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 autohandai/community-skills --skill analyzing-phishing-email-headersgit clone --depth 1 https://github.com/autohandai/community-skillsWrote 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/autohandai/community-skills/analyzing-phishing-email-headers)<a href="https://agentmods.dev/skills/autohandai/community-skills/analyzing-phishing-email-headers"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/analyzing-phishing-email-headers/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/autohandai/community-skills/analyzing-phishing-email-headers"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/analyzing-phishing-email-headers.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 | $0.00043 | $0.00799 |
| Opus 5 | $0.00022 | $0.00400 |
| Sonnet 5 | $0.00009 | $0.00160 |
| Haiku 4.5 | $0.00004 | $0.00080 |
Grade A, and why
analyzing-phishing-email-headers scanned grade A with 0 findings 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 8d ago.
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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Phishing Email Headers
Overview
Email headers contain critical metadata that reveals the true origin, routing path, and authentication status of emails. Analyzing these headers is a foundational skill for identifying phishing attempts, verifying sender authenticity, and gathering threat intelligence. This skill covers systematic extraction and interpretation of email headers using both manual techniques and automated tools.
Prerequisites
- Basic understanding of SMTP protocol and email delivery
- Familiarity with DNS records (MX, TXT, SPF, DKIM, DMARC)
- Python 3.8+ installed
- Access to email client that can export raw headers (Outlook, Gmail, Thunderbird)
Key Concepts
Critical Header Fields
- Received: Chain of mail servers the message passed through (read bottom to top)
- From / Return-Path / Reply-To: Sender identity fields (often spoofed)
- Authentication-Results: SPF, DKIM, DMARC verification outcomes
- X-Originating-IP: Original sender IP address
- Message-ID: Unique identifier; anomalies indicate spoofing
- X-Mailer / User-Agent: Email client used to compose the message
Red Flags in Headers
- Mismatched
FromandReturn-Pathdomains - SPF/DKIM/DMARC failures in
Authentication-Results - Suspicious
Receivedchains with unfamiliar relay servers X-Originating-IPfrom unexpected geographies- Missing or malformed
Message-ID - Unusual
X-Mailervalues (e.g., mass-mailing tools)
Implementation Steps
Step 1: Extract Raw Email Headers
Gmail: Open email -> Three dots -> "Show original"
Outlook: Open email -> File -> Properties -> Internet Headers
Thunderbird: View -> Message Source (Ctrl+U)
Step 2: Parse Headers with Python
Use the scripts/process.py script to automate header analysis including IP geolocation, authentication validation, and anomaly detection.
Step 3: Validate Authentication Chain
- Check SPF alignment: Does the sending IP match the domain's SPF record?
- Check DKIM signature: Is the cryptographic signature valid?
- Check DMARC policy: Does the message pass DMARC alignment?
What ships with it
7 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.
- 8d ago First seen · 79 lines · 43 tokens per session scan A a585101b3d1e
analyzing-phishing-email-headers is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 43 tokens to every session and 799 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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