Claude-Code-CyberSecurity-Skill is a collection of 22 SKILL.md instruction packs that turn Claude Code into a methodology-driven co-pilot for authorized offensive, defensive, and compliance work.
| Tool | Masriyan/Claude-Code-CyberSecurity-Skill — 22 production-quality Claude Code Skills spanning offensive security, defense, RE, CTI, purple teaming, and GRC |
| Category | AI-assisted security workflow / prompt-skill framework (Python 3.10+, MIT) |
| Primary Use | Installing structured SKILL.md methodologies into ~/.claude/skills/ so Claude Code produces expert-grade recon, malware analysis, YARA/Sigma rule generation, and reporting artifacts |
| Safe Use | Intended for authorized penetration tests, internal labs, SOC/IR teams, and compliance functions; offensive skills ship built-in authorization gates that prompt Claude to confirm legal scope before proceeding |
| Telemetry Note | Purely local instruction files and Python scripts — no network callbacks described in the README; note the repo's own AV advisory that payload string templates may trigger engine detections, so egress/DLP monitoring of any generated artifacts is the defender's concern |
Masriyan/Claude-Code-CyberSecurity-Skill takes a different angle on security automation than most repositories covered here: instead of shipping a scanner or a C2 framework, it ships knowledge. The project contains 22 structured SKILL.md files that get installed into Claude Code's skill directories, transforming the AI coding agent into a domain expert for tasks ranging from subdomain enumeration methodology to STIX bundling and OT/ICS analysis. At roughly 419 stars, version 3.3.0, MIT-licensed and Python 3.10+, it is one of the more serious attempts to formalize how large language models participate in real security operations rather than casual chat about them.
The mechanics are worth understanding before dismissing this as a prompt collection. Each SKILL.md combines YAML frontmatter (name, description, tags), explicit activation triggers that tell Claude when the skill applies, a step-by-step methodology section, output templates that constrain what Claude produces, references to bundled Python automation scripts, and — notably for the offensive skills — authorization gates that force a scope-confirmation prompt before any offensive workflow proceeds. That last element is a deliberate design decision, embedding the rules-of-engagement check into the instruction layer rather than leaving it to operator discipline.
Installation follows the standard Claude Code convention. Skills live either globally in ~/.claude/skills/ or per-project in .claude/skills/, and the README offers a plain copy (cp -r skills/* ~/.claude/skills/), a symlink for development workflows, or targeted project installs. After that, activation is conversational: asking Claude to enumerate subdomains or map TTPs to MITRE ATT&CK triggers the matching skill automatically, or you can invoke one explicitly with a phrase like use the log-analysis skill. There is no daemon, no service, no telemetry — the repository is inert text and scripts until Claude Code reads it.
The breadth of the 22 skills is the headline feature. Offensive coverage includes recon and OSINT (subdomain enumeration, DNS analysis, Google dorking, WHOIS), a vulnerability scanner skill oriented toward dependency auditing and config review with CVSS scoring, and exploit development templates for CTF-style buffer overflow and web payload work. Defensive coverage is arguably stronger: malware analysis with static triage, YARA generation and IOC extraction; threat hunting with ATT&CK mapping and a Sigma plus SIEM query library; incident response built on PICERL playbooks, timeline analysis and memory forensics; and a log-analysis skill targeting Splunk SPL, KQL, and EQL query construction.
Beyond the classic red/blue split, the collection reaches into domains most tooling ignores. A CSOC automation skill covers alert triage, playbook YAML, and shift reporting; a GRC skill maps controls to NIST CSF 2.0, ISO 27001, and SOC 2; and a supply-chain skill handles SBOM generation, typosquatting and dependency-confusion detection, and SLSA/Sigstore provenance checks. There are dedicated skills for AI/LLM security (OWASP LLM Top 10, prompt injection testing, RAG and agent security), mobile (MASVS/MASTG, APK/IPA static analysis, Frida workflows), and OT/ICS with a safety-first methodology referencing the Purdue model, Modbus/DNP3/S7, and IEC 62443.
Internally, the skills leverage what Claude Code already does natively — reading files and logs directly, executing scripts through the Bash tool, analyzing disassembly or PCAP data in context, generating detection rules, and performing web searches for CVE lookups. The skill files act as the methodology layer on top of those capabilities, so a malware-analysis request produces output in a defined report format with IOC tables rather than free-form prose. Roughly 35 Python automation scripts back the skills, and version 3.3 added an offline --demo self-test to every script that asserts actual detection logic, not just CLI startup.
That self-test push is the most interesting engineering signal in the README. The v3.3 changelog describes concrete bug fixes verified by those demos: the ML model scanner now catches dangerous imports in modern STACK_GLOBAL pickles, the APK analyzer reports only genuinely exported components, the IaC scanner stopped false-flagging pinned Docker images, the binary analyzer's Full-RELRO check actually validates BIND_NOW, and the hardening checker no longer counts commented-out SSH directives as a pass. This is the kind of correction work you expect from a maintained detection tool, and it suggests the author is testing claims rather than just writing prompt text.
The repository is also candid about its engineering process. A make check suite (after pip install -r requirements-dev.txt) covers packaging and frontmatter validation, all 35 script CLI interfaces, IOC/STIX regressions, purple-team input validation, and file provenance. The README explicitly states that activation rates and detection-accuracy targets are not yet measured benchmarks — an unusually honest disclaimer that practitioners should weigh when deciding how much to trust generated detections in production workflows.
For authorized use, the collection is best understood as a methodology amplifier for people who already know the domain. A pentester with a signed scope can use the red-team skill for engagement planning and reporting structure; a SOC analyst can have Claude draft Sigma rules or Splunk queries from a hypothesis and then validate them; a compliance lead can run a SOC 2 gap analysis. The built-in authorization gates on offensive skills are a welcome guardrail, but they are prompt-level controls — the actual legal boundary remains the operator's engagement letter and scope document.
Defenders and blue teams should note two things. First, the README carries an antivirus advisory: some engines flag the repo due to security-testing payload string templates, which the author characterizes as false positives comparable to what SecLists, PayloadsAllTheThings, and Metasploit experience, with details in SECURITY.md. Second, because the tool itself is passive local instruction files, its footprint is minimal — the observable artifacts are whatever Claude generates, meaning detection content, reports, and scripts that appear in your environment's working directories.
Where this fits: if your team has already adopted Claude Code for engineering work, this repository is a disciplined way to extend it into security operations without surrendering methodology to ad-hoc prompting. The purple-team skill's detect-tune-validate loop with Atomic Red Team, CALDERA, and coverage measurement via Navigator/DeTT&CT is a good example of the collection's thesis — AI assistance anchored to established frameworks, structured outputs, and now self-tested automation scripts, all under the operator's authorization and review.
Masriyan/Claude-Code-CyberSecurity-Skill.Educational analysis for authorized security professionals. Use only in controlled, authorized environments.
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