    summary: "EDGAR pipeline: configure keys + user-agent, find filings by CIK, extract data via recipes/scripts, and export CSV/JSON reports."
    when_to_use: "Building SEC EDGAR extraction flows, running edgar-analyzer CLI, or generating compensation/filing reports from DEF 14A and related filings."
    quick_start: "1. edgar-analyzer setup 2. edgar-analyzer analyze-project projects/<name> 3. edgar-analyzer generate-code 4. edgar-analyzer run-extraction"
tags:
  - sec
  - edgar
  - filings
  - cik
  - def14a
  - extraction

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:

bash
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

bash
# 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