Agent skill

Extracting Iocs From Malware Samples

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Extracts indicators of compromise (IOCs) from malware samples, including file hashes, network indicators (IPs, domains, URLs, PCAP indicators), host artifacts (file paths, registry keys, mutexes)…

Apache-2.0Auto-check passedSecurity

Install Extracting Iocs From Malware Samples

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-iocs-from-malware-samples -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-iocs-from-malware-samples --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/extracting-iocs-from-malware-samples .claude/skills/extracting-iocs-from-malware-samples && 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
extracting-iocs-from-malware-samples
GitHub stars
34k
Token cost
~3.6k tokens
SKILL.md length
648 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Extracts indicators of compromise (IOCs) from malware samples, including file hashes, network indicators (IPs, domains, URLs, PCAP indicators), host artifacts (file paths, registry keys, mutexes)…

  • Works in 6 steps: Extract File-Based IOCs → Extract Network IOCs → Extract Host-Based IOCs → …
  • Threat indicator harvesting
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls python3; reaches virustotal.com; needs VT_API_KEY

What it does

Extracting Iocs From Malware Samples is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Extracts indicators of compromise (IOCs) from malware samples, including file hashes, network indicators (IPs, domains, URLs, PCAP indicators), host artifacts (file paths, registry keys, mutexes), and behavioral patterns, using tools like CyberChef, then defangs and exports them in standard threat-intel formats. Use for IOC extraction, threat indicator harvesting, or building detection content from a sample.

Its SKILL.md is about 3.6k 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

  • Threat indicator harvesting
  • Building detection content from a sample

Example prompts

  • “Use the extracting-iocs-from-malware-samples skill to extract indicators of compromise (IOCs) from malware samples, including file hashes, network…”
  • “/extracting-iocs-from-malware-samples”

Requirements

  • Python 3
  • A credential in VT_API_KEY

Workflow steps

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

  1. Extract File-Based IOCs
  2. Extract Network IOCs
  3. Extract Host-Based IOCs
  4. Extract Network IOCs from PCAP
  5. Defang and Validate IOCs
  6. Export IOCs in Standard Formats

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.

    Shell commands in SKILL.md call:

    • python3

    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:

    • virustotal.com

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

  • Credentials

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

    • VT_API_KEY

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

Context cost

Extracting Iocs From Malware Samples loads about 3.6k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 112 tokens; SKILL.md has 648 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~112
When it runs · the whole SKILL.md, loaded when a task matches
~3.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.2k

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). 648 words, ~3,554 tokens.

Download SKILL.mdSave it as .claude/skills/extracting-iocs-from-malware-samples/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
extracting-iocs-from-malware-samples
description
Extracts indicators of compromise (IOCs) from malware samples, including file hashes, network indicators (IPs, domains, URLs, PCAP indicators), host artifacts (file paths, registry keys, mutexes), and behavioral patterns, using tools like CyberChef, then defangs and exports them in standard threat-intel formats. Use for IOC extraction, threat indicator harvesting, or building detection content from a sample.
domain
cybersecurity
subdomain
malware-analysis
tags
malware, IOC-extraction, threat-intelligence, indicators, detection
version
1.0.0
author
mahipal
license
Apache-2.0
nist_csf
DE.AE-02, RS.AN-03, ID.RA-01, DE.CM-01
mitre_attack
T1027, T1055, T1140, T1497

Extracting IOCs from Malware Samples

When to Use

  • A malware analysis (static or dynamic) is complete and actionable indicators need to be extracted for defense teams
  • Building blocklists for firewalls, proxies, and DNS sinkholes from analyzed samples
  • Creating YARA rules, Snort/Suricata signatures, or SIEM detection content from malware artifacts
  • Contributing to threat intelligence sharing platforms (MISP, OTX, ThreatConnect)
  • Tracking malware campaigns by correlating IOCs across multiple samples

Do not use for IOCs from unverified sources without validation; false positives in blocklists can disrupt legitimate business operations.

Prerequisites

  • Python 3.8+ with iocextract, pefile, yara-python libraries installed
  • Completed malware analysis report (static analysis, dynamic analysis, or reverse engineering)
  • Access to PCAP files, memory dumps, or sandbox reports from the analysis
  • MISP instance or STIX/TAXII server for structured IOC sharing
  • VirusTotal API key for IOC enrichment and validation
  • CyberChef for decoding obfuscated indicators

Workflow

Step 1: Extract File-Based IOCs

Compute hashes and identify file metadata indicators:

bash
# Generate all standard hashes
md5sum malware_sample.exe
sha1sum malware_sample.exe
sha256sum malware_sample.exe

# Generate ssdeep fuzzy hash for similarity matching
ssdeep malware_sample.exe

# Generate imphash (import hash) for PE files
python3 -c "
import pefile
pe = pefile.PE('malware_sample.exe')
print(f'Imphash: {pe.get_imphash()}')
"

# Generate TLSH (Trend Micro Locality Sensitive Hash)
python3 -c "
import tlsh
with open('malware_sample.exe', 'rb') as f:
    h = tlsh.hash(f.read())
print(f'TLSH: {h}')
"

# Compile file metadata IOCs
python3 << 'PYEOF'
import pefile
import os
import hashlib
import datetime

pe = pefile.PE("malware_sample.exe")

print("FILE IOCs:")
with open("malware_sample.exe", "rb") as f:
    data = f.read()
    print(f"  MD5:        {hashlib.md5(data).hexdigest()}")
    print(f"  SHA-1:      {hashlib.sha1(data).hexdigest()}")
    print(f"  SHA-256:    {hashlib.sha256(data).hexdigest()}")
    print(f"  File Size:  {len(data)} bytes")

ts = pe.FILE_HEADER.TimeDateStamp
print(f"  Compile:    {datetime.datetime.utcfromtimestamp(ts)} UTC")
print(f"  Imphash:    {pe.get_imphash()}")
PYEOF
Step 2: Extract Network IOCs

Pull network indicators from strings, PCAP, and sandbox reports:

python
# Extract network IOCs from strings
import re

with open("malware_sample.exe", "rb") as f:
    data = f.read()

# Extract ASCII and Unicode strings
ascii_strings = re.findall(b'[ -~]{4,}', data)
unicode_strings = re.findall(b'(?:[ -~]\x00){4,}', data)

all_strings = [s.decode('ascii', errors='ignore') for s in ascii_strings]
all_strings += [s.decode('utf-16-le', errors='ignore') for s in unicode_strings]

# IP addresses (excluding private ranges for C2 indicators)
ip_pattern = re.compile(r'\b(?:(?:25[0-5]|2[0-4]\d|1\d{2}|[1-9]?\d)\.){3}(?:25[0-5]|2[0-4]\d|1\d{2}|[1-9]?\d)\b')
ips = set()
for s in all_strings:
    for ip in ip_pattern.findall(s):
        # Filter out private/reserved ranges
        octets = [int(o) for o in ip.split('.')]
        if octets[0] not in [10, 127, 0] and not (octets[0] == 172 and 16 <= octets[1] <= 31) and not (octets[0] == 192 and octets[1] == 168):
            ips.add(ip)

# Domain names
domain_pattern = re.compile(r'\b[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z]{2,})+\b')
domains = set()
for s in all_strings:
    for d in domain_pattern.findall(s):
        if not d.endswith(('.dll', '.exe', '.sys', '.com.au')):
            domains.add(d)

# URLs
url_pattern = re.compile(r'https?://[^\s<>"{}|\\^`\[\]]+')
urls = set()
for s in all_strings:
    for u in url_pattern.findall(s):
        urls.add(u)

print("NETWORK IOCs:")
print(f"  IPs:     {ips}")
print(f"  Domains: {domains}")
print(f"  URLs:    {urls}")
Step 3: Extract Host-Based IOCs

Identify file paths, registry keys, mutexes, and services:

python
# Extract host-based IOCs from sandbox report
import json

with open("cuckoo_report.json") as f:
    report = json.load(f)

print("HOST IOCs:")

# File paths created or modified
print("\nFile Paths:")
for f in report["behavior"]["summary"].get("files", []):
    if any(p in f.lower() for p in ["temp", "appdata", "system32", "programdata"]):
        print(f"  [DROPPED] {f}")

# Registry keys for persistence
print("\nRegistry Keys:")
for key in report["behavior"]["summary"].get("write_keys", []):
    if any(p in key.lower() for p in ["run", "service", "startup", "shell"]):
        print(f"  [PERSIST] {key}")

# Mutexes (unique to malware family)
print("\nMutexes:")
for mutex in report["behavior"]["summary"].get("mutexes", []):
    if mutex not in ["Local\\!IETld!Mutex", "RasPbFile"]:  # Filter known Windows mutexes
        print(f"  [MUTEX] {mutex}")

# Created services
print("\nServices:")
for svc in report["behavior"]["summary"].get("started_services", []):
    print(f"  [SERVICE] {svc}")
Step 4: Extract Network IOCs from PCAP

Parse network captures for additional indicators:

bash
# Extract DNS queries from PCAP
tshark -r capture.pcap -T fields -e dns.qry.name -Y "dns.flags.response == 0" | sort -u

# Extract HTTP hosts and URLs
tshark -r capture.pcap -T fields -e http.host -e http.request.uri -Y "http.request" | sort -u

# Extract TLS server names (SNI)
tshark -r capture.pcap -T fields -e tls.handshake.extensions_server_name -Y "tls.handshake.type == 1" | sort -u

# Extract JA3 hashes
tshark -r capture.pcap -T fields -e tls.handshake.ja3 -Y "tls.handshake.type == 1" | sort -u

# Extract unique destination IPs
tshark -r capture.pcap -T fields -e ip.dst -Y "ip.src == 10.0.2.15" | sort -u

# Extract User-Agent strings
tshark -r capture.pcap -T fields -e http.user_agent -Y "http.user_agent" | sort -u
Step 5: Defang and Validate IOCs

Defang indicators for safe sharing and validate against threat intelligence:

python
# Defang IOCs for safe sharing
def defang_ip(ip):
    return ip.replace(".", "[.]")

def defang_url(url):
    return url.replace("http", "hxxp").replace(".", "[.]")

def defang_domain(domain):
    return domain.replace(".", "[.]")

# Validate IOCs against VirusTotal
import requests

VT_API_KEY = "your_api_key"

def check_vt_ip(ip):
    resp = requests.get(f"https://www.virustotal.com/api/v3/ip_addresses/{ip}",
                       headers={"x-apikey": VT_API_KEY})
    data = resp.json()
    stats = data["data"]["attributes"]["last_analysis_stats"]
    return stats["malicious"]

def check_vt_domain(domain):
    resp = requests.get(f"https://www.virustotal.com/api/v3/domains/{domain}",
                       headers={"x-apikey": VT_API_KEY})
    data = resp.json()
    stats = data["data"]["attributes"]["last_analysis_stats"]
    return stats["malicious"]

# Validate each IOC
for ip in ips:
    detections = check_vt_ip(ip)
    print(f"  {defang_ip(ip)} - VT: {detections} detections")
Step 6: Export IOCs in Standard Formats

Generate structured IOC outputs for sharing and ingestion:

python
# Export as STIX 2.1 bundle
from stix2 import Indicator, Bundle, Malware, Relationship
import datetime

indicators = []

# File hash indicator
indicators.append(Indicator(
    name="Malware SHA-256 Hash",
    pattern=f"[file:hashes.'SHA-256' = '{sha256_hash}']",
    pattern_type="stix",
    valid_from=datetime.datetime.now(datetime.timezone.utc),
    labels=["malicious-activity"]
))

# IP indicator
for ip in ips:
    indicators.append(Indicator(
        name=f"C2 IP Address {ip}",
        pattern=f"[ipv4-addr:value = '{ip}']",
        pattern_type="stix",
        valid_from=datetime.datetime.now(datetime.timezone.utc),
        labels=["malicious-activity"]
    ))

# Domain indicator
for domain in domains:
    indicators.append(Indicator(
        name=f"C2 Domain {domain}",
        pattern=f"[domain-name:value = '{domain}']",
        pattern_type="stix",
        valid_from=datetime.datetime.now(datetime.timezone.utc),
        labels=["malicious-activity"]
    ))

bundle = Bundle(objects=indicators)
with open("iocs_stix.json", "w") as f:
    f.write(bundle.serialize(pretty=True))

# Export as CSV for SIEM ingestion
import csv
with open("iocs.csv", "w", newline="") as f:
    writer = csv.writer(f)
    writer.writerow(["type", "value", "context", "confidence"])
    writer.writerow(["sha256", sha256_hash, "malware_sample", "high"])
    for ip in ips:
        writer.writerow(["ipv4", ip, "c2_server", "high"])
    for domain in domains:
        writer.writerow(["domain", domain, "c2_domain", "high"])
    for url in urls:
        writer.writerow(["url", url, "c2_url", "high"])

Key Concepts

TermDefinition
IOC (Indicator of Compromise)Forensic artifact observed in a network or system that indicates a potential intrusion: hashes, IPs, domains, file paths, registry keys
DefangingModifying IOCs to prevent accidental activation (e.g., replacing dots with [.] in URLs and IPs for safe sharing in reports)
ImphashMD5 hash of the import table functions in a PE file; samples from the same malware family often share the same imphash
STIX/TAXIIStructured Threat Information Expression / Trusted Automated Exchange; standards for encoding and transmitting threat intelligence
JA3/JA3STLS client/server fingerprint based on ClientHello/ServerHello parameters; identifies specific malware families by their TLS implementation
Fuzzy Hashing (ssdeep)Context-triggered piecewise hashing that identifies similar files even with minor modifications; useful for malware variant detection
MISPMalware Information Sharing Platform; open-source threat intelligence platform for collecting, storing, and sharing IOCs
Show full SKILL.md (288 more words)Show less

Tools & Systems

  • iocextract (Python): Automated IOC extraction library supporting IPs, URLs, domains, hashes, and YARA rules from text
  • MISP: Open-source threat intelligence sharing platform for structured IOC management and distribution
  • CyberChef: Web-based tool for decoding, decrypting, and transforming data useful for deobfuscating encoded IOCs
  • tshark: Command-line network protocol analyzer for extracting network IOCs from PCAP files
  • VirusTotal: Online service for validating and enriching IOCs with community detection results and threat intelligence

Common Scenarios

Scenario: Building a Comprehensive IOC Package from a Ransomware Sample

Context: A ransomware incident requires rapid IOC extraction for blocking across the enterprise while the full investigation continues. Multiple data sources are available: the sample binary, PCAP from network monitoring, and a Cuckoo sandbox report.

Approach:

  1. Compute all file hashes (MD5, SHA-1, SHA-256, imphash, ssdeep) for the ransomware binary and any dropped files
  2. Extract network IOCs from strings in the binary (hardcoded C2 addresses)
  3. Parse the PCAP for DNS queries, HTTP requests, and TLS SNI fields
  4. Extract host IOCs from the sandbox report (file paths, registry keys, mutexes, ransom note filenames)
  5. Validate all network IOCs against VirusTotal to confirm malicious status and check for known associations
  6. Defang all indicators and compile into STIX 2.1 format for sharing and CSV for SIEM ingestion
  7. Submit to MISP event for organizational and community sharing

Pitfalls:

  • Including IP addresses of legitimate CDNs or cloud services without validating context (e.g., AWS IPs used for hosting, not inherently malicious)
  • Not defanging URLs and IPs in reports, leading to accidental clicks or DNS resolution
  • Extracting strings from packed binaries (IOCs from packed samples are unreliable; unpack first)
  • Forgetting to include dropped file hashes (the initial dropper and the final payload are separate IOCs)

Output Format

IOC EXTRACTION REPORT
======================
Sample:           ransomware.exe
Analysis Date:    2025-09-15
Analyst:          [Name]

FILE INDICATORS
SHA-256:          e3b0c44298fc1c149afbf4c8996fb924...
SHA-1:            da39a3ee5e6b4b0d3255bfef95601890afd80709
MD5:              d41d8cd98f00b204e9800998ecf8427e
Imphash:          a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6
ssdeep:           3072:kJh3bN7fY+aUkJh3bN7fY+aU:kJh3R7aUkJh3R7aU

NETWORK INDICATORS
C2 IPs:           185.220.101[.]42, 91.215.85[.]17
C2 Domains:       update.malicious[.]com, backup.evil[.]net
C2 URLs:          hxxps://update.malicious[.]com/gate.php
                  hxxps://backup.evil[.]net/gate.php
JA3 Hash:         a0e9f5d64349fb13191bc781f81f42e1
User-Agent:       Mozilla/5.0 (compatible; MSIE 10.0)

HOST INDICATORS
File Paths:       C:\Users\Public\svchost.exe
                  C:\Users\%USER%\AppData\Local\Temp\payload.dll
                  C:\Users\%USER%\Desktop\README_DECRYPT.txt
Registry Keys:    HKCU\Software\Microsoft\Windows\CurrentVersion\Run\WindowsUpdate
Mutexes:          Global\CryptLocker_2025_Q3
Services:         FakeWindowsUpdate

CONFIDENCE ASSESSMENT
High Confidence:  SHA-256, C2 IPs (validated via VT), Mutexes
Medium Confidence: Domains (could be compromised legitimate sites)
Low Confidence:   User-Agent (common string, high false positive risk)

EXPORT FILES
stix_bundle.json  - STIX 2.1 format for TIP ingestion
iocs.csv          - Flat CSV for SIEM blocklist import
yara_rule.yar     - YARA detection rule

© 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/extracting-iocs-from-malware-samples 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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Questions about Extracting Iocs From Malware Samples

What does Extracting Iocs From Malware Samples do?

Extracts indicators of compromise (IOCs) from malware samples, including file hashes, network indicators (IPs, domains, URLs, PCAP indicators), host artifacts (file paths, registry keys, mutexes)…. Extracting Iocs From Malware Samples is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Extracts indicators of compromise (IOCs) from malware samples, including file hashes, network indicators (IPs, domains, URLs, PCAP indicators), host artifacts (file paths, registry keys, mutexes), and behavioral patterns, using tools like CyberChef, then defangs and exports them in standard threat-intel formats.

When should I use Extracting Iocs From Malware Samples?

Extracting Iocs From Malware Samples fits situations like: threat indicator harvesting; building detection content from a sample.

How do I install Extracting Iocs From Malware Samples in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-iocs-from-malware-samples -a claude-code`. Or copy the skill folder (skills/extracting-iocs-from-malware-samples in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/extracting-iocs-from-malware-samples in your project. Claude Code loads it when a task matches its description.

How do I install Extracting Iocs From Malware Samples in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-iocs-from-malware-samples -a codex`. Or copy the skill folder (skills/extracting-iocs-from-malware-samples in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/extracting-iocs-from-malware-samples in your project. Codex loads it when a task matches its description.

Can I use Extracting Iocs From Malware Samples 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 extracting-iocs-from-malware-samples -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/extracting-iocs-from-malware-samples, .gemini/skills/extracting-iocs-from-malware-samples, .github/skills/extracting-iocs-from-malware-samples and .opencode/skills/extracting-iocs-from-malware-samples in your project.

What does Extracting Iocs From Malware Samples need to run?

Going by SKILL.md and its folder, Extracting Iocs From Malware Samples needs Python for the scripts in its folder, the command-line tools its instructions call (python3) and credentials named VT_API_KEY. Our summary lists: Python 3; A credential in VT_API_KEY.

Does Extracting Iocs From Malware Samples access the network?

SKILL.md names 1 domain. In commands or code: virustotal.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Extracting Iocs From Malware Samples 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 Extracting Iocs From Malware Samples use?

Extracting Iocs From Malware Samples 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 Extracting Iocs From Malware Samples use?

About 3.6k tokens (SKILL.md is roughly 14k 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 660 tokens, read only when the agent opens those files.

What are the alternatives to Extracting Iocs From Malware Samples?

Skills that share tags, products or a category with Extracting Iocs From Malware Samples: Fla Ascend Performance (fla-org/flash-linear-attention, 5.8k stars), Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Skill Scanner (getsentry/skills, 1k stars) and Serenity Aleabitoreddit (yan-labs/serenity-aleabitoreddit, 481 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Extracting Iocs From Malware Samples?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,993 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.