Agent skill

SEC EDGAR Filing Analysis

by HKUDS in HKUDS/Vibe-Trading

Interprets US company filings from SEC EDGAR (10-K, 10-Q, 8-K, proxy statements, Form 4) to pull out financials, risk factors and investment signals.

MITAuto-check passedBusiness, Finance & HR

Install SEC EDGAR Filing Analysis

skills CLI
$ npx skills add HKUDS/Vibe-Trading --skill edgar-sec-filings -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install HKUDS/Vibe-Trading edgar-sec-filings --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/HKUDS/Vibe-Trading.git skills-src && mkdir -p .claude/skills && cp -r skills-src/agent/src/skills/edgar-sec-filings .claude/skills/edgar-sec-filings && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
edgar-sec-filings
GitHub stars
35k
Token cost
~3.1k tokens
SKILL.md length
844 words
Files
1
Skills in repo
89
Repo updated
First seen
Licence
MIT

At a glance

Interprets US company filings from SEC EDGAR (10-K, 10-Q, 8-K, proxy statements, Form 4) to pull out financials, risk factors and investment signals.

  • Works in 5 steps: Revenue drivers: which segments /… → Margin commentary: management… → Forward guidance language: "expect",… → …
  • Extracting key financials and risk factors from a 10-K or 10-Q
  • SKILL.md covers Overview, Filing Types and Investment…, EDGAR Data Access and 10-K / 10-Q Analysis Framework, plus 6 more sections
  • Reaches sec.gov and efts.sec.gov

What it does

This skill supplies an analytical framework for reading SEC filings rather than fetching them. A table covers filing types with their frequency, key content and what each signals: 10-K for a full-year view, 10-Q for trend confirmation, 8-K for events such as M&A or a CEO change, DEF 14A for governance, Form 4 for insider buying and selling, 13F for institutional holdings and SC 13D/G for ownership stakes above 5%.

For retrieval it points to the read_url tool with EDGAR URLs, or to yfinance Ticker objects for structured data. The 10-K and 10-Q framework then walks through the income statement, looking at revenue growth, gross margin trend, SG&A as a share of revenue, R&D intensity and non-recurring items, and through the balance sheet, looking at net cash or debt, liquidity ratios, goodwill share and inventory days.

When your agent uses it

  • Extracting key financials and risk factors from a 10-K or 10-Q
  • Reading an 8-K for a material event such as M&A or a restatement
  • Following insider buying and selling through Form 4 filings
  • Checking governance and executive pay in a proxy statement

Example prompts

  • “Summarize the risk factors and MD&A in Apple's latest 10-K.”
  • “What does this 8-K about a CEO change mean for the company?”
  • “Pull the recent Form 4 filings for this ticker and tell me whether insiders are buying.”
  • “Compare gross margin and inventory days across the last four 10-Q filings.”

Requirements

  • Network access to SEC EDGAR through the read_url tool or yfinance

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

  1. Revenue drivers: which segments / geographies are growing, which are declining
  2. Margin commentary: management explanation for margin changes
  3. Forward guidance language: "expect", "anticipate", "believe" — tone shift detection
  4. Risk factor changes: compare risk factors vs prior filing; NEW risks added = material change
  5. Liquidity and capital resources: debt maturity schedule, credit facility availability

What it can do on your machine

Read from SKILL.md and the folder at commit 8e43007. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are python).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • sec.gov
    • efts.sec.gov

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

SEC EDGAR Filing Analysis loads about 3.1k tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 844 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~3.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from HKUDS/Vibe-Trading at commit 8e43007, republished under its MIT licence (© HKUDS). 844 words, ~3,085 tokens.

Download SKILL.mdSave it as .claude/skills/edgar-sec-filings/SKILL.md (or your agent's skills folder).
name
edgar-sec-filings
description
SEC EDGAR filing analysis — 10-K, 10-Q, 8-K, proxy statements, insider Form 4. Extract key financials, risk factors, management discussion, and generate investment signals from US public company filings.
category
flow

SEC EDGAR Filing Analysis

Overview

Analyze US public company filings from SEC EDGAR to extract fundamental insights, risk signals, and investment-relevant information. Covers annual reports (10-K), quarterly reports (10-Q), current events (8-K), proxy statements (DEF 14A), and insider transactions (Form 4).

This skill provides the analytical framework for interpreting SEC filings. Data retrieval uses read_url tool with EDGAR URLs or yfinance Ticker objects for structured financial data.

Filing Types and Investment Relevance

FilingFrequencyKey ContentSignal Value
10-KAnnualFull-year financials, risk factors, MD&A, segment dataComprehensive fundamental view
10-QQuarterlyQuarterly financials, interim MD&A, legal updatesTrend confirmation / inflection detection
8-KEvent-drivenMaterial events: M&A, CEO change, restatement, guidanceCatalyst / risk trigger
DEF 14AAnnual (proxy)Executive comp, board composition, shareholder proposalsGovernance quality signal
Form 4Within 2 daysInsider buys / sellsInsider conviction signal
13FQuarterlyInstitutional holdings >$100M AUMSmart money positioning
SC 13D/GEvent-driven>5% ownership stake disclosureActivist / strategic investor signal

EDGAR Data Access

Direct EDGAR URLs
python
# Company filings search
# https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK={ticker}&type={filing_type}

# Example: Apple 10-K filings
url = "https://www.sec.gov/cgi-bin/browse-edgar?action=getcompany&CIK=AAPL&type=10-K&dateb=&owner=include&count=10"

# EDGAR full-text search (EFTS)
# https://efts.sec.gov/LATEST/search-index?q={query}&dateRange=custom&startdt={start}&enddt={end}
Via yfinance (structured data)
python
import yfinance as yf
ticker = yf.Ticker("AAPL")

# Financial statements (derived from 10-K/10-Q)
income = ticker.financials           # Annual income statement
income_q = ticker.quarterly_financials  # Quarterly
balance = ticker.balance_sheet       # Balance sheet
cashflow = ticker.cashflow           # Cash flow statement

# Insider transactions (derived from Form 4)
insider = ticker.insider_transactions

# Institutional holders (derived from 13F)
institutions = ticker.institutional_holders
major = ticker.major_holders

10-K / 10-Q Analysis Framework

I. Financial Statement Deep Dive

Income Statement Focus:

  • Revenue growth rate: YoY and QoQ acceleration / deceleration
  • Gross margin trend: expanding (pricing power) vs compressing (cost pressure)
  • Operating leverage: SG&A as % of revenue declining = positive operating leverage
  • R&D intensity: R&D / revenue ratio vs peers
  • Non-recurring items: restructuring charges, impairments, one-time gains

Balance Sheet Focus:

  • Cash & equivalents vs total debt: net cash / net debt position
  • Current ratio and quick ratio: liquidity health
  • Goodwill / intangibles as % of total assets: acquisition-driven growth risk
  • Inventory days (for manufacturers / retailers): rising = demand weakness signal
  • Accounts receivable days: rising = collection risk or channel stuffing

Cash Flow Focus:

  • FCF = Operating CF - CapEx: true cash generation power
  • FCF conversion = FCF / Net Income: >80% = high earnings quality
  • CapEx intensity = CapEx / Revenue: rising = growth investment or maintenance burden
  • Stock-based compensation: add back to get true cash earnings
  • Buyback vs dividend: capital return strategy signal
II. MD&A (Management Discussion & Analysis)

The MD&A section is the most qualitative and forward-looking part of the filing.

Key extraction targets:

  1. Revenue drivers: which segments / geographies are growing, which are declining
  2. Margin commentary: management explanation for margin changes
  3. Forward guidance language: "expect", "anticipate", "believe" — tone shift detection
  4. Risk factor changes: compare risk factors vs prior filing; NEW risks added = material change
  5. Liquidity and capital resources: debt maturity schedule, credit facility availability

Tone analysis signals:

python
# Simplified tone scoring
positive_words = ["growth", "improvement", "strong", "exceeded", "momentum", "opportunity"]
negative_words = ["challenging", "decline", "uncertainty", "headwind", "pressure", "risk"]
cautious_words = ["moderate", "cautious", "prudent", "measured", "selective"]

# Count frequency change vs prior filing
# Rising negative word count = deteriorating outlook
# Rising cautious words = management hedging
III. Risk Factor Analysis

Risk factor change detection (10-K vs prior 10-K):

Change TypeSignalAction
New risk factor addedMaterial new risk identifiedDeep dive on the specific risk
Risk factor removedRisk resolved or deemed immaterialPositive signal if genuine resolution
Language intensifiedRisk escalatingReview exposure and hedging
Order changed (moved higher)Risk priority elevatedAssess potential impact magnitude

Common risk categories for US equities:

  • Regulatory / legal risk (antitrust, FDA, patent expiry)
  • Customer concentration (>10% revenue from single customer must be disclosed)
  • Geographic concentration (China exposure, emerging market risk)
  • Technology disruption risk
  • Cybersecurity risk (new SEC mandate: material cybersecurity incidents must be disclosed in 8-K)
  • Climate / ESG risk (increasingly required)
Show full SKILL.md (337 more words)Show less

8-K Event Analysis

Material Event Classification
Event Type8-K ItemTypical Price ImpactTime Sensitivity
Earnings pre-release2.02HighImmediate
M&A announcement1.01Very highImmediate
CEO / CFO departure5.02Medium-highSame day
Restatement4.02Very high (negative)Immediate
Guidance revision7.01/8.01HighSame day
Credit agreement change1.01Low-mediumMonitor
Share repurchase program8.01Low positiveBackground signal
Dividend change8.01MediumSame day
8-K Signal Rules
python
# High-priority 8-K events
if item == "4.02":  # Restatement
    signal = "strong_negative"  # Restatements destroy trust
    action = "review_all_prior_financials"
elif item == "2.02" and surprise_direction == "negative":
    signal = "negative"  # Earnings pre-announcement miss
elif item == "5.02" and role in ["CEO", "CFO"]:
    signal = "uncertainty"  # C-suite departure = governance risk
elif item == "1.01" and event_type == "acquisition":
    signal = "evaluate"  # M&A: acquirer usually -2 to -5%, target +20-40%

Insider Transaction Analysis (Form 4)

Signal Framework
PatternSignalConfidence
Cluster buying: 3+ insiders buying within 30 daysStrong bullishHigh
CEO/CFO large open-market purchase (>$500K)BullishHigh
Insider buying after price decline >20%Contrarian bullishMedium-high
Cluster selling at all-time highsNeutral to mildly bearishLow (may be pre-planned)
CFO selling >50% of holdingsBearishMedium
10b5-1 plan salesNeutralLow (pre-programmed)

Key distinctions:

  • Open-market purchases (most informative): insider spending own money
  • 10b5-1 plan sales (least informative): pre-programmed, regulatory safe harbor
  • Option exercises + immediate sale: often tax-driven, low signal value
  • Gift transactions: ignore for signal purposes
python
# Insider signal scoring
def score_insider_activity(transactions, lookback_days=90):
    buys = [t for t in transactions if t.type == "Purchase" and t.days_ago <= lookback_days]
    sells = [t for t in transactions if t.type == "Sale" and t.days_ago <= lookback_days]

    buy_value = sum(t.value for t in buys)
    sell_value = sum(t.value for t in sells)

    # Filter out 10b5-1 plan sales
    organic_sells = [s for s in sells if not s.is_10b5_1]

    if len(buys) >= 3 and buy_value > 1_000_000:
        return "strong_bullish"
    elif buy_value > sell_value * 2:
        return "bullish"
    elif len(organic_sells) >= 3 and sell_value > 5_000_000:
        return "bearish_watch"
    else:
        return "neutral"

13F Institutional Holdings Analysis

Smart Money Tracking

Key metrics:

  • Number of institutional holders: rising = broadening ownership base
  • Top 10 holder concentration: >50% = concentrated, vulnerable to single-fund redemption
  • New positions initiated this quarter: smart money entering
  • Positions closed this quarter: smart money exiting
  • Activist stakes (SC 13D): potential for corporate action catalyst

Institutional quality tiers:

  1. Tier 1 — Conviction signals: Berkshire, Baupost, Greenlight, Pershing Square, Tiger Global
  2. Tier 2 — Trend signals: BlackRock, Vanguard, Fidelity (flow-driven, less stock-picking signal)
  3. Tier 3 — Quantitative: Renaissance, Two Sigma, Citadel (high turnover, less directional signal)
python
# 13F change detection
def analyze_13f_changes(current_holders, prior_holders):
    new_positions = current_holders - prior_holders  # New entries
    closed_positions = prior_holders - current_holders  # Exits

    # Flag: multiple Tier 1 funds initiating
    tier1_new = [h for h in new_positions if h.tier == 1]
    if len(tier1_new) >= 2:
        signal = "strong_smart_money_accumulation"

    return signal

Composite Filing Signal

Scoring Template
python
filing_score = {
    "financial_health": 0,       # -2 to +2: based on 10-K/10-Q financials
    "management_tone": 0,        # -2 to +2: MD&A sentiment shift
    "risk_factor_change": 0,     # -2 to +2: new risks vs resolved risks
    "insider_activity": 0,       # -2 to +2: net insider buying/selling
    "institutional_flow": 0,     # -2 to +2: 13F position changes
    "event_catalyst": 0,         # -2 to +2: recent 8-K impact
}
# Total range: -12 to +12
# > +6: strong fundamental bullish
# +2 to +6: mild bullish
# -2 to +2: neutral
# < -2: fundamental caution

Output Format

## SEC Filing Analysis — [Ticker]

### Filing Summary
- **Latest 10-K/10-Q**: [date], [period]
- **Recent 8-K events**: [list material events]
- **Insider activity (90d)**: [net buy/sell summary]

### Financial Health
- Revenue trend: [accelerating / stable / decelerating]
- Margin trajectory: [expanding / stable / compressing]
- FCF conversion: [strong / adequate / weak]
- Balance sheet: [net cash / moderate leverage / high leverage]

### MD&A Tone Shift
- vs prior filing: [more optimistic / unchanged / more cautious]
- Key language changes: [specific quotes or paraphrases]

### Risk Factor Changes
- New risks: [list any new risk factors added]
- Intensified risks: [list risks with stronger language]
- Resolved risks: [list removed risk factors]

### Insider & Institutional Signals
- Insider net activity: [cluster buy / neutral / cluster sell]
- Institutional positioning: [accumulation / stable / distribution]

### Composite Signal
| Dimension | Score (-2~+2) | Basis |
|-----------|---------------|-------|
| Financial health | +1 | Revenue accelerating, margins stable |
| Management tone | -1 | More cautious language in MD&A |
| ... | ... | ... |

### Investment Implication
- Direction: [bullish / bearish / neutral]
- Confidence: [high / medium / low]
- Key monitoring: [next earnings date, upcoming 8-K triggers]

Notes

  • EDGAR filings are public and free; no API key required (rate limit: 10 requests/second with User-Agent header)
  • 10-K/10-Q data is backward-looking; combine with forward guidance and analyst estimates for complete view
  • Insider transaction data has a 2-business-day reporting lag; real-time insider data requires paid services
  • 13F data is reported with a 45-day lag after quarter-end; positions may have already changed
  • This framework is for research purposes only and does not constitute investment advice

© HKUDS, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in agent/src/skills/edgar-sec-filings of HKUDS/Vibe-Trading.

Open the folder on GitHubat commit 8e43007

Compare with similar skills

SEC EDGAR Filing Analysis next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

SEC EDGAR Filing Analysis compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
SEC EDGAR Filing Analysis this skillHKUDS/Vibe-Trading35k—~3.1kAutomated safety check: PassMIT
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Earnings Report Deep Readingxbtlin/ai-berkshire17k—~1.4kAutomated safety check: PassMIT
Financial Data Cross-Validationxbtlin/ai-berkshire17k—~1.4kAutomated safety check: PassMIT
Financial Statement Deep DiveGeeksfino/finskills282—~1.8kAutomated safety check: PassApache-2.0

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Questions about SEC EDGAR Filing Analysis

What does SEC EDGAR Filing Analysis do?

Interprets US company filings from SEC EDGAR (10-K, 10-Q, 8-K, proxy statements, Form 4) to pull out financials, risk factors and investment signals. This skill supplies an analytical framework for reading SEC filings rather than fetching them. A table covers filing types with their frequency, key content and what each signals: 10-K for a full-year view, 10-Q for trend confirmation, 8-K for events such as M&A or a CEO change, DEF 14A for governance, Form 4 for insider buying and selling, 13F for institutional holdings and SC 13D/G for ownership stakes above 5%.

When should I use SEC EDGAR Filing Analysis?

SEC EDGAR Filing Analysis fits situations like: extracting key financials and risk factors from a 10-K or 10-Q; reading an 8-K for a material event such as M&A or a restatement; following insider buying and selling through Form 4 filings; checking governance and executive pay in a proxy statement.

How do I install SEC EDGAR Filing Analysis in Claude Code?

Run `npx skills add HKUDS/Vibe-Trading --skill edgar-sec-filings -a claude-code`. Or copy the skill folder (agent/src/skills/edgar-sec-filings in HKUDS/Vibe-Trading) into .claude/skills/edgar-sec-filings in your project. Claude Code loads it when a task matches its description.

How do I install SEC EDGAR Filing Analysis in Codex?

Run `npx skills add HKUDS/Vibe-Trading --skill edgar-sec-filings -a codex`. Or copy the skill folder (agent/src/skills/edgar-sec-filings in HKUDS/Vibe-Trading) into .agents/skills/edgar-sec-filings in your project. Codex loads it when a task matches its description.

Can I use SEC EDGAR Filing Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add HKUDS/Vibe-Trading --skill edgar-sec-filings -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/edgar-sec-filings, .gemini/skills/edgar-sec-filings, .github/skills/edgar-sec-filings and .opencode/skills/edgar-sec-filings in your project.

What does SEC EDGAR Filing Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: SEC EDGAR Filing Analysis is instructions for the agent only. Our summary lists: Network access to SEC EDGAR through the read_url tool or yfinance.

Does SEC EDGAR Filing Analysis access the network?

SKILL.md names 2 domains. In commands or code: sec.gov and efts.sec.gov; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is SEC EDGAR Filing Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does SEC EDGAR Filing Analysis use?

SEC EDGAR Filing Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does SEC EDGAR Filing Analysis use?

About 3.1k tokens (SKILL.md is roughly 12k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to SEC EDGAR Filing Analysis?

Skills that share tags, products or a category with SEC EDGAR Filing Analysis: Financial Research (firecrawl/web-agent, 1.2k stars), US Market Data Toolkit (Geeksfino/finskills, 282 stars), Earnings Report Deep Reading (xbtlin/ai-berkshire, 17k stars) and Financial Data Cross-Validation (xbtlin/ai-berkshire, 17k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains SEC EDGAR Filing Analysis?

HKUDS (a GitHub organization) maintains it in HKUDS/Vibe-Trading, which has 35,163 GitHub stars. The repository holds 89 skills in this directory. The repository was last updated on October 10, 2026.

Source: HKUDS/Vibe-Trading on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.