analyzing-phishing-emails

analyzing-phishing-emails is a skill for Claude Code from trilwu/secskills. It costs 119 tokens per session (4,384 once invoked), scanned A, original, MIT.

A guide for checking whether a suspicious email is genuine by examining its technical evidence, links, attachments, and sender identity. Phishing is an attempt to trick someone into revealing information or running harmful content.

In plain words
What is it for?
It helps analyse email files and headers, verify SPF, DKIM, and DMARC sender checks, unwrap redirected links, inspect QR codes and attachments safely, and collect indicators for blocking or investigation.
Why use it?
The visible sender address and link text can be forged, so they do not reliably show where a message came from or where it leads.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the secskills-defense plugin — 22 skills shipped together

Good fit It helps analyse email files and headers, verify SPF, DKIM, and DMARC sender checks, unwrap redirected links, inspect QR codes and attachments safely, and collect indicators for blocking or investigation.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/trilwu/secskills/analyzing-phishing-emails
Install

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.

Any agent
npx skills add trilwu/secskills --skill analyzing-phishing-emails
Clone the repo
git clone --depth 1 https://github.com/trilwu/secskills

Made for: Claude Code.

Or install secskills-defense, the plugin that ships this one along with the rest of its 22 skills.

Wrote 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.

agentmods badge for analyzing-phishing-emails

README.md
[![agentmods](https://agentmods.dev/badge/skills/trilwu/secskills/analyzing-phishing-emails/github.svg)](https://agentmods.dev/skills/trilwu/secskills/analyzing-phishing-emails)
Your own site
<a href="https://agentmods.dev/skills/trilwu/secskills/analyzing-phishing-emails"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/analyzing-phishing-emails/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.

agentmods 80×15 button for analyzing-phishing-emails

Your own site · 80×15
<a href="https://agentmods.dev/skills/trilwu/secskills/analyzing-phishing-emails"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/analyzing-phishing-emails.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 119 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,384 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 3 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high YARA Match · line 32
    YARA rule matched a hack tool or exploit indicator (offensive tools, reconnaissance, privilege escalation, or exploit frameworks).
    Fix: Remove offensive tool references and exploit code. Legitimate agent skills should not contain penetration testing tools, exploit frameworks, or reconnaissance utilities.
  • high Prompt Injection · line 268
    Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.
    Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
  • medium Data Exfiltration · line 178
    Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.
    Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00119 $0.04384
Opus 5 $0.00060 $0.02192
Sonnet 5 $0.00024 $0.00877
Haiku 4.5 $0.00012 $0.00438

Measured 11d ago against content hash 75c57a5a0ba9, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

analyzing-phishing-emails 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 11d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

`curl -sI hxxp://bit[.]ly/xyz` (read `Location:`, do not follow) or a
secskills-defense/skills/analyzing-phishing-emails/SKILL.md · 305 lines

How it starts

The opening of the file, as written. The whole thing — 305 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyzing Phishing Emails

An email is a stack of claims — who sent it, where it came from, that its links are safe — and phishing analysis is checking each claim against evidence the sender could not forge: the Received chain, the authentication results, and the true destination of every link and attachment. The From header is a display, not a fact. Anyone can type any address into it; your job is to find the evidence that agrees or disagrees.

When to Use

  • A user reports a suspicious email and you need a verdict and IOCs
  • You have a .eml or .msg file to analyze offline
  • You are handed raw headers and asked whether a message is spoofed
  • A message carries a link or attachment that needs safe triage
  • A business email compromise, invoice-fraud, or vendor-impersonation case
  • Confirming whether a domain or brand was spoofed against your users

When NOT to Use

  • You extracted an attachment and need to detonate it — come here first to safely extract and defang it, then hand the payload to analyzing-malware
  • The phish already succeeded and you are chasing the mailbox/OAuth compromise in the tenant — use investigating-m365-entra
  • You are building the phishing campaign, not analyzing one — use performing-social-engineering
  • The broader incident the phish kicked off — use responding-to-incidents
  • Analyzing the callback traffic from a detonated payload — use analyzing-network-traffic

Safe Handling — Do This First

Treat every reported message as live. The failure mode is not misreading a header; it is clicking a link in a production mail client or double-clicking an attachment on your own host.

  • Never open the message in a live client. Attacker-controlled remote images fire a read beacon; one click on a link authenticates you to their harvester.
  • Work from the raw source only — the .eml/.msg, not a forwarded copy. Forwarding rewrites headers and strips the evidence you need.
  • Defang every indicator before it touches a report, ticket, or chat: httphxxp, .[.], @[at]. So http://evil.com/login becomes hxxp://evil[.]com/login. Defanging prevents an accidental click downstream and stops link-preview bots from detonating it for you.
  • Extract and detonate only in an isolated VM with no host sharing and simulated or monitored egress — see analyzing-malware for the build.

Read the full file on GitHub · 305 lines

Changes

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.

  1. 11d ago First seen · 305 lines · 119 tokens per session scan A 75c57a5a0ba9

Subscribe to this mod's changes

analyzing-phishing-emails is a skill published in the GitHub repository trilwu/secskills (138 stars, last pushed 6d ago), licensed MIT. It adds 119 tokens to every session and 4,384 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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