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Agent skill

Analyzing Indicators Of Compromise

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority.

Apache-2.0Auto-check passedSecurity

Install Analyzing Indicators Of Compromise

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-indicators-of-compromise --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analyzing-indicators-of-compromise .claude/skills/analyzing-indicators-of-compromise && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
analyzing-indicators-of-compromise
GitHub stars
34k
Token cost
~1.9k tokens
SKILL.md length
645 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority.

  • Works in 5 steps: Normalize and Classify IOC Types → Multi-Source Enrichment → Contextualize with Campaign Attribution → …
  • Triaging IOCs from phishing emails
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 2 more sections
  • Runs Python scripts from its folder; reaches api.abuseipdb.com and mb-api.abuse.ch; needs API_KEY

What it does

Analyzing Indicators Of Compromise is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. 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 multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Triaging IOCs from phishing emails
  • Security alerts
  • External threat feeds
  • Enriching raw IOCs with multi-source intelligence

Example prompts

  • “Use the analyzing-indicators-of-compromise skill to analyz indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and…”
  • “/analyzing-indicators-of-compromise”

Requirements

  • Python 3
  • A credential in YOUR_VT_API_KEY
  • A credential in YOUR_KEY

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Normalize and Classify IOC Types
  2. Multi-Source Enrichment
  3. Contextualize with Campaign Attribution
  4. Assign Confidence Score and Disposition
  5. Document and Distribute

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • api.abuseipdb.com
    • mb-api.abuse.ch

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • API_KEY

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Analyzing Indicators Of Compromise loads about 1.9k tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 130 tokens; SKILL.md has 645 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~130
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 645 words, ~1,938 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-indicators-of-compromise/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyzing-indicators-of-compromise
description
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 multi-source intelligence; or making block/monitor/whitelist decisions. Activates for requests involving VirusTotal, AbuseIPDB, MalwareBazaar, MISP, or IOC enrichment pipelines.
domain
cybersecurity
subdomain
threat-intelligence
tags
IOC, VirusTotal, AbuseIPDB, MalwareBazaar, MISP, threat-intelligence, STIX, NIST-CSF
version
1.0.0
author
mahipal
license
Apache-2.0
atlas_techniques
AML.T0052
nist_csf
ID.RA-01, ID.RA-05, DE.CM-01, DE.AE-02
mitre_attack
T1071, T1105, T1041, T1567
mitre_f3.version
1.1

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):

python
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()

AbuseIPDB (IP addresses):

python
import requests

response = requests.get(
    "https://api.abuseipdb.com/api/v2/check",
    headers={"Key": "YOUR_KEY", "Accept": "application/json"},
    params={"ipAddress": "1.2.3.4", "maxAgeInDays": 90}
)
data = response.json()["data"]
print(f"Confidence: {data['abuseConfidenceScore']}%, Reports: {data['totalReports']}")

MalwareBazaar (file hashes):

python
response = requests.post(
    "https://mb-api.abuse.ch/api/v1/",
    data={"query": "get_info", "hash": sha256_hash}
)
result = response.json()
if result["query_status"] == "ok":
    print(result["data"][0]["tags"], result["data"][0]["signature"])
Step 3: Contextualize with Campaign Attribution

Query MISP for existing events matching the IOC:

python
from pymisp import PyMISP

misp = PyMISP("https://misp.example.com", "API_KEY")
results = misp.search(value="evil-domain.com", type_attribute="domain")
for event in results:
    print(event["Event"]["info"], event["Event"]["threat_level_id"])

Check Shodan for IP context (hosting provider, open ports, banners) to identify if the IP belongs to bulletproof hosting or a legitimate cloud provider (false positive risk).

Step 4: Assign Confidence Score and Disposition

Apply a tiered decision framework:

  • Block (High Confidence ≥ 70%): ≥15 AV detections on VT, AbuseIPDB score ≥70, matches known malware family or campaign
  • Monitor/Alert (Medium 40–69%): 5–14 AV detections, moderate AbuseIPDB score, no campaign attribution
  • Whitelist/Investigate (Low <40%): ≤4 AV detections, no abuse reports, legitimate service (Google, Cloudflare CDN IPs)
  • False Positive: Legitimate business service incorrectly flagged; document and exclude from future alerts
Step 5: Document and Distribute

Record findings in TIP/MISP with:

  • All enrichment data collected (timestamps, source, score)
  • Disposition decision and rationale
  • Blocking actions taken (firewall, proxy, DNS sinkhole)
  • Related incident ticket number

Export to STIX indicator object with confidence field set appropriately.

Show full SKILL.md (282 more words)Show less

Key Concepts

TermDefinition
IOCIndicator of Compromise — observable network or host artifact indicating potential compromise
EnrichmentProcess of adding contextual data to a raw IOC from multiple intelligence sources
DefangingModifying IOCs (replacing . with [.]) to prevent accidental activation in documentation
False Positive RatePercentage of benign artifacts incorrectly flagged as malicious; critical for tuning block thresholds
SinkholeDNS server redirecting malicious domain lookups to a benign IP for detection without blocking traffic entirely
TTLTime-to-live for an IOC in blocking controls; IP indicators should expire after 30 days, domains after 90 days

Tools & Systems

  • VirusTotal: Multi-engine malware scanner and threat intelligence platform with 70+ AV engines, sandbox reports, and community comments
  • AbuseIPDB: Community-maintained IP reputation database with 90-day abuse report history
  • MalwareBazaar (abuse.ch): Free malware hash repository with YARA rule associations and malware family tagging
  • URLScan.io: Free URL analysis service that captures screenshots, DOM, and network requests for phishing URL triage
  • Shodan: Internet-wide scan data providing hosting provider, open ports, and banner information for IP enrichment

Common Pitfalls

  • Blocking shared infrastructure: CDN IPs (Cloudflare 104.21.x.x, AWS CloudFront) may legitimately host malicious content but blocking the IP disrupts thousands of legitimate sites.
  • VT score obsession: Low VT detection count does not mean benign — zero-day malware and custom APT tools often score 0 initially. Check sandbox behavior, MISP, and passive DNS.
  • Missing defanging: Pasting live IOCs in emails or Confluence docs can trigger automated URL scanners or phishing tools.
  • No expiration policy: IOCs without TTLs accumulate in blocklists indefinitely, generating false positives as infrastructure is repurposed by legitimate users.
  • Over-relying on single source: VirusTotal aggregates AV opinions — all may be wrong or lag behind emerging malware. Use 3+ independent sources for high-stakes decisions.

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/analyzing-indicators-of-compromise of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

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Categories

Questions about Analyzing Indicators Of Compromise

What does Analyzing Indicators Of Compromise do?

Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Analyzing Indicators Of Compromise is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority.

When should I use Analyzing Indicators Of Compromise?

Analyzing Indicators Of Compromise fits situations like: triaging IOCs from phishing emails; security alerts; external threat feeds; enriching raw IOCs with multi-source intelligence.

How do I install Analyzing Indicators Of Compromise in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a claude-code`. Or copy the skill folder (skills/analyzing-indicators-of-compromise in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/analyzing-indicators-of-compromise in your project. Claude Code loads it when a task matches its description.

How do I install Analyzing Indicators Of Compromise in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a codex`. Or copy the skill folder (skills/analyzing-indicators-of-compromise in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/analyzing-indicators-of-compromise in your project. Codex loads it when a task matches its description.

Can I use Analyzing Indicators Of Compromise in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-indicators-of-compromise -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-indicators-of-compromise, .gemini/skills/analyzing-indicators-of-compromise, .github/skills/analyzing-indicators-of-compromise and .opencode/skills/analyzing-indicators-of-compromise in your project.

What does Analyzing Indicators Of Compromise need to run?

Going by SKILL.md and its folder, Analyzing Indicators Of Compromise needs Python for the scripts in its folder and credentials named API_KEY. Our summary lists: Python 3; A credential in YOUR_VT_API_KEY; A credential in YOUR_KEY.

Does Analyzing Indicators Of Compromise access the network?

SKILL.md names 2 domains. In commands or code: api.abuseipdb.com and mb-api.abuse.ch; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Analyzing Indicators Of Compromise safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Analyzing Indicators Of Compromise use?

Analyzing Indicators Of Compromise is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Analyzing Indicators Of Compromise use?

About 1.9k tokens (SKILL.md is roughly 7.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 732 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Indicators Of Compromise?

Skills that share tags, products or a category with Analyzing Indicators Of Compromise: Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Skill Scanner (getsentry/skills, 1k stars), Serenity Aleabitoreddit (yan-labs/serenity-aleabitoreddit, 481 stars) and Security Alert Triage (elastic/agent-skills, 592 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Indicators Of Compromise?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.