AI-Assisted Vulnerability Discovery

AI-Assisted Vulnerability Discovery is an AI Engineering skill for Claude Code, published by xalgord in xalgorix.

852 stars151 forkson xalgord/xalgorixAdded 2026/07/20+1% in starsRepository updated 2026/08/04

ai-agentai-securityautomationautonomous-pentestingbug-bountycybersecurityethical-hackinggolangpenetration-testingpentestpentesting-toolsreconsecuritysecurity-researchsecurity-toolstypescriptvulnerability-detectionvulnerability-scanner

Install in seconds

Install AI-Assisted Vulnerability Discovery

Copy AI-Assisted Vulnerability Discovery into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/xalgorix/xalgorix/tree/main/internal/tools/skills/data/ai-security/performing-ai-assisted-vulnerability-discovery ~/.claude/skills/performing-ai-assisted-vulnerability-discovery

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/xalgorix/xalgorix.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: internal/tools/skills/data/ai-security/performing-ai-assisted-vulnerability-discovery/SKILL.md in xalgord/xalgorix
Installs to: ~/.claude/skills/performing-ai-assisted-vulnerability-discovery
Collection: One of 51 skills cataloged from this repository
Category: AI Engineering — 3670 skills

What AI-Assisted Vulnerability Discovery does

Uses LLMs to generate valid fuzzing seeds, evolve grammars from coverage feedback, and help reproduce and triage crashes in authorized security research. Also supports evidence-based analysis of Burp traffic and report drafting.

AI-Assisted Vulnerability Discovery is cataloged under AI Engineering on DirSkills. AI-Assisted Vulnerability Discovery comes from a repository tagged ai-agent, ai-security, automation, autonomous-pentesting and bug-bounty.

Documentation

README

Excerpt

Performing AI-Assisted Vulnerability Discovery

When to Use

During authorized vulnerability research where complex input formats (SQL, URLs, custom/binary protocols) stall a blind fuzzer
When bootstrapping a coverage-guided fuzzer (AFL++, libFuzzer, Honggfuzz) that needs syntax-valid, security-relevant seeds
When you have crash candidates and need to scale proof-of-vulnerability (PoV) generation across many agents/models
When triaging large volumes of real Burp HTTP traffic and want evidence-driven passive analysis + report drafting
When working under cost/time budgets (bug-bounty, CTF, AIxCC-style cyber reasoning systems)

Critical: Techniques Most Often Missed

This is the opening of the README. Read the full README on GitHub.

Commands AI-Assisted Vulnerability Discovery provides

Slash commands named in this skill’s SKILL.md, listed in the order they first appear.

/mcp
/header

Frequently asked about AI-Assisted Vulnerability Discovery

What else does xalgord publish alongside AI-Assisted Vulnerability Discovery?
AI-Assisted Vulnerability Discovery is one of 51 skills that DirSkills catalogs from xalgord/xalgorix, the repository it ships in. Its siblings there include AFL++ Fuzzing, AI Security Guardrails and API Abuse Detection with Rate Limiting. Each one is a separate skill with its own page in this directory, installs the same way AI-Assisted Vulnerability Discovery does, and is maintained by xalgord in that same repository. The rest of the collection is listed on the xalgord/xalgorix page.
How does AI-Assisted Vulnerability Discovery compare to other AI Engineering skills?
AI-Assisted Vulnerability Discovery ranks #1525 by stars among the 3670 AI Engineering skills in this catalog. The most-starred ones next to it are Architecture Decision Records, AI-First Engineering and Agentic OS. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of AI-Assisted Vulnerability Discovery against them. Open each page to compare what they document and how they install.