Borrowing it
Nothing to install: this file belongs to SCStelz/security-investigator. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SCStelz/security-investigator/main/.github/skills/email-threat-posture/SKILL.mdgit clone --depth 1 https://github.com/SCStelz/security-investigatorWrote 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/scstelz/security-investigator/email-threat-posture)<a href="https://agentmods.dev/skills/scstelz/security-investigator/email-threat-posture"><img src="https://agentmods.dev/badge/skills/scstelz/security-investigator/email-threat-posture/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/scstelz/security-investigator/email-threat-posture"><img src="https://agentmods.dev/badge/skills/scstelz/security-investigator/email-threat-posture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Agent Snooping · line 964 Skill enumerates or reads other installed skills. Access to other skills' SKILL.md files or the skills directory reveals prompt instructions, capabilities, and secrets that should be invisible to peer skills.Fix: Remove all code or instructions that list or read other skills' files or directories. Skills should operate independently; cross-skill access is a privilege escalation.
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 | $0.00156 | $0.09894 |
| Opus 5 | $0.00078 | $0.04947 |
| Sonnet 5 | $0.00031 | $0.01979 |
| Haiku 4.5 | $0.00016 | $0.00989 |
Grade A, and why
email-threat-posture 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 12d 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 — 970 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Email Threat Protection Posture — Instructions
Purpose
This skill generates an Email Threat Protection Posture Report using Microsoft Defender for Office 365 (MDO) telemetry available through Advanced Hunting. It provides C-level visibility into how effectively the organization's email security stack is detecting, blocking, and remediating email-based threats.
What this skill covers:
| Domain | Key Questions Answered |
|---|---|
| 📬 Mail Flow Overview | How many inbound emails? What's the daily trend? Who are the top senders? |
| 🛡️ Threat Composition | How many phishing, spam, and malware threats were detected? |
| 🎯 Phishing Protection | How many phishing emails were blocked vs delivered? Who are the most targeted users? |
| 🔐 Email Authentication | What are the DMARC/DKIM/SPF/CompAuth pass rates? Which domains fail authentication? |
| 🧹 Post-Delivery Remediation | How effective is ZAP? How many remediations succeeded vs failed? |
| 🔗 Safe Links Protection | How many URL clicks were scanned? Were any phishing clicks allowed through? |
| 📎 Attachment Analysis | What attachment types are flowing through email? Were any malicious? |
| 📊 Detection Methods | What detection technologies are catching threats (URL detonation, fingerprinting, etc.)? |
| 📦 Delivery Disposition | Where do emails end up — inbox, junk, quarantine, blocked? |
| 🚨 MDO Incidents | How many security incidents were generated by Defender for Office? What severity, status, and types? |
Data sources: EmailEvents, EmailPostDeliveryEvents, UrlClickEvents, EmailAttachmentInfo, SecurityAlert, SecurityIncident (Advanced Hunting)
References:
What ships with it
1 file 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.
- 12d ago First seen · 970 lines · 156 tokens per session scan A 6532e717a3cd
email-threat-posture is a skill published in the GitHub repository SCStelz/security-investigator (245 stars, last pushed 4d ago), licensed MIT. It adds 156 tokens to every session and 9,894 once invoked, about $0.0008 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 2026-08-30.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…