Incident Response Network
LeoYeAI/openclaw-master-skills
Network forensics evidence collection and analysis during security incidents.
Agent skill
Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement…
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-traffic-of-malware -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-network-traffic-of-malware --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/analyzing-network-traffic-of-malware .claude/skills/analyzing-network-traffic-of-malware && 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 "analyzing-network-traffic-of-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-network-traffic-of-malware into .claude/skills/analyzing-network-traffic-of-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-network-traffic-of-malware", 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/analyzing-network-traffic-of-malwareType 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 analyzing-network-traffic-of-malware -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-network-traffic-of-malware --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/analyzing-network-traffic-of-malware .agents/skills/analyzing-network-traffic-of-malware && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "analyzing-network-traffic-of-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-network-traffic-of-malware into .agents/skills/analyzing-network-traffic-of-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-network-traffic-of-malware", 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 analyzing-network-traffic-of-malware -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-network-traffic-of-malware --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/analyzing-network-traffic-of-malware .cursor/skills/analyzing-network-traffic-of-malware && 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 "analyzing-network-traffic-of-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-network-traffic-of-malware into .cursor/skills/analyzing-network-traffic-of-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-network-traffic-of-malware", 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/analyzing-network-traffic-of-malware--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 analyzing-network-traffic-of-malware -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-network-traffic-of-malware --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/analyzing-network-traffic-of-malware .gemini/skills/analyzing-network-traffic-of-malware && 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 "analyzing-network-traffic-of-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-network-traffic-of-malware into .gemini/skills/analyzing-network-traffic-of-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-network-traffic-of-malware", 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 analyzing-network-traffic-of-malwareInstalls 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 analyzing-network-traffic-of-malware -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/analyzing-network-traffic-of-malware .github/skills/analyzing-network-traffic-of-malware && 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 "analyzing-network-traffic-of-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-network-traffic-of-malware into .github/skills/analyzing-network-traffic-of-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-network-traffic-of-malware", 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 analyzing-network-traffic-of-malware -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 analyzing-network-traffic-of-malware --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/analyzing-network-traffic-of-malware .opencode/skills/analyzing-network-traffic-of-malware && 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 "analyzing-network-traffic-of-malware" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/analyzing-network-traffic-of-malware into .opencode/skills/analyzing-network-traffic-of-malware/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "analyzing-network-traffic-of-malware", 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.
analyzing-network-traffic-of-malwareAnalyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement…
Analyzing Network Traffic Of Malware is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata. Activates for requests involving malware network analysis, C2 traffic decoding, malware PCAP analysis, or network-based malware detection.
Its SKILL.md is about 3k 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, covering Red teaming and adversary simulation, Network security and Incident response. It works with Wireshark. 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.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Analyzing Network Traffic Of Malware loads about 3k tokens when it runs, and up to ~3.7k if it reads all its reference files. Until then it costs about 103 tokens; SKILL.md has 598 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). 598 words, ~2,961 tokens.
.claude/skills/analyzing-network-traffic-of-malware/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 host-based analysis of malware behavior; use Cuckoo sandbox reports or Volatility memory analysis for process-level activity.
scapy and dpkt for programmatic packet analysisGet a high-level understanding of the network traffic:
# Capture statistics
capinfos malware.pcap
# Protocol hierarchy
tshark -r malware.pcap -q -z io,phs
# Endpoint statistics (top talkers)
tshark -r malware.pcap -q -z endpoints,ip
# Conversation statistics
tshark -r malware.pcap -q -z conv,tcp
# DNS query summary
tshark -r malware.pcap -q -z dns,treeExamine DNS queries for DGA, tunneling, or C2 domain resolution:
# Extract all DNS queries
tshark -r malware.pcap -T fields -e frame.time -e dns.qry.name -e dns.a \
-Y "dns.flags.response == 1" | sort
# Detect DGA patterns (high entropy domain names)
python3 << 'PYEOF'
import math
from collections import Counter
def entropy(s):
p = [n/len(s) for n in Counter(s).values()]
return -sum(pi * math.log2(pi) for pi in p if pi > 0)
# Parse DNS queries from tshark output
import subprocess
result = subprocess.run(
["tshark", "-r", "malware.pcap", "-T", "fields", "-e", "dns.qry.name",
"-Y", "dns.flags.response == 0"],
capture_output=True, text=True
)
domains = set(result.stdout.strip().split('\n'))
print("Suspicious DNS queries (high entropy):")
for domain in domains:
if domain:
subdomain = domain.split('.')[0]
ent = entropy(subdomain)
if ent > 3.5 and len(subdomain) > 10:
print(f" {domain} (entropy: {ent:.2f})")
PYEOF
# Detect DNS tunneling (large TXT responses)
tshark -r malware.pcap -T fields -e dns.qry.name -e dns.txt \
-Y "dns.resp.type == 16 and dns.resp.len > 100"Examine web-based command-and-control traffic:
# Extract HTTP requests
tshark -r malware.pcap -T fields \
-e frame.time -e ip.src -e ip.dst -e http.host \
-e http.request.method -e http.request.uri -e http.user_agent \
-Y "http.request"
# Extract HTTP response bodies (potential payload downloads)
tshark -r malware.pcap -T fields \
-e http.host -e http.request.uri -e http.content_type -e tcp.len \
-Y "http.response and tcp.len > 1000"
# Extract POST data (potential exfiltration)
tshark -r malware.pcap -T fields \
-e http.host -e http.request.uri -e http.file_data \
-Y "http.request.method == POST"
# TLS analysis (SNI, JA3 fingerprints)
tshark -r malware.pcap -T fields \
-e tls.handshake.extensions_server_name \
-e tls.handshake.ja3 \
-Y "tls.handshake.type == 1"
# Extract TLS certificate details
tshark -r malware.pcap -T fields \
-e x509ce.dNSName -e x509af.serialNumber \
-e x509sat.utf8String \
-Y "tls.handshake.type == 11"
# Export HTTP objects (downloaded files)
tshark -r malware.pcap --export-objects http,exported_files/Identify regular periodic communication indicating C2 beaconing:
# Beacon detection from PCAP
from scapy.all import rdpcap, IP, TCP
from collections import defaultdict
import statistics
packets = rdpcap("malware.pcap")
# Group connections by destination IP:port
connections = defaultdict(list)
for pkt in packets:
if IP in pkt and TCP in pkt:
if pkt[TCP].flags & 0x02: # SYN flag
dst = f"{pkt[IP].dst}:{pkt[TCP].dport}"
connections[dst].append(float(pkt.time))
# Analyze timing intervals for beaconing
print("Beacon Analysis:")
for dst, times in connections.items():
if len(times) >= 5:
intervals = [times[i+1] - times[i] for i in range(len(times)-1)]
avg = statistics.mean(intervals)
stdev = statistics.stdev(intervals) if len(intervals) > 1 else 0
jitter = (stdev / avg * 100) if avg > 0 else 0
if 10 < avg < 3600 and jitter < 30: # Regular interval with < 30% jitter
print(f" [!] {dst}: {len(times)} connections")
print(f" Interval: {avg:.1f}s ± {stdev:.1f}s (jitter: {jitter:.1f}%)")
print(f" Pattern: LIKELY BEACONING")Create Suricata/Snort rules from observed traffic patterns:
# Run Suricata against the PCAP for existing signature matches
suricata -r malware.pcap -l suricata_output/ -c /etc/suricata/suricata.yaml
# Review alerts
cat suricata_output/fast.log
# Create custom Suricata rule from observed patterns
cat << 'EOF' > custom_malware.rules
# C2 beacon detection based on observed URI pattern
alert http $HOME_NET any -> $EXTERNAL_NET any (
msg:"MALWARE MalwareX C2 Beacon";
flow:established,to_server;
http.method; content:"POST";
http.uri; content:"/gate.php?id=";
http.user_agent; content:"Mozilla/5.0 (compatible; MSIE 10.0)";
sid:9000001; rev:1;
)
# DNS query for known C2 domain
alert dns $HOME_NET any -> any any (
msg:"MALWARE MalwareX C2 DNS Query";
dns.query; content:"update.malicious.com";
sid:9000002; rev:1;
)
# JA3 hash match for malware TLS client
alert tls $HOME_NET any -> $EXTERNAL_NET any (
msg:"MALWARE MalwareX JA3 Match";
ja3.hash; content:"a0e9f5d64349fb13191bc781f81f42e1";
sid:9000003; rev:1;
)
EOFRecover transferred files and embedded data:
# Extract files using Zeek
zeek -r malware.pcap /opt/zeek/share/zeek/policy/frameworks/files/extract-all-files.zeek
ls extract_files/
# Extract files using NetworkMiner (GUI)
# Or use tshark for specific protocol exports
tshark -r malware.pcap --export-objects http,http_objects/
tshark -r malware.pcap --export-objects smb,smb_objects/
tshark -r malware.pcap --export-objects tftp,tftp_objects/
# Hash all extracted files
sha256sum http_objects/* smb_objects/* 2>/dev/null
# Generate Zeek logs for comprehensive metadata
zeek -r malware.pcap
# Output: conn.log, dns.log, http.log, ssl.log, files.log, etc.| Term | Definition |
|---|---|
| Beaconing | Regular periodic connections from malware to C2 server, identifiable by consistent time intervals and packet sizes |
| JA3/JA3S | TLS fingerprinting method creating a hash from ClientHello/ServerHello parameters to uniquely identify malware TLS implementations |
| DGA (Domain Generation Algorithm) | Algorithm generating pseudo-random domain names that malware queries to locate C2 servers, evading static domain blocklists |
| DNS Tunneling | Encoding data in DNS queries and responses to establish a C2 channel or exfiltrate data through DNS infrastructure |
| Fast Flux | DNS technique rapidly rotating IP addresses for a domain to avoid takedown and distribute C2 across many compromised hosts |
| SNI (Server Name Indication) | TLS extension revealing the hostname the client is connecting to; visible even in encrypted HTTPS connections |
| Network Signature | Suricata/Snort rule matching specific patterns in network traffic (headers, payloads, timing) to detect malicious communications |
Context: Malware communicates with its C2 server using a custom binary protocol over TCP port 8443. Standard HTTP analysis yields no results. The protocol structure needs to be reverse engineered from the PCAP.
Approach:
Pitfalls:
MALWARE NETWORK TRAFFIC ANALYSIS
===================================
PCAP File: malware_sandbox.pcap
Duration: 300 seconds
Total Packets: 12,847
Total Bytes: 4.2 MB
DNS ACTIVITY
Total Queries: 47
DGA Detected: Yes (23 high-entropy queries to .com TLD)
Tunneling: No
Resolved C2: update.malicious[.]com -> 185.220.101[.]42
C2 COMMUNICATION
Protocol: HTTPS (TLS 1.2)
Server: 185.220.101[.]42:443
SNI: update.malicious[.]com
JA3 Hash: a0e9f5d64349fb13191bc781f81f42e1
Beacon Interval: 60.2s ± 6.8s (11.3% jitter)
Total Sessions: 237
Data Sent: 147 MB
Data Received: 2.3 MB
Certificate: CN=update.malicious[.]com (self-signed, expired)
PAYLOAD DOWNLOADS
GET /payload.dll from compromised-site[.]com
Size: 98,304 bytes
SHA-256: abc123def456...
Content-Type: application/octet-stream
EXFILTRATION
Method: HTTPS POST to /gate.php
Content-Type: application/octet-stream
Average Size: 15,432 bytes per request
Total Volume: 147 MB over 4 hours
SURICATA ALERTS
[1:2028401] ET MALWARE Generic C2 Beacon Pattern
[1:2028500] ET POLICY Self-Signed Certificate
GENERATED SIGNATURES
SID 9000001: MalwareX HTTP beacon pattern
SID 9000002: MalwareX DNS C2 domain
SID 9000003: MalwareX JA3 TLS fingerprint© 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/analyzing-network-traffic-of-malware of mukul975/Anthropic-Cybersecurity-Skills.
Open the folder on GitHubat commit 54a7988
Analyzing Network Traffic Of Malware 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 |
|---|---|---|---|---|---|---|
| Analyzing Network Traffic Of Malware this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Incident Response NetworkLeoYeAI/openclaw-master-skills | 2.2k | — | ~5k | Automated safety check: Pass | Apache-2.0 | |
| Protocol Reverse Engineeringwshobson/agents | 40k | 8 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Traffic Analysis Pcapyaklang/hack-skills | 2.4k | — | ~2.8k | Automated safety check: Notes | MIT | |
| Azure Kusto Irqlmicrosoft/GitHub-Copilot-for-Azure | 255 | — | ~2.6k | Automated safety check: Pass | MIT | |
| Find Cybersecurity Firmjeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~3.7k | Automated safety check: Notes | MIT |
LeoYeAI/openclaw-master-skills
Network forensics evidence collection and analysis during security incidents.
wshobson/agents
Master network protocol reverse engineering including packet analysis, protocol dissection, and custom protocol documentation.
yaklang/hack-skills
Traffic analysis and PCAP forensics playbook. An agent skill from yaklang/hack-skills.
microsoft/GitHub-Copilot-for-Azure
Compose IRQL (Incident Response Query Language) queries for Kusto cybersecurity investigations.
jeremylongshore/tons-of-skills-marketplace
A skill your agent uses whenever the user wants to find, shortlist, vet, or enrich US cybersecurity firms — pen-testing/red team, security audits, vCISO, SOC 2 readiness, incident response, managed…
dslsdzc/rev-skills
物联网协议:MQTT/CoAP/BLE/Zigbee;BLE 链路层(广播解析/配对加密)与 NFC/智能卡(ISO14443/APDU/MIFARE)。
mukul975/Anthropic-Cybersecurity-Skills
Weighs infrastructure, TTP, malware code and timing evidence with the Diamond Model and competing hypotheses to reach a confidence-rated attribution.
mukul975/Anthropic-Cybersecurity-Skills
Walks through reverse engineering Go-compiled malware in Ghidra: parsing buildinfo and pclntab, recovering stripped function names and extracting dependencies.
mukul975/Anthropic-Cybersecurity-Skills
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.
Works with
Categories
Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement…. Analyzing Network Traffic Of Malware is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyzes network traffic generated by malware during sandbox execution or live incident response to identify C2 protocols, data exfiltration channels, payload downloads, and lateral movement patterns using Wireshark, Zeek, and Suricata.
Analyzing Network Traffic Of Malware fits situations like: tasks that involve Red teaming and adversary simulation; tasks that involve Network security; tasks that involve Incident response.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-traffic-of-malware -a claude-code`. Or copy the skill folder (skills/analyzing-network-traffic-of-malware in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/analyzing-network-traffic-of-malware in your project. Claude Code loads it when a task matches its description.
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-network-traffic-of-malware -a codex`. Or copy the skill folder (skills/analyzing-network-traffic-of-malware in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/analyzing-network-traffic-of-malware 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 analyzing-network-traffic-of-malware -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-network-traffic-of-malware, .gemini/skills/analyzing-network-traffic-of-malware, .github/skills/analyzing-network-traffic-of-malware and .opencode/skills/analyzing-network-traffic-of-malware in your project.
Going by SKILL.md and its folder, Analyzing Network Traffic Of Malware needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. 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.
Analyzing Network Traffic Of Malware 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 3k tokens (SKILL.md is roughly 12k 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.
Skills that share tags, products or a category with Analyzing Network Traffic Of Malware: Incident Response Network (LeoYeAI/openclaw-master-skills, 2.2k stars), Protocol Reverse Engineering (wshobson/agents, 40k stars), Traffic Analysis Pcap (yaklang/hack-skills, 2.4k stars) and Azure Kusto Irql (microsoft/GitHub-Copilot-for-Azure, 255 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 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.