analyzing-indicators-of-compromise

analyzing-indicators-of-compromise is a skill for Claude Code from mukul975/Anthropic-Cybersecurity-Skills. It costs 106 tokens per session (1,874 once invoked), scanned A, original, Apache-2.0.

A guide for checking indicators of compromise—such as IP addresses, domains, URLs, file hashes, and email details—for signs of malicious activity.

In plain words
What is it for?
Use it to normalize and enrich phishing or incident indicators, compare them with reputation and campaign data, and prioritize investigation or blocking.
Why use it?
It adds context and confidence to raw security alerts or threat-feed data before analysts decide how to respond or block something.

Skill for Claude Code

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

Part of the cybersecurity-skills plugin — 56 skills shipped together

Good fit Use it to normalize and enrich phishing or incident indicators, compare them with reputation and campaign data, and prioritize investigation or blocking.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/mukul975/anthropic-cybersecurity-skills/analyzing-indicators-of-compromise
About the project

Anthropic Cybersecurity Skills is a library of structured cybersecurity procedures for AI agents, covering security domains and mappings to established security frameworks. It is for authorized security analysis, penetration testing, incident response, research, defense, and education across compatible AI platforms. The catalogue entries package parts of this library as agent skills, instructions, or a plugin.

mukul975/Anthropic-Cybersecurity-Skills · 32,631 stars · on GitHub · mahipal.engineer

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 mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise
Clone the repo
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills

Made for: Claude Code.

Or install cybersecurity-skills, the plugin that ships this one along with the rest of its 56 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-indicators-of-compromise

README.md
[![agentmods](https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/analyzing-indicators-of-compromise/github.svg)](https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-indicators-of-compromise)
Your own site
<a href="https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-indicators-of-compromise"><img src="https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/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/mukul975/anthropic-cybersecurity-skills/analyzing-indicators-of-compromise"><img src="https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/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 1,874 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. Third-party audits
  • Socket pass 7 Apr 2026
  • Snyk fail 7 Apr 2026
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, 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 Data Exfiltration · line 117
    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.
  • medium Data Exfiltration · line 127
    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 $0.00106 $0.01874
Opus 5 $0.00053 $0.00937
Sonnet 5 $0.00021 $0.00375
Haiku 4.5 $0.00011 $0.00187

Measured 13d ago against content hash 4d6ef90f4bbd, 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 13d 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

Copies of this mod

8 near-identical copies found in the catalogue:

skills/analyzing-indicators-of-compromise/SKILL.md · 194 lines

How it starts

The opening of the file, as written. The whole thing — 194 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 · 194 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. 13d ago First seen · 194 lines · 106 tokens per session scan A 4d6ef90f4bbd

Subscribe to this mod's changes

analyzing-indicators-of-compromise is a skill published in the GitHub repository mukul975/Anthropic-Cybersecurity-Skills (32,631 stars, last pushed 12d ago), licensed Apache-2.0. It adds 106 tokens to every session and 1,874 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). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

analyzing-indicators-of-compromise

Use when analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…

oyi77/1ai-skills · 107 tokens

analyzing-indicators-of-compromise

A guide for investigating indicators of compromise, or signs such as suspicious IP addresses, domains, URLs, file fingerprints, and email addresses. It combines information from several security-intelligence services.

killvxk/cybersecurity-skills-zh · 111 tokens

analyzing-indicators-of-compromise

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…

26zl/cybersec-toolkit · 106 tokens

analyzing-indicators-of-compromise

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…

plurigrid/asi · 106 tokens

analyzing-indicators-of-compromise

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…

balsm-health/Balsm-AI · 106 tokens

analyzing-indicators-of-compromise

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…

pinkpixel-dev/skills-collection-1 · 106 tokens