sec-edgar-skill

Q: How do I install sec-edgar-skill?

Run npx skills add https://github.com/rebyteai-template/rebyte-skills --skill sec-edgar-skill in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sec-edgar-skill/ (personal) or .claude/skills/sec-edgar-skill/ (project).

by rebyteai-templateBusiness & Finance

SEC EDGAR filing analysis using EdgarTools. Use when user asks about SEC filings, company financials, 10-K/10-Q analysis, insider trading, revenue trends, or financial comparisons. Triggers include "SEC filing", "10-K", "10-Q", "8-K", "EDGAR", "company financials", "revenue", "earnings", "insider trading", "financial statements". Do NOT use for real-time stock prices or market data (use market-data skill instead).

Installs: 258
Last 24h: —
Trending rank: —
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/rebyteai-template/rebyte-skills --skill sec-edgar-skill

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads sec-edgar-skill automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

SEC EDGAR Skill - Filing Analysis

Prerequisites

CRITICAL: Run this setup before ANY EdgarTools operations:

from edgar import set_identity
set_identity("Your Name your.email@example.com")  # SEC requires identification

This is a SEC legal requirement. Operations will fail without it.

Installation

EdgarTools must be installed:

pip install edgartools

Token Efficiency Strategy

ALWAYS use .to_context() first - it provides summaries with 56-89% fewer tokens:

Object	`repr()` tokens	`.to_context()` tokens	Savings
Company	~750	~75	90%
Filing	~125	~50	60%
XBRL	~2,500	~275	89%
Statement	~1,250	~400	68%

Rule: Call .to_context() first to understand what's available, then drill down.

Three Ways to Access Filings

1. Published Filings - Bulk Cross-Company Analysis

from edgar import get_filings

# Get recent 10-K filings
filings = get_filings(form="10-K")

# Filter by date range
filings = get_filings(form="10-K", year=2024, quarter=1)

# Multiple form types
filings = get_filings(form=["10-K", "10-Q"])

2. Current Filings - Real-Time Monitoring

from edgar import get_current_filings

# Get today's filings from RSS feed
current = get_current_filings()

# Filter by form type
current_10k = get_current_filings().filter(form="10-K")

3. Company Filings - Single Entity Analysis

from edgar import Company

# By ticker
company = Company("AAPL")

# By CIK
company = Company("0000320193")

# Get company's filings
filings = company.get_filings(form="10-K")
latest_10k = filings.latest()

Financial Data Access

Method 1: Entity Facts API (Fast, Multi-Period)

Best for comparing trends across periods:

company = Company("AAPL")

# Get income statement for multiple periods
income = company.income_statement(periods=5)
print(income)  # Shows 5 years of data

# Get balance sheet
balance = company.balance_sheet(periods=3)

# Get cash flow
cashflow = company.cash_flow_statement(periods=3)

Method 2: Filing XBRL (Detailed, Single Period)

Best for comprehensive single-filing analysis:

company = Company("AAPL")
filing = company.get_filings(form="10-K").latest()

# Get XBRL data
xbrl = filing.xbrl()

# Access financial statements
statements = xbrl.statements
income_stmt = statements.income_statement
balance_sheet = statements.balance_sheet
cash_flow = statements.cash_flow_statement

Common Workflows

Workflow 1: Compare Revenue Across Companies

from edgar import Company

companies = ["AAPL", "MSFT", "GOOGL"]
for ticker in companies:
    company = Company(ticker)
    income = company.income_statement(periods=3)
    print(f"\n{ticker} Revenue Trend:")
    print(income)

Workflow 2: Analyze Latest 10-K

from edgar import Company

company = Company("NVDA")
filing = company.get_filings(form="10-K").latest()

# Get filing metadata
print(filing.to_context())

# Get full text (expensive - 50K+ tokens)
# text = filing.text()

# Get specific sections
# items = filing.items()  # Risk factors, MD&A, etc.

Workflow 3: Track Insider Trading

from edgar import Company

company = Company("TSLA")
insider_filings = company.get_filings(form="4")  # Form 4 = insider trades

for filing in insider_filings[:10]:
    print(filing.to_context())

Workflow 4: Monitor Recent Filings by Sector

from edgar import get_filings

# Get recent tech 10-Ks (use SIC codes)
# SIC 7370-7379 = Computer Programming, Data Processing
filings = get_filings(form="10-K", year=2024)
# Filter by company characteristics after retrieval

Workflow 5: Multi-Year Financial Trend

from edgar import Company

company = Company("AMZN")

# 5-year income statement
income = company.income_statement(periods=20)  # 20 quarters = 5 years

# 5-year balance sheet
balance = company.balance_sheet(periods=20)

print("Income Statement Trend:")
print(income)
print("\nBalance Sheet Trend:")
print(balance)

Search Within Filings

CRITICAL DISTINCTION:

filing = company.get_filings(form="10-K").latest()

# Search WITHIN the filing document (finds text in the 10-K)
results = filing.search("climate risk")

# Search API DOCUMENTATION (finds how to use EdgarTools)
docs_results = filing.docs.search("how to extract")

Do NOT mix these up!

Key Objects Reference

Company

company = Company("AAPL")
company.to_context()  # Summary with available actions
company.name          # Company name
company.cik           # CIK number
company.sic           # SIC code
company.industry      # Industry description
company.get_filings() # Access filings

Filing

filing.to_context()   # Summary
filing.form           # Form type (10-K, 10-Q, etc.)
filing.filing_date    # Date filed
filing.accession_number
filing.text()         # Full document text (EXPENSIVE)
filing.markdown()     # Markdown format
filing.xbrl()         # XBRL financial data
filing.items()        # Document sections

XBRL (Financial Data)

xbrl = filing.xbrl()
xbrl.to_context()     # Summary
xbrl.statements       # All financial statements
xbrl.facts            # Individual facts/metrics

Statement (Financial Statement)

stmt = xbrl.statements.income_statement
print(stmt)           # ASCII table format
stmt.to_dataframe()   # Pandas DataFrame

Anti-Patterns (Avoid These)

DON'T: Parse financials from raw text

# BAD - expensive and error-prone
text = filing.text()
# try to regex parse revenue from text...

DO: Use structured XBRL data

# GOOD - structured and accurate
income = company.income_statement(periods=3)

DON'T: Load full filing when you only need metadata

# BAD - wastes tokens
text = filing.text()  # 50K+ tokens

DO: Use context first

# GOOD - minimal tokens
print(filing.to_context())  # ~50 tokens

Form Types Quick Reference

Form	Description	Use Case
10-K	Annual report	Full-year financials, business description
10-Q	Quarterly report	Quarterly financials
8-K	Current report	Material events (M&A, exec changes)
DEF 14A	Proxy statement	Executive comp, board info
4	Insider trading	Stock transactions by insiders
13F	Institutional holdings	What hedge funds own
S-1	IPO registration	Pre-IPO filings
424B	Prospectus	Bond/stock offerings

Error Handling

from edgar import Company

try:
    company = Company("INVALID")
except Exception as e:
    print(f"Company not found: {e}")

# Check if filings exist
filings = company.get_filings(form="10-K")
if len(filings) == 0:
    print("No 10-K filings found")

Performance Tips

Filter before retrieving: Use form type, date filters
Use Entity Facts API for trends: Faster than parsing multiple filings
Batch operations: Process multiple companies in loops
Cache results: Store frequently accessed data

Reference Documentation

For detailed documentation, see:

EdgarTools workflows
Object reference
Form types reference

Or use the built-in docs:

from edgar import Company
company = Company("AAPL")
company.docs.search("how to get revenue")

FAQ

What does the sec-edgar-skill skill do?

How do I install sec-edgar-skill?

Run npx skills add https://github.com/rebyteai-template/rebyte-skills --skill sec-edgar-skill in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sec-edgar-skill/ (personal) or .claude/skills/sec-edgar-skill/ (project).

Which AI agents can use sec-edgar-skill?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is sec-edgar-skill safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains sec-edgar-skill?

It is published by rebyteai-template in rebyteai-template/rebyte-skills. It has 258 recorded installs.