analyzing-indicators-of-compromise

analyzing-indicators-of-compromise is a skill for Claude Code, Codex from onfire7777/universal-ai-skills-library. It costs 106 tokens per session (1,665 once invoked), scanned A, a copy of analyzing-indicators-of-compromise, MIT.

A workflow for checking indicators of compromise, such as suspicious IP addresses, domains, URLs, file hashes, and email artifacts. It enriches these clues with reputation and threat-intelligence sources to estimate whether they are malicious.

In plain words
What is it for?
Use it to classify and enrich IOCs, check reputation, compare them with known campaigns, and prioritize investigation or defensive action.
Why use it?
It helps security teams triage many raw alerts or phishing artifacts and decide which clues deserve investigation or possible blocking.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to classify and enrich IOCs, check reputation, compare them with known campaigns, and prioritize investigation or defensive action.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/onfire7777/universal-ai-skills-library/analyzing-indicators-of-compromise
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 onfire7777/universal-ai-skills-library --skill analyzing-indicators-of-compromise
Clone the repo
git clone --depth 1 https://github.com/onfire7777/universal-ai-skills-library

Made for: Claude Code, Codex.

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-indicators-of-compromise

README.md
[![agentmods](https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/analyzing-indicators-of-compromise/github.svg)](https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/analyzing-indicators-of-compromise)
Your own site
<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/analyzing-indicators-of-compromise"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/analyzing-indicators-of-compromise/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-indicators-of-compromise

Your own site · 80×15
<a href="https://agentmods.dev/skills/onfire7777/universal-ai-skills-library/analyzing-indicators-of-compromise"><img src="https://agentmods.dev/badge/skills/onfire7777/universal-ai-skills-library/analyzing-indicators-of-compromise.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 106 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,665 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 2 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 86% copy Near-identical to another mod 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 $0.00106 $0.01665
Opus 5 $0.00053 $0.00833
Sonnet 5 $0.00021 $0.00333
Haiku 4.5 $0.00011 $0.00167

Measured 9d ago against content hash 14a2283f5dbc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

analyzing-indicators-of-compromise scanned grade A with 2 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 9d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Sends data to an external URLlowData exfiltration

A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.

response = requests.post( "https://mb-api.abuse.ch/api/v1/",

Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.

Makes network callslowCapability

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

response = requests.get(
Origin

This is a copy

86% identical to analyzing-indicators-of-compromise — 71 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skills/analyzing-indicators-of-compromise/SKILL.md · 153 lines

How it starts

The opening of the file, as written. The whole thing — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyzing Indicators of Compromise

When to Use

Use this skill when:

  • A phishing email or alert generates IOCs (URLs, IP addresses, file hashes) requiring rapid triage
  • Automated feeds deliver bulk IOCs that need confidence scoring before ingestion into blocking controls
  • An incident investigation requires contextual enrichment of observed network artifacts

Do not use this skill in isolation for high-stakes blocking decisions — always combine automated enrichment with analyst judgment, especially for shared infrastructure (CDNs, cloud providers).

Prerequisites

  • VirusTotal API key (free or Enterprise) for multi-AV and sandbox lookup
  • AbuseIPDB API key for IP reputation checks
  • MISP instance or TIP for cross-referencing against known campaigns
  • Python with requests and vt-py libraries, or SOAR platform with pre-built connectors

Workflow

Step 1: Normalize and Classify IOC Types

Before enriching, classify each IOC:

  • IPv4/IPv6 address: Check if RFC 1918 private (skip external enrichment), validate format
  • Domain/FQDN: Defang for safe handling (evil[.]com), extract registered domain via tldextract
  • URL: Extract domain + path separately; check for redirectors
  • File hash: Identify hash type (MD5/SHA-1/SHA-256); prefer SHA-256 for uniqueness
  • Email address: Split into domain (check MX/DMARC) and local part for pattern analysis

Defang IOCs in documentation (replace . with [.] and :// with [://]) to prevent accidental clicks.

Step 2: Multi-Source Enrichment

VirusTotal (file hash, URL, IP, domain):

import vt

client = vt.Client("YOUR_VT_API_KEY")

# File hash lookup
file_obj = client.get_object(f"/files/{sha256_hash}")
detections = file_obj.last_analysis_stats
print(f"Malicious: {detections['malicious']}/{sum(detections.values())}")

# Domain analysis
domain_obj = client.get_object(f"/domains/{domain}")
print(domain_obj.last_analysis_stats)
print(domain_obj.reputation)
client.close()

Read the full file on GitHub · 153 lines

Files

What ships with it

3 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.

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. 9d ago First seen · 153 lines · 106 tokens per session scan A 14a2283f5dbc

Subscribe to this mod's changes

analyzing-indicators-of-compromise is a skill published in the GitHub repository onfire7777/universal-ai-skills-library (16 stars, last pushed yesterday), licensed MIT. It adds 106 tokens to every session and 1,665 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 2 findings (sends data to an external url, makes network calls). It is 86% identical to analyzing-indicators-of-compromise, differing in 71 lines, and is treated as a copy.

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