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/ioc-investigation/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/ioc-investigation)<a href="https://agentmods.dev/skills/scstelz/security-investigator/ioc-investigation"><img src="https://agentmods.dev/badge/skills/scstelz/security-investigator/ioc-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/scstelz/security-investigator/ioc-investigation"><img src="https://agentmods.dev/badge/skills/scstelz/security-investigator/ioc-investigation.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 Excessive Agency · line 97 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00118 | $0.09803 |
| Opus 5 | $0.00059 | $0.04901 |
| Sonnet 5 | $0.00024 | $0.01961 |
| Haiku 4.5 | $0.00012 | $0.00980 |
Grade A, and why
ioc-investigation 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 13d 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 — 1,096 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IoC (Indicator of Compromise) Investigation - Instructions
Purpose
This skill performs comprehensive security investigations on Indicators of Compromise (IoCs) including:
- IP Addresses: Network connections, threat intel matches, geographic analysis, organizational exposure
- DNS Domains: Domain reputation, connection events, email-based threats, URL analysis
- URLs: URL reputation, phishing detection, email delivery, browser activity
- File Hashes: Malware analysis, file prevalence, related alerts, affected devices
The investigation correlates IoCs with Microsoft Defender Threat Intelligence, identifies associated CVEs, and enumerates organizational assets affected by those vulnerabilities.
📑 TABLE OF CONTENTS
- Critical Workflow Rules - Start here!
- Investigation Types - By IoC type
- Quick Start - 5-step investigation pattern
- Execution Workflow - Complete process
- Sample KQL Queries - Validated query patterns
- Defender API Queries - Threat Intel & Vulnerability Management
- JSON Export Structure - Required fields
- Error Handling - Troubleshooting guide
Investigation shortcuts:
- Suspicious IP from spray/brute-force (TP Q4): Q2 (network connections) → Q11 (sign-in analysis) → Q8 (alert evidence) → Q1 (TI match)
- IP from user risk event (TP Q3): Q11 (sign-in analysis) → Q2 (device connections) → Q9 (security alerts) →
enrich_ips.py - Phishing domain/URL (TP Q8): Q4 (DNS/HTTP connections) → Q6 (email delivery) → Q8 (alert evidence) → Q1 (TI match)
- File hash from incident (TP Q1): Q7 (file events across all tables) → Q9 (security alerts) → Q10 (custom indicator check) → Q12 (CVE extraction)
- IoC organizational exposure (TP Q1+Q11): Q2/Q4 (affected devices) → Q9 (alert correlation) → Q12 (CVEs from alerts)
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.
- 13d ago First seen · 1,096 lines · 118 tokens per session scan A c2026456cd61
ioc-investigation is a skill published in the GitHub repository SCStelz/security-investigator (245 stars, last pushed 4d ago), licensed MIT. It adds 118 tokens to every session and 9,803 once invoked, about $0.0006 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.
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