Fla Ascend Performance
fla-org/flash-linear-attention
Guidelines for Ascend NPU kernel / Triton-Ascend backend performance work in the FLA repo.
Agent skill
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)…
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-iocs-from-malware-samples -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-iocs-from-malware-samples --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "extracting-iocs-from-malware-samples" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-iocs-from-malware-samples into .claude/skills/extracting-iocs-from-malware-samples/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-iocs-from-malware-samples", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-iocs-from-malware-samplesType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-iocs-from-malware-samples -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-iocs-from-malware-samples --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/extracting-iocs-from-malware-samples .agents/skills/extracting-iocs-from-malware-samples && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "extracting-iocs-from-malware-samples" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-iocs-from-malware-samples into .agents/skills/extracting-iocs-from-malware-samples/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-iocs-from-malware-samples", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-iocs-from-malware-samples -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-iocs-from-malware-samples --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/extracting-iocs-from-malware-samples .cursor/skills/extracting-iocs-from-malware-samples && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "extracting-iocs-from-malware-samples" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-iocs-from-malware-samples into .cursor/skills/extracting-iocs-from-malware-samples/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-iocs-from-malware-samples", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git --path skills/extracting-iocs-from-malware-samples--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-iocs-from-malware-samples -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-iocs-from-malware-samples --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/extracting-iocs-from-malware-samples .gemini/skills/extracting-iocs-from-malware-samples && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "extracting-iocs-from-malware-samples" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-iocs-from-malware-samples into .gemini/skills/extracting-iocs-from-malware-samples/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-iocs-from-malware-samples", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-iocs-from-malware-samplesInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-iocs-from-malware-samples -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/extracting-iocs-from-malware-samples .github/skills/extracting-iocs-from-malware-samples && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "extracting-iocs-from-malware-samples" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-iocs-from-malware-samples into .github/skills/extracting-iocs-from-malware-samples/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-iocs-from-malware-samples", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill extracting-iocs-from-malware-samples -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills extracting-iocs-from-malware-samples --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/extracting-iocs-from-malware-samples .opencode/skills/extracting-iocs-from-malware-samples && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "extracting-iocs-from-malware-samples" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/extracting-iocs-from-malware-samples into .opencode/skills/extracting-iocs-from-malware-samples/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "extracting-iocs-from-malware-samples", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
extracting-iocs-from-malware-samplesExtracts 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. 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.
6 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.
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.
Ships 1 file in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
python3From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
virustotal.comFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
VT_API_KEYFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 648 words, ~3,554 tokens.
.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.Do not use for IOCs from unverified sources without validation; false positives in blocklists can disrupt legitimate business operations.
iocextract, pefile, yara-python libraries installedCompute hashes and identify file metadata indicators:
# 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()}")
PYEOFPull network indicators from strings, PCAP, and sandbox reports:
# 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}")Identify file paths, registry keys, mutexes, and services:
# 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}")Parse network captures for additional indicators:
# 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 -uDefang indicators for safe sharing and validate against threat intelligence:
# 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")Generate structured IOC outputs for sharing and ingestion:
# 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"])| Term | Definition |
|---|---|
| IOC (Indicator of Compromise) | Forensic artifact observed in a network or system that indicates a potential intrusion: hashes, IPs, domains, file paths, registry keys |
| Defanging | Modifying IOCs to prevent accidental activation (e.g., replacing dots with [.] in URLs and IPs for safe sharing in reports) |
| Imphash | MD5 hash of the import table functions in a PE file; samples from the same malware family often share the same imphash |
| STIX/TAXII | Structured Threat Information Expression / Trusted Automated Exchange; standards for encoding and transmitting threat intelligence |
| JA3/JA3S | TLS 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 |
| MISP | Malware Information Sharing Platform; open-source threat intelligence platform for collecting, storing, and sharing IOCs |
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:
Pitfalls:
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
SKILL.md and 3 other files (scripts, references) in skills/extracting-iocs-from-malware-samples of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Extracting Iocs From Malware Samples next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Extracting Iocs From Malware Samples this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3.6k | Automated safety check: Pass | Apache-2.0 | |
| Fla Ascend Performancefla-org/flash-linear-attention | 5.8k | — | ~6.3k | Automated safety check: Pass | MIT | |
| Deepsec Documentation Guidevercel-labs/deepsec | 8.1k | — | ~956 | Automated safety check: Pass | Apache-2.0 | |
| Skill Scannergetsentry/skills | 1k | 4 repos | ~2.5k | Automated safety check: Warn | Apache-2.0 | |
| Serenity Aleabitoreddityan-labs/serenity-aleabitoreddit | 481 | 1 repos | ~3.3k | Automated safety check: Pass | None | |
| Security Alert Triageelastic/agent-skills | 592 | 1 repos | ~3.5k | Automated safety check: Notes | Apache-2.0 |
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Guides forensic analysis of Windows LNK shortcut files and Jump Lists with LECmd, JLECmd and manual parsing to show file access and program execution.
mukul975/Anthropic-Cybersecurity-Skills
Hunts Windows malware persistence with Sysinternals Autoruns, covering run keys, services, scheduled tasks and drivers, with baseline comparison.
mukul975/Anthropic-Cybersecurity-Skills
Guides a Windows forensic examination of the NTFS Master File Table to recover deleted-file evidence, build timelines and spot timestomping.
mukul975/Anthropic-Cybersecurity-Skills
Detects DNS tunneling, ICMP exfiltration and HTTP-based covert channels in packet captures and DNS logs when hunting for hidden command-and-control traffic.
Categories
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.
Extracting Iocs From Malware Samples fits situations like: threat indicator harvesting; building detection content from a sample.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.