Agent skill

Analyzing Email Headers For Phishing Investigation

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Parse and analyze email headers (Received chain, Return-Path, Message-ID) to trace the true origin of a phishing email and validate SPF, DKIM, and DMARC results to confirm or rule out sender spoofing.

Apache-2.0Auto-check passedSecurity

Install Analyzing Email Headers For Phishing Investigation

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-email-headers-for-phishing-investigation --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/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analyzing-email-headers-for-phishing-investigation .claude/skills/analyzing-email-headers-for-phishing-investigation && 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
analyzing-email-headers-for-phishing-investigation
GitHub stars
34k
Token cost
~3.2k tokens
SKILL.md length
481 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Parse and analyze email headers (Received chain, Return-Path, Message-ID) to trace the true origin of a phishing email and validate SPF, DKIM, and DMARC results to confirm or rule out sender spoofing.

  • Works in 5 steps: Extract Raw Email Headers → Parse the Email Header Chain → Validate SPF, DKIM, and DMARC Records → …
  • Triaging a suspicious
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls python3, curl and pip; reaches api.abuseipdb.com and virustotal.com

What it does

Analyzing Email Headers For Phishing Investigation is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Parse and analyze email headers (Received chain, Return-Path, Message-ID) to trace the true origin of a phishing email and validate SPF, DKIM, and DMARC results to confirm or rule out sender spoofing. Use when triaging a suspicious or reported email, investigating a phishing incident, or verifying whether a message's sender domain was spoofed.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Triaging a suspicious
  • Investigating a phishing incident
  • Verifying whether a messages sender domain was spoofed

Example prompts

  • “/analyzing-email-headers-for-phishing-investigation”

Requirements

  • Python 3
  • A credential in YOUR_API_KEY
  • A credential in YOUR_VT_API_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Extract Raw Email Headers
  2. Parse the Email Header Chain
  3. Validate SPF, DKIM, and DMARC Records
  4. Analyze Sender Domain and Infrastructure
  5. Examine Email Body and Attachments

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. 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

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3
    • curl
    • pip

    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:

    • api.abuseipdb.com
    • virustotal.com

    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

Analyzing Email Headers For Phishing Investigation loads about 3.2k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 99 tokens; SKILL.md has 481 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~99
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.9k

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); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 481 words, ~3,173 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-email-headers-for-phishing-investigation/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyzing-email-headers-for-phishing-investigation
description
Parse and analyze email headers (Received chain, Return-Path, Message-ID) to trace the true origin of a phishing email and validate SPF, DKIM, and DMARC results to confirm or rule out sender spoofing. Use when triaging a suspicious or reported email, investigating a phishing incident, or verifying whether a message's sender domain was spoofed.
domain
cybersecurity
subdomain
digital-forensics
tags
forensics, email-analysis, phishing, spf, dkim, dmarc, header-analysis
version
1.0
author
mahipal
license
Apache-2.0
atlas_techniques
AML.T0052
nist_csf
RS.AN-03, DE.AE-02, RS.MA-01
mitre_attack
T1566.001, T1566.002, T1598.003
mitre_f3.version
1.1

Analyzing Email Headers for Phishing Investigation

When to Use

  • When investigating a suspected phishing email to determine its true origin
  • For verifying sender authenticity and detecting email spoofing
  • During incident response when a user has clicked a phishing link
  • When tracing the delivery path and relay servers of a suspicious email
  • For validating SPF, DKIM, and DMARC alignment to identify forgery

Prerequisites

  • Raw email headers from the suspicious message (EML or MSG format)
  • Understanding of SMTP protocol and email header fields
  • Access to DNS lookup tools (dig, nslookup) for SPF/DKIM/DMARC verification
  • Email header analysis tools (MHA, emailheaders.net concepts)
  • Python with email parsing libraries for automated analysis
  • Access to threat intelligence platforms for IP/domain reputation

Workflow

Step 1: Extract Raw Email Headers
bash
# Export from Outlook: Open email > File > Properties > Internet Headers
# Export from Gmail: Open email > Three dots > Show original
# Export from Thunderbird: View > Message Source

# If working with EML file from forensic image
cp /mnt/evidence/Users/suspect/AppData/Local/Microsoft/Outlook/phishing_email.eml \
   /cases/case-2024-001/email/

# If working with PST file, extract individual messages
pip install pypff
python3 << 'PYEOF'
import pypff

pst = pypff.file()
pst.open("/cases/case-2024-001/email/outlook.pst")
root = pst.get_root_folder()

def extract_messages(folder, path=""):
    for i in range(folder.get_number_of_sub_messages()):
        msg = folder.get_sub_message(i)
        headers = msg.get_transport_headers()
        subject = msg.get_subject()
        if headers:
            filename = f"/cases/case-2024-001/email/msg_{i}_{subject[:30]}.txt"
            with open(filename, 'w') as f:
                f.write(headers)
    for i in range(folder.get_number_of_sub_folders()):
        extract_messages(folder.get_sub_folder(i))

extract_messages(root)
PYEOF
Step 2: Parse the Email Header Chain
bash
# Parse headers using Python email library
python3 << 'PYEOF'
import email
from email import policy

with open('/cases/case-2024-001/email/phishing_email.eml', 'r') as f:
    msg = email.message_from_file(f, policy=policy.default)

print("=== KEY HEADER FIELDS ===")
print(f"From:          {msg['From']}")
print(f"To:            {msg['To']}")
print(f"Subject:       {msg['Subject']}")
print(f"Date:          {msg['Date']}")
print(f"Message-ID:    {msg['Message-ID']}")
print(f"Reply-To:      {msg['Reply-To']}")
print(f"Return-Path:   {msg['Return-Path']}")
print(f"X-Mailer:      {msg['X-Mailer']}")
print(f"X-Originating-IP: {msg['X-Originating-IP']}")

print("\n=== RECEIVED HEADERS (bottom-up = chronological) ===")
received_headers = msg.get_all('Received')
if received_headers:
    for i, header in enumerate(reversed(received_headers)):
        print(f"\nHop {i+1}: {header.strip()}")

print("\n=== AUTHENTICATION RESULTS ===")
auth_results = msg.get_all('Authentication-Results')
if auth_results:
    for result in auth_results:
        print(result)

print(f"\nARC-Authentication-Results: {msg.get('ARC-Authentication-Results', 'Not present')}")
print(f"Received-SPF: {msg.get('Received-SPF', 'Not present')}")
print(f"DKIM-Signature: {msg.get('DKIM-Signature', 'Not present')}")
PYEOF
Step 3: Validate SPF, DKIM, and DMARC Records
bash
# Extract the envelope sender domain
SENDER_DOMAIN="example-corp.com"

# Check SPF record
dig TXT $SENDER_DOMAIN +short | grep "v=spf1"
# Example: "v=spf1 include:_spf.google.com include:sendgrid.net ~all"

# Check DKIM record (selector from DKIM-Signature header, e.g., "s=selector1")
DKIM_SELECTOR="selector1"
dig TXT ${DKIM_SELECTOR}._domainkey.${SENDER_DOMAIN} +short

# Check DMARC record
dig TXT _dmarc.${SENDER_DOMAIN} +short
# Example: "v=DMARC1; p=reject; rua=mailto:dmarc@example-corp.com; pct=100"

# Verify the sending IP against SPF
# Extract IP from first Received header
SENDING_IP="203.0.113.45"

# Manual SPF check using python
python3 << 'PYEOF'
import spf  # pip install pyspf

result, explanation = spf.check2(
    i='203.0.113.45',
    s='sender@example-corp.com',
    h='mail.example-corp.com'
)
print(f"SPF Result: {result}")
print(f"Explanation: {explanation}")
# Results: pass, fail, softfail, neutral, none, temperror, permerror
PYEOF

# Check if sending IP is in known malicious IP lists
# Query AbuseIPDB or VirusTotal
curl -s "https://api.abuseipdb.com/api/v2/check?ipAddress=${SENDING_IP}" \
   -H "Key: YOUR_API_KEY" -H "Accept: application/json" | python3 -m json.tool
Step 4: Analyze Sender Domain and Infrastructure
bash
# WHOIS lookup on sender domain
whois $SENDER_DOMAIN | grep -iE '(registrar|creation|expiration|registrant|nameserver)'

# Check domain age (recently registered domains are suspicious)
# DNS record investigation
dig A $SENDER_DOMAIN +short
dig MX $SENDER_DOMAIN +short
dig NS $SENDER_DOMAIN +short

# Reverse DNS on sending IP
dig -x $SENDING_IP +short

# Check for lookalike/typosquatting domains
# Compare with legitimate domain using visual similarity
python3 << 'PYEOF'
import Levenshtein  # pip install python-Levenshtein

legitimate = "microsoft.com"
suspicious = "micr0soft.com"

distance = Levenshtein.distance(legitimate, suspicious)
ratio = Levenshtein.ratio(legitimate, suspicious)
print(f"Edit distance: {distance}")
print(f"Similarity ratio: {ratio:.2%}")
if ratio > 0.8:
    print("WARNING: Likely typosquatting/lookalike domain!")
PYEOF

# Check domain reputation on VirusTotal
curl -s "https://www.virustotal.com/api/v3/domains/${SENDER_DOMAIN}" \
   -H "x-apikey: YOUR_VT_API_KEY" | python3 -m json.tool

# Check if the Reply-To differs from From (common phishing indicator)
python3 -c "
import email
with open('/cases/case-2024-001/email/phishing_email.eml') as f:
    msg = email.message_from_file(f)
from_addr = email.utils.parseaddr(msg['From'])[1]
reply_to = email.utils.parseaddr(msg.get('Reply-To', msg['From']))[1]
if from_addr != reply_to:
    print(f'WARNING: From ({from_addr}) != Reply-To ({reply_to})')
else:
    print('From and Reply-To match')
"
Step 5: Examine Email Body and Attachments
bash
# Extract URLs from email body
python3 << 'PYEOF'
import email
import re
from email import policy

with open('/cases/case-2024-001/email/phishing_email.eml', 'r') as f:
    msg = email.message_from_file(f, policy=policy.default)

body = msg.get_body(preferencelist=('html', 'plain'))
if body:
    content = body.get_content()
    urls = re.findall(r'https?://[^\s<>"\']+', content)
    print("=== URLs FOUND IN EMAIL BODY ===")
    for url in set(urls):
        print(f"  {url}")

    # Check for URL obfuscation (display text != href)
    href_pattern = re.findall(r'<a[^>]*href=["\']([^"\']+)["\'][^>]*>(.*?)</a>', content, re.DOTALL)
    print("\n=== HYPERLINK ANALYSIS ===")
    for href, text in href_pattern:
        display_url = re.findall(r'https?://[^\s<]+', text)
        if display_url and display_url[0] != href:
            print(f"  MISMATCH: Display='{display_url[0]}' -> Actual='{href}'")

# Extract and hash attachments
print("\n=== ATTACHMENTS ===")
for part in msg.walk():
    if part.get_content_disposition() == 'attachment':
        filename = part.get_filename()
        content = part.get_payload(decode=True)
        import hashlib
        sha256 = hashlib.sha256(content).hexdigest()
        print(f"  File: {filename}, Size: {len(content)}, SHA-256: {sha256}")
        with open(f'/cases/case-2024-001/email/attachments/{filename}', 'wb') as af:
            af.write(content)
PYEOF

# Submit attachment hashes to VirusTotal
# Submit URLs to URLhaus or PhishTank for reputation check

Key Concepts

ConceptDescription
SPF (Sender Policy Framework)DNS record specifying authorized mail servers for a domain
DKIM (DomainKeys Identified Mail)Cryptographic signature verifying email content integrity
DMARCPolicy framework combining SPF and DKIM for sender authentication
Received headersServer-added headers showing each hop in the delivery chain (read bottom to top)
Return-PathEnvelope sender address used for bounce messages; may differ from From
Message-IDUnique identifier assigned by the originating mail server
X-Originating-IPOriginal sender IP address (added by some mail services)
Header forgeryAttackers can forge From, Reply-To, and other headers but not Received chains

Tools & Systems

ToolPurpose
MXToolboxOnline email header analyzer and DNS lookup
dig/nslookupDNS record queries for SPF, DKIM, DMARC verification
pyspfPython SPF record validation library
dkimpyPython DKIM signature verification library
PhishToolSpecialized phishing email analysis platform
VirusTotalURL and file reputation checking service
AbuseIPDBIP address reputation database
whoisDomain registration information lookup
Show full SKILL.md (178 more words)Show less

Common Scenarios

Scenario 1: CEO Fraud / Business Email Compromise The email claims to be from the CEO but Reply-To points to a Gmail address, SPF fails because the sending IP is not authorized for the spoofed domain, DKIM is missing, and the From domain is a lookalike (ceo-company.com vs company.com).

Scenario 2: Credential Harvesting Phishing Email contains a link that displays "login.microsoft.com" but href points to a lookalike domain, the attachment is an HTML file containing a fake login page with credential exfiltration JavaScript, the sending domain was registered 3 days ago.

Scenario 3: Malware Delivery via Attachment Email with an Office document attachment containing macros, the sender domain passes SPF but the account was compromised, DKIM signature is valid (sent from legitimate infrastructure), attachment SHA-256 matches known malware on VirusTotal.

Scenario 4: Spear Phishing with Legitimate Service Attacker uses a legitimate email marketing service to send phishing, SPF and DKIM pass because the service is authorized, the phishing is in the content not the infrastructure, requires URL and content analysis rather than header authentication checks.

Output Format

Email Header Analysis Report:
  Subject:     "Urgent: Invoice Payment Required"
  From:        accounting@examp1e-corp.com (SPOOFED)
  Reply-To:    payments.urgent@gmail.com (MISMATCH)
  Return-Path: <bounce@mail-server.xyz>
  Date:        2024-01-15 09:23:45 UTC

  Delivery Path (4 hops):
    Hop 1: mail-server.xyz [203.0.113.45] -> relay1.isp.com
    Hop 2: relay1.isp.com -> mx.target-company.com
    Hop 3: mx.target-company.com -> internal-filter.target.com
    Hop 4: internal-filter.target.com -> mailbox

  Authentication:
    SPF:    FAIL (203.0.113.45 not authorized for examp1e-corp.com)
    DKIM:   NONE (no signature present)
    DMARC:  FAIL (p=none, no enforcement)

  Indicators of Phishing:
    - Lookalike domain (examp1e-corp.com vs example-corp.com, 96% similar)
    - From/Reply-To mismatch
    - Domain registered 2 days before email sent
    - URL in body points to credential harvesting page
    - Attachment: invoice.xlsm (SHA-256: a3f2...) - Known malware on VT

  Risk Level: HIGH

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/analyzing-email-headers-for-phishing-investigation of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

Analyzing Email Headers For Phishing Investigation 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.

Analyzing Email Headers For Phishing Investigation compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Analyzing Email Headers For Phishing Investigation this skillmukul975/Anthropic-Cybersecurity-Skills34k—~3.2kAutomated safety check: PassApache-2.0
Deepsec Documentation Guidevercel-labs/deepsec8.1k—~956Automated safety check: PassApache-2.0
Skill Scannergetsentry/skills1k4 repos~2.5kAutomated safety check: WarnApache-2.0
Serenity Aleabitoreddityan-labs/serenity-aleabitoreddit4811 repos~3.3kAutomated safety check: PassNone
Security Alert Triageelastic/agent-skills5921 repos~3.5kAutomated safety check: NotesApache-2.0
Shiro Attack CLISummerSec/ShiroAttack22.6k—~945Automated safety check: PassMIT

Similar skills

  • Deepsec Documentation Guide

    vercel-labs/deepsec

    Official

    Points the agent at deepsec's own docs to answer questions about initializing, configuring, resuming, scanning with and extending the vulnerability scanner.

    8.1k GitHub stars~956 tokensUpdated 12 days ago
    SecurityAuto-check passed
  • Skill Scanner

    getsentry/skills

    Official

    Scan agent skills for security issues. An agent skill from getsentry/skills.

    1k GitHub starsUsed in 4 repos~2.5k tokens
    SecurityAuto-check: warnings
  • Serenity Aleabitoreddit

    yan-labs/serenity-aleabitoreddit

    Apply trader Serenity's (@aleabitoreddit) AI/semiconductor supply-chain analytical lens to US-stock ideas and market judgment.

    481 GitHub starsUsed in 1 repo~3.3k tokens
    SecurityAuto-check passed
  • Security Alert Triage

    elastic/agent-skills

    Official

    Triage Elastic Security alerts — gather context, classify threats, create cases, and acknowledge.

    592 GitHub starsUsed in 1 repo~3.5k tokens
    SecurityAuto-check: notes
  • Shiro Attack CLI

    SummerSec/ShiroAttack2

    当用户要求利用、检测或测试 Apache Shiro rememberMe 反序列化漏洞 (Shiro-550, CVE-2016-4437) 时使用。触发词包括 "Shiro"、"rememberMe"、"shiro attack"、"CVE-2016-4437"、"Shiro-550"、"爆破 Shiro key"、"利用 Shiro"、"Shiro…

    2.6k GitHub stars~945 tokensUpdated 4 mo ago
    SecurityAuto-check passed
  • Cve Remediation

    rundeck/rundeck

    Verify if a CVE affects the project and remediate it. An agent skill from rundeck/rundeck.

    6.3k GitHub stars~2.9k tokensUpdated yesterday
    SecurityAuto-check passed

More from mukul975/Anthropic-Cybersecurity-Skills

All 644 skills in this repo
  • Campaign Attribution Evidence Analysis

    mukul975/Anthropic-Cybersecurity-Skills

    Weighs infrastructure, TTP, malware code and timing evidence with the Diamond Model and competing hypotheses to reach a confidence-rated attribution.

    34k GitHub stars~2.3k tokensUpdated 1 mo ago
    Auto-check passed
  • Go Malware Analysis in Ghidra

    mukul975/Anthropic-Cybersecurity-Skills

    Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.

    34k GitHub stars~2.8k tokensUpdated 1 mo ago
    Auto-check passed
  • LNK and Jump List Forensics

    mukul975/Anthropic-Cybersecurity-Skills

    Guides forensic analysis of Windows LNK shortcut files and Jump Lists with LECmd, JLECmd and manual parsing to show file access and program execution.

    34k GitHub stars~2.8k tokensUpdated 1 mo ago
    Auto-check passed
  • Malware Persistence Analysis with Autoruns

    mukul975/Anthropic-Cybersecurity-Skills

    Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.

    34k GitHub stars~1.2k tokensUpdated 1 mo ago
    Auto-check passed
  • NTFS MFT Deleted File Recovery

    mukul975/Anthropic-Cybersecurity-Skills

    Guides a Windows forensic examination of the NTFS Master File Table to recover deleted-file evidence, build timelines and spot timestomping.

    34k GitHub stars~2.7k tokensUpdated 1 mo ago
    Auto-check passed
  • Network Covert Channel Analysis

    mukul975/Anthropic-Cybersecurity-Skills

    Detects DNS tunneling, ICMP exfiltration and HTTP-based covert channels in packet captures and DNS logs when hunting for hidden command-and-control traffic.

    34k GitHub stars~2k tokensUpdated 1 mo ago
    Auto-check passed

Categories

Questions about Analyzing Email Headers For Phishing Investigation

What does Analyzing Email Headers For Phishing Investigation do?

Parse and analyze email headers (Received chain, Return-Path, Message-ID) to trace the true origin of a phishing email and validate SPF, DKIM, and DMARC results to confirm or rule out sender spoofing. Analyzing Email Headers For Phishing Investigation is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Parse and analyze email headers (Received chain, Return-Path, Message-ID) to trace the true origin of a phishing email and validate SPF, DKIM, and DMARC results to confirm or rule out sender spoofing.

When should I use Analyzing Email Headers For Phishing Investigation?

Analyzing Email Headers For Phishing Investigation fits situations like: triaging a suspicious; investigating a phishing incident; verifying whether a messages sender domain was spoofed.

How do I install Analyzing Email Headers For Phishing Investigation in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a claude-code`. Or copy the skill folder (skills/analyzing-email-headers-for-phishing-investigation in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/analyzing-email-headers-for-phishing-investigation in your project. Claude Code loads it when a task matches its description.

How do I install Analyzing Email Headers For Phishing Investigation in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a codex`. Or copy the skill folder (skills/analyzing-email-headers-for-phishing-investigation in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/analyzing-email-headers-for-phishing-investigation in your project. Codex loads it when a task matches its description.

Can I use Analyzing Email Headers For Phishing Investigation 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 mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-email-headers-for-phishing-investigation -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-email-headers-for-phishing-investigation, .gemini/skills/analyzing-email-headers-for-phishing-investigation, .github/skills/analyzing-email-headers-for-phishing-investigation and .opencode/skills/analyzing-email-headers-for-phishing-investigation in your project.

What does Analyzing Email Headers For Phishing Investigation need to run?

Going by SKILL.md and its folder, Analyzing Email Headers For Phishing Investigation needs Python for the scripts in its folder and the command-line tools its instructions call (python3, curl and pip). Our summary lists: Python 3; A credential in YOUR_API_KEY; A credential in YOUR_VT_API_KEY.

Does Analyzing Email Headers For Phishing Investigation access the network?

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

Is Analyzing Email Headers For Phishing Investigation 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Analyzing Email Headers For Phishing Investigation use?

Analyzing Email Headers For Phishing Investigation is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Analyzing Email Headers For Phishing Investigation use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 693 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Email Headers For Phishing Investigation?

Skills that share tags, products or a category with Analyzing Email Headers For Phishing Investigation: Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Skill Scanner (getsentry/skills, 1k stars), Serenity Aleabitoreddit (yan-labs/serenity-aleabitoreddit, 481 stars) and Security Alert Triage (elastic/agent-skills, 592 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Email Headers For Phishing Investigation?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.