Agent skill

Analyzing Command And Control Communication

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback…

Apache-2.0Auto-check passedSecurity

Install Analyzing Command And Control Communication

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-command-and-control-communication -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-command-and-control-communication --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-command-and-control-communication .claude/skills/analyzing-command-and-control-communication && 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-command-and-control-communication
GitHub stars
34k
Token cost
~3.6k tokens
SKILL.md length
619 words
Files
4 (incl. scripts, references)
Skills in repo
637
Repo updated
First seen
Licence
Apache-2.0

At a glance

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback…

  • Works in 6 steps: Identify the C2 Channel → Analyze Beacon Pattern → Decode C2 Protocol Structure → …
  • Tasks that involve Red teaming and adversary simulation
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls python3; reaches api.shodan.io and virustotal.com; needs VT_API_KEY

What it does

Analyzing Command And Control Communication is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback domains, dead drops). Use after reverse engineering reveals network traffic needing protocol analysis or when building detection signatures for a framework like Cobalt Strike, Metasploit, or Sliver.

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, covering Red teaming and adversary simulation, Reverse engineering and malware and Penetration testing. It works with Metasploit. 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

  • Tasks that involve Red teaming and adversary simulation
  • Tasks that involve Reverse engineering and malware
  • Tasks that involve Penetration testing

Example prompts

  • “Use the analyzing-command-and-control-communication skill to analyz malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to…”
  • “/analyzing-command-and-control-communication”

Requirements

  • Python 3
  • A credential in VT_API_KEY

Workflow steps

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

  1. Identify the C2 Channel
  2. Analyze Beacon Pattern
  3. Decode C2 Protocol Structure
  4. Identify C2 Framework
  5. Map C2 Infrastructure
  6. Create Network Detection Signatures

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:

    • api.shodan.io
    • 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

Analyzing Command And Control Communication loads about 3.6k tokens when it runs, and up to ~4.3k if it reads all its reference files. Until then it costs about 110 tokens; SKILL.md has 619 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~110
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
~4.3k

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). 619 words, ~3,591 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-command-and-control-communication/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyzing-command-and-control-communication
description
Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback domains, dead drops). Use after reverse engineering reveals network traffic needing protocol analysis or when building detection signatures for a framework like Cobalt Strike, Metasploit, or Sliver.
domain
cybersecurity
subdomain
malware-analysis
tags
malware, C2, command-and-control, beacon, protocol-analysis
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
T1071.001, T1573, T1571, T1008, T1095

Analyzing Command-and-Control Communication

When to Use

  • Reverse engineering a malware sample has revealed network communication that needs protocol analysis
  • Building network-level detection signatures for a specific C2 framework (Cobalt Strike, Metasploit, Sliver)
  • Mapping C2 infrastructure including primary servers, fallback domains, and dead drops
  • Analyzing encrypted or encoded C2 traffic to understand the command set and data format
  • Attributing malware to a threat actor based on C2 infrastructure patterns and tooling

Do not use for general network anomaly detection; this is specifically for understanding known or suspected C2 protocols from malware analysis.

Prerequisites

  • PCAP capture of malware network traffic (from sandbox, network tap, or full packet capture)
  • Wireshark/tshark for packet-level analysis
  • Reverse engineering tools (Ghidra, dnSpy) for understanding C2 code in the malware binary
  • Python 3.8+ with scapy, dpkt, and requests for protocol analysis and replay
  • Threat intelligence databases for C2 infrastructure correlation (VirusTotal, Shodan, Censys)
  • JA3/JA3S fingerprint databases for TLS-based C2 identification

Workflow

Step 1: Identify the C2 Channel

Determine the protocol and transport used for C2 communication:

C2 Communication Channels:
━━━━━━━━━━━━━━━━━━━━━━━━━
HTTP/HTTPS:     Most common; uses standard web traffic to blend in
                Indicators: Regular POST/GET requests, specific URI patterns, custom headers

DNS:            Tunneling data through DNS queries and responses
                Indicators: High-volume TXT queries, long subdomain names, high entropy

Custom TCP/UDP: Proprietary binary protocol on non-standard port
                Indicators: Non-HTTP traffic on high ports, unknown protocol

ICMP:           Data encoded in ICMP echo/reply payloads
                Indicators: ICMP packets with large or non-standard payloads

WebSocket:      Persistent bidirectional connection for real-time C2
                Indicators: WebSocket upgrade followed by binary frames

Cloud Services: Using legitimate APIs (Telegram, Discord, Slack, GitHub)
                Indicators: API calls to cloud services from unexpected processes

Email:          SMTP/IMAP for C2 commands and data exfiltration
                Indicators: Automated email operations from non-email processes
Step 2: Analyze Beacon Pattern

Characterize the periodic communication pattern:

python
from scapy.all import rdpcap, IP, TCP
from collections import defaultdict
import statistics
import json

packets = rdpcap("c2_traffic.pcap")

# Group TCP SYN packets by destination
connections = defaultdict(list)
for pkt in packets:
    if IP in pkt and TCP in pkt and (pkt[TCP].flags & 0x02):
        key = f"{pkt[IP].dst}:{pkt[TCP].dport}"
        connections[key].append(float(pkt.time))

# Analyze each destination for beaconing
for dst, times in sorted(connections.items()):
    if len(times) < 3:
        continue

    intervals = [times[i+1] - times[i] for i in range(len(times)-1)]
    avg_interval = statistics.mean(intervals)
    stdev = statistics.stdev(intervals) if len(intervals) > 1 else 0
    jitter_pct = (stdev / avg_interval * 100) if avg_interval > 0 else 0
    duration = times[-1] - times[0]

    beacon_data = {
        "destination": dst,
        "connections": len(times),
        "duration_seconds": round(duration, 1),
        "avg_interval_seconds": round(avg_interval, 1),
        "stdev_seconds": round(stdev, 1),
        "jitter_percent": round(jitter_pct, 1),
        "is_beacon": 5 < avg_interval < 7200 and jitter_pct < 25,
    }

    if beacon_data["is_beacon"]:
        print(f"[!] BEACON DETECTED: {dst}")
        print(f"    Interval: {avg_interval:.0f}s +/- {stdev:.0f}s ({jitter_pct:.0f}% jitter)")
        print(f"    Sessions: {len(times)} over {duration:.0f}s")
Step 3: Decode C2 Protocol Structure

Reverse engineer the message format from captured traffic:

python
# HTTP-based C2 protocol analysis
import dpkt
import base64

with open("c2_traffic.pcap", "rb") as f:
    pcap = dpkt.pcap.Reader(f)

for ts, buf in pcap:
    eth = dpkt.ethernet.Ethernet(buf)
    if not isinstance(eth.data, dpkt.ip.IP):
        continue
    ip = eth.data
    if not isinstance(ip.data, dpkt.tcp.TCP):
        continue
    tcp = ip.data

    if tcp.dport == 80 or tcp.dport == 443:
        if len(tcp.data) > 0:
            try:
                http = dpkt.http.Request(tcp.data)
                print(f"\n--- C2 REQUEST ---")
                print(f"Method: {http.method}")
                print(f"URI: {http.uri}")
                print(f"Headers: {dict(http.headers)}")
                if http.body:
                    print(f"Body ({len(http.body)} bytes):")
                    # Try Base64 decode
                    try:
                        decoded = base64.b64decode(http.body)
                        print(f"  Decoded: {decoded[:200]}")
                    except:
                        print(f"  Raw: {http.body[:200]}")
            except:
                pass
Step 4: Identify C2 Framework

Match observed patterns to known C2 frameworks:

Known C2 Framework Signatures:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Cobalt Strike:
  - Default URIs: /pixel, /submit.php, /___utm.gif, /ca, /dpixel
  - Malleable C2 profiles customize all traffic characteristics
  - JA3: varies by profile, catalog at ja3er.com
  - Watermark in beacon config (unique per license)
  - Config extraction: use CobaltStrikeParser or 1768.py

Metasploit/Meterpreter:
  - Default staging URI patterns: random 4-char checksum
  - Reverse HTTP(S) handler patterns
  - Meterpreter TLV (Type-Length-Value) protocol structure

Sliver:
  - mTLS, HTTP, DNS, WireGuard transport options
  - Protobuf-encoded messages
  - Unique implant ID in communication

Covenant:
  - .NET-based C2 framework
  - HTTP with customizable profiles
  - Task-based command execution

PoshC2:
  - PowerShell/C# based
  - HTTP with encrypted payloads
  - Cookie-based session management
bash
# Extract Cobalt Strike beacon configuration from PCAP or sample
python3 << 'PYEOF'
# Using CobaltStrikeParser (pip install cobalt-strike-parser)
from cobalt_strike_parser import BeaconConfig

try:
    config = BeaconConfig.from_file("suspect.exe")
    print("Cobalt Strike Beacon Configuration:")
    for key, value in config.items():
        print(f"  {key}: {value}")
except Exception as e:
    print(f"Not a Cobalt Strike beacon or parse error: {e}")
PYEOF
Step 5: Map C2 Infrastructure

Document the full C2 infrastructure and failover mechanisms:

python
# Infrastructure mapping
import requests
import json

c2_indicators = {
    "primary_c2": "185.220.101.42",
    "domains": ["update.malicious.com", "backup.evil.net"],
    "ports": [443, 8443],
    "failover_dns": ["ns1.malicious-dns.com"],
}

# Enrich with Shodan
def shodan_lookup(ip, api_key):
    resp = requests.get(f"https://api.shodan.io/shodan/host/{ip}?key={api_key}")
    if resp.status_code == 200:
        data = resp.json()
        return {
            "ip": ip,
            "ports": data.get("ports", []),
            "os": data.get("os"),
            "org": data.get("org"),
            "asn": data.get("asn"),
            "country": data.get("country_code"),
            "hostnames": data.get("hostnames", []),
            "last_update": data.get("last_update"),
        }
    return None

# Enrich with passive DNS
def pdns_lookup(domain):
    # Using VirusTotal passive DNS
    resp = requests.get(
        f"https://www.virustotal.com/api/v3/domains/{domain}/resolutions",
        headers={"x-apikey": VT_API_KEY}
    )
    if resp.status_code == 200:
        data = resp.json()
        resolutions = []
        for r in data.get("data", []):
            resolutions.append({
                "ip": r["attributes"]["ip_address"],
                "date": r["attributes"]["date"],
            })
        return resolutions
    return []
Step 6: Create Network Detection Signatures

Build detection rules based on analyzed C2 characteristics:

bash
# Suricata rules for the analyzed C2
cat << 'EOF' > c2_detection.rules
# HTTP beacon pattern
alert http $HOME_NET any -> $EXTERNAL_NET any (
    msg:"MALWARE MalwareX C2 HTTP Beacon";
    flow:established,to_server;
    http.method; content:"POST";
    http.uri; content:"/gate.php"; startswith;
    http.header; content:"User-Agent: Mozilla/5.0 (compatible; MSIE 10.0)";
    threshold:type threshold, track by_src, count 5, seconds 600;
    sid:9000010; rev:1;
)

# JA3 fingerprint match
alert tls $HOME_NET any -> $EXTERNAL_NET any (
    msg:"MALWARE MalwareX TLS JA3 Fingerprint";
    ja3.hash; content:"a0e9f5d64349fb13191bc781f81f42e1";
    sid:9000011; rev:1;
)

# DNS beacon detection (high-entropy subdomain)
alert dns $HOME_NET any -> any any (
    msg:"MALWARE Suspected DNS C2 Tunneling";
    dns.query; pcre:"/^[a-z0-9]{20,}\./";
    threshold:type threshold, track by_src, count 10, seconds 60;
    sid:9000012; rev:1;
)

# Certificate-based detection
alert tls $HOME_NET any -> $EXTERNAL_NET any (
    msg:"MALWARE MalwareX Self-Signed C2 Certificate";
    tls.cert_subject; content:"CN=update.malicious.com";
    sid:9000013; rev:1;
)
EOF

Key Concepts

TermDefinition
BeaconingPeriodic check-in communication from malware to C2 server at regular intervals, often with jitter to avoid pattern detection
JitterRandomization applied to beacon interval (e.g., 60s +/- 15%) to make the timing pattern less predictable and harder to detect
Malleable C2Cobalt Strike feature allowing operators to customize all aspects of C2 traffic (URIs, headers, encoding) to mimic legitimate services
Dead DropIntermediate location (paste site, cloud storage, social media) where C2 commands are posted for the malware to retrieve
Domain FrontingUsing a trusted CDN domain in the TLS SNI while routing to a different backend, making C2 traffic appear to go to a legitimate service
Fast FluxRapidly changing DNS records for C2 domains to distribute across many IPs and resist takedown efforts
C2 FrameworkSoftware toolkit providing C2 server, implant generator, and operator interface (Cobalt Strike, Metasploit, Sliver, Covenant)
Show full SKILL.md (242 more words)Show less

Tools & Systems

  • Wireshark: Packet analyzer for detailed C2 protocol analysis at the packet level
  • RITA (Real Intelligence Threat Analytics): Open-source tool analyzing Zeek logs for beacon detection and DNS tunneling
  • CobaltStrikeParser: Tool extracting Cobalt Strike beacon configuration from samples and memory dumps
  • JA3/JA3S: TLS fingerprinting method for identifying C2 frameworks by their TLS implementation characteristics
  • Shodan/Censys: Internet scanning platforms for mapping C2 infrastructure and identifying related servers

Common Scenarios

Scenario: Reverse Engineering a Custom C2 Protocol

Context: A malware sample communicates with its C2 server using an unknown binary protocol over TCP port 8443. The protocol needs to be decoded to understand the command set and build detection signatures.

Approach:

  1. Filter PCAP for TCP port 8443 conversations and extract the TCP streams
  2. Analyze the first few exchanges to identify the handshake/authentication mechanism
  3. Map the message structure (length prefix, type field, payload encoding)
  4. Cross-reference with Ghidra disassembly of the send/receive functions in the malware
  5. Identify the command dispatcher and document each command code's function
  6. Build a protocol decoder in Python for ongoing traffic analysis
  7. Create Suricata rules matching the protocol handshake or static header bytes

Pitfalls:

  • Assuming the protocol is static; some C2 frameworks negotiate encryption during the handshake
  • Not capturing enough traffic to see all command types (some commands are rare)
  • Missing fallback C2 channels (DNS, ICMP) that activate when the primary channel fails
  • Confusing encrypted payload data with the protocol framing structure

Output Format

C2 COMMUNICATION ANALYSIS REPORT
===================================
Sample:           malware.exe (SHA-256: e3b0c44...)
C2 Framework:     Cobalt Strike 4.9

BEACON CONFIGURATION
C2 Server:        hxxps://185.220.101[.]42/updates
Beacon Type:      HTTPS (reverse)
Sleep:            60 seconds
Jitter:           15%
User-Agent:       Mozilla/5.0 (Windows NT 10.0; Win64; x64)
URI (GET):        /dpixel
URI (POST):       /submit.php
Watermark:        1234567890

PROTOCOL ANALYSIS
Transport:        HTTPS (TLS 1.2)
JA3 Hash:         a0e9f5d64349fb13191bc781f81f42e1
Certificate:      CN=Microsoft Update (self-signed)
Encoding:         Base64 with XOR key 0x69
Command Format:   [4B length][4B command_id][payload]

COMMAND SET
0x01 - Sleep          Change beacon interval
0x02 - Shell          Execute cmd.exe command
0x03 - Download       Transfer file from C2
0x04 - Upload         Exfiltrate file to C2
0x05 - Inject         Process injection
0x06 - Keylog         Start keylogger
0x07 - Screenshot     Capture screen

INFRASTRUCTURE
Primary:          185.220.101[.]42 (AS12345, Hosting Co, NL)
Failover:         91.215.85[.]17 (AS67890, VPS Provider, RU)
DNS:              update.malicious[.]com -> 185.220.101[.]42
Registrar:        NameCheap
Registration:     2025-09-01

DETECTION SIGNATURES
SID 9000010:      HTTP beacon pattern
SID 9000011:      JA3 TLS fingerprint
SID 9000013:      C2 certificate match

© 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-command-and-control-communication 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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Works with

Categories

Questions about Analyzing Command And Control Communication

What does Analyzing Command And Control Communication do?

Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback…. Analyzing Command And Control Communication is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyzes malware C2 communication over HTTP, HTTPS, DNS, and custom protocols to reverse-engineer beacon patterns, command structures, data encoding, and infrastructure (primary servers, fallback domains, dead drops).

When should I use Analyzing Command And Control Communication?

Analyzing Command And Control Communication fits situations like: tasks that involve Red teaming and adversary simulation; tasks that involve Reverse engineering and malware; tasks that involve Penetration testing.

How do I install Analyzing Command And Control Communication in Claude Code?

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

How do I install Analyzing Command And Control Communication in Codex?

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

Can I use Analyzing Command And Control Communication 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-command-and-control-communication -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-command-and-control-communication, .gemini/skills/analyzing-command-and-control-communication, .github/skills/analyzing-command-and-control-communication and .opencode/skills/analyzing-command-and-control-communication in your project.

What does Analyzing Command And Control Communication need to run?

Going by SKILL.md and its folder, Analyzing Command And Control Communication 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 Analyzing Command And Control Communication access the network?

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

Is Analyzing Command And Control Communication 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 Command And Control Communication use?

Analyzing Command And Control Communication 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 Command And Control Communication 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 689 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Command And Control Communication?

Skills that share tags, products or a category with Analyzing Command And Control Communication: Add Community Skill (samugit83/redamon, 3k stars), Metasploit (Encod3d-Sec/TORCH, 329 stars), Incident Response Network (LeoYeAI/openclaw-master-skills, 2.2k stars) and Client Request Signature Reversal (awarexone/Agentic-Bug-Hunter, 5.3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Command And Control Communication?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,922 GitHub stars. The repository holds 637 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.