Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add andycungkrinx91/konoha --skill analyzing-email-headers-for-phishing-investigationgit clone --depth 1 https://github.com/andycungkrinx91/konohaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/andycungkrinx91/konoha/analyzing-email-headers-for-phishing-investigation)<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/analyzing-email-headers-for-phishing-investigation"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/analyzing-email-headers-for-phishing-investigation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/analyzing-email-headers-for-phishing-investigation"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/analyzing-email-headers-for-phishing-investigation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.03262 |
| Opus 5.5 | $0.00018 | $0.01305 |
| Sonnet 5.5 | $0.00009 | $0.00652 |
| Haiku 4.5 | $0.00004 | $0.00326 |
Grade A, and why
analyzing-email-headers-for-phishing-investigation scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s "https://api.abuseipdb.com/api/v2/check?ipAddress=${SENDING_IP}" \ This is a copy
86% identical to analyzing-email-headers-for-phishing-investigation — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 364 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
# 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
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today First seen · 364 lines · 44 tokens per session scan A 755f45233a32
analyzing-email-headers-for-phishing-investigation is a skill published in the GitHub repository andycungkrinx91/konoha (12 stars, last pushed today), licensed MIT. It adds 44 tokens to every session and 3,262 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 86% identical to analyzing-email-headers-for-phishing-investigation, differing in 8 lines, and is treated as a copy.
Other skills, from other repositories
analyzing-email-headers-for-phishing-investigation
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…
analyzing-email-headers-for-phishing-investigation
Parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation.
analyzing-email-headers-for-phishing-investigation
Parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation.
analyzing-email-headers-for-phishing-investigation
Use when parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation. Use when working with analyzing email headers for phishing investigation.
analyzing-email-headers-for-phishing-investigation
An email-forensics guide for tracing where a suspicious message came from and checking whether its sender details are genuine. It uses SPF, DKIM, and DMARC, which are standards that help verify email senders.
analyzing-email-headers-for-phishing-investigation
Parse and analyze email headers to trace the origin of phishing emails, verify sender authenticity, and identify spoofing through SPF, DKIM, and DMARC validation.