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 trilwu/secskills --skill analyzing-phishing-emailsgit clone --depth 1 https://github.com/trilwu/secskillsWrote 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/trilwu/secskills/analyzing-phishing-emails)<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.
<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>- NVIDIA SkillSpector warn
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.
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 | $0.00119 | $0.04384 |
| Opus 5 | $0.00060 | $0.02192 |
| Sonnet 5 | $0.00024 | $0.00877 |
| Haiku 4.5 | $0.00012 | $0.00438 |
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 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
.emlor.msgfile 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:
http→hxxp,.→[.],@→[at]. Sohttp://evil.com/loginbecomeshxxp://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-malwarefor the build.
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.
- 11d ago First seen · 305 lines · 119 tokens per session scan A 75c57a5a0ba9
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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