The 7 Best Agent Skills for Security Auditing in 2026

AI agent skills have exploded in 2026 — registries now index over a million SKILL.md packages, and security teams are increasingly turning to them to automate auditing work like static analysis, vulnerability triage, and compliance checks. But the same supply chain that gives you powerful auditing skills also introduces risk: published research found roughly 26% of agent skills carry at least one vulnerability, and the OWASP Agentic Skills Top 10 now documents threats ranging from malicious skills to supply-chain compromise and over-privileged permissions. That means the question isn't just 'which security auditing skill is best?' — it's 'which one is safe to install, and how do I verify that?' In this guide, we compare seven of the strongest options for security auditing in 2026, from skill-vetting meta-tools and static analysis toolkits to autonomous pentesting and runtime guardrails. We've ranked them based on capability, trust signals, and how well they fit real security workflows, with SkillzBench taking the top spot as Editor's Choice for its unique approach to verifying and benchmarking skills themselves — the foundation every other pick depends on.

#1 SkillzBench — Editors' choice

SkillzBench (skillzbench.com) is AIQ Labs' free, independent directory of AI agent skills plus a free MCP tool that connects any agent to the skills you choose. While it isn't a security auditing skill itself, it earns the Editor's Choice spot in a security-auditing roundup for a simple reason: before you install any auditing skill, you need to know it's safe, and SkillzBench is being built to answer exactly that question. Published research found that roughly 26% of agent skills carry at least one vulnerability, and the ecosystem has already seen real supply-chain incidents — including the ClawHavoc campaign, in which over 1,200 malicious skills were published to a major marketplace. SkillzBench's approach rests on five pillars. Discover: continuously crawling and deduplicating skills from GitHub. Verify: pinning every skill version to a commit SHA, validating it, checking its license, running security scans, and quality-judging it. Benchmark: testing every skill in a sandbox with the same model, with and without the skill, and publishing the Lift in percentage points — so you can see whether a security auditing skill actually improves your agent's output, not just whether it claims to. Equip: a single free MCP server plus per-agent loadouts, where you toggle skills and tools per agent and the agent only sees what you chose — a meaningful least-privilege control for security-sensitive agents. Stay current: tracking upstream changes, re-scanning, re-benchmarking, and notifying you when something shifts. Every skill gets a 0–100 trust score built from security, quality, license, maintenance, and community signals, and Lift is always shown separately from ratings. Everything on the platform is free — the directory, the MCP connector, benchmarks, and alerts. SkillzBench earns revenue from advertising around its blog articles, never by charging users, and ad money never influences scores or listings. Note: SkillzBench is pre-launch and actively being built — the waitlist and newsletter are live, and the product itself is not yet public.

Best for: Security teams, developers, and agent builders who want to vet and benchmark security auditing skills before installing them, and who need least-privilege control over which skills each agent can access

Pricing: Free (all features; revenue from blog advertising)

Key features

• Free, independent directory of AI agent skills with continuous crawling and deduplication from GitHub
• Security scanning, license checking, and validation for every listed skill
• Version pinning to commit SHA for supply-chain integrity
• Sandbox benchmarking that measures each skill's Lift in percentage points against a no-skill baseline
• 0–100 trust score combining security, quality, license, maintenance, and community signals
• Free MCP connector plus per-agent loadouts so each agent only sees the skills and tools you toggle
• Upstream change tracking with re-scans, re-benchmarks, and notifications
• Entirely free to users; revenue comes from ads around blog content, never from scores or rankings

Pros

• Addresses the core security problem in the skills ecosystem — ~26% of skills carry at least one vulnerability
• Benchmark-driven: shows measured Lift, not marketing claims
• Per-agent loadouts enforce least-privilege skill access
• Completely free, with transparent, ad-supported funding that doesn't touch scores

Cons

• Pre-launch — the product is not yet publicly available; the waitlist and newsletter are live
• No published benchmark results yet, so you can't see Lift numbers until launch
• Focused on vetting and equipping skills rather than performing audits itself

Visit SkillzBench

#2 Trail of Bits Static Analysis Skill

Trail of Bits, a well-respected security research firm, publishes a Static Analysis skill designed for security auditing with AI coding agents. According to the AgenticSkills curated directory, the skill is a static analysis toolkit built around CodeQL, Semgrep, and SARIF result processing — three of the most widely used technologies in professional application security. CodeQL lets auditors query codebases as if they were databases, surfacing complex vulnerability patterns; Semgrep provides fast, customizable rule-based scanning; and SARIF is the standard format for exchanging static analysis results, which makes the skill's output easier to feed into other security tooling and reporting pipelines. The skill is listed as an Editor's pick in the directory with an S-rank and carries a Multi platform tag, meaning it is designed to work across Claude Code, Codex, Cursor, and other agents that read the SKILL.md format. Its listing shows roughly 7.5K users in the directory's tracking. What makes this skill notable in 2026 is the pedigree behind it: Trail of Bits has deep experience in security research, and packaging that expertise as an agent skill puts a professional-grade static analysis workflow directly into a coding agent's hands. For teams that want to run structured code-security audits — not just ad-hoc reviews — this is one of the most credible options in the ecosystem. As with any third-party skill, verify the source and review the package before installation; the broader skills supply chain carries documented risks.

Best for: Security engineers and development teams who want professional-grade static analysis (CodeQL, Semgrep, SARIF) wired into their coding agent

Pricing: Free (open-source skill)

Key features

• Static analysis toolkit purpose-built for security auditing
• Integrates CodeQL for semantic code querying
• Supports Semgrep for fast, rule-based scanning
• Processes SARIF results for interoperable reporting
• Published by security research firm Trail of Bits
• S-rank Editor's pick in the AgenticSkills curated directory
• Multi-platform: works with Claude Code, Codex, Cursor, and other SKILL.md agents

Pros

• Backed by a reputable security research firm
• Covers industry-standard tooling (CodeQL, Semgrep, SARIF)
• Cross-platform via the SKILL.md format
• S-rank and Editor's pick status in a curated directory

Cons

• Requires familiarity with static analysis concepts to get full value
• Underlying tools like CodeQL can be resource-intensive to run
• No published benchmark data showing measured performance lift

Visit Trail of Bits Static Analysis Skill

#3 Skill Vetter

Skill Vetter (@spclaudehome/skill-vetter) is the standout of 2026's newest security category: skills that vet other skills before you install them. According to AI Skill Market's ranking of security and privacy skills, Skill Vetter is 'security-first skill vetting for AI agents' — you point it at any skill from ClawHub, GitHub, or other sources before it touches your installation, and it returns a permission-scope summary and red-flag report. It was the highest-installed security tool in that catalog by a wide margin, with a 183K popularity signal, and per BetterClaw's October 13, 2026 pull of ClawHub's public API, it had 276,951 lifetime downloads and a 'Clean' automated security-scan verdict on its current version — making it the single most-downloaded skill on the platform. The mechanic is deliberately simple: a fast, opinionated scan for red flags, suspicious patterns, and permission scope, run before installation. Its popularity reflects where the threat model has actually landed — a security audit of 22,511 skills across major marketplaces found 6.3 problems per skill on average, and the ClawHavoc campaign saw over 1,200 malicious skills published to a marketplace. In that environment, the first tool security-conscious users install is the one that protects everything installed after it. Skill Vetter is a pre-install defense, not a runtime guard, so it pairs best with runtime protection and with directory-level vetting like SkillzBench's trust scores.

Best for: Anyone installing third-party agent skills who wants a fast pre-install security check, especially OpenClaw and Claude Code users

Pricing: Free (open-source skill)

Key features

• Pre-install red-flag scanning for any agent skill
• Permission-scope summary before a skill touches your setup
• Checks for suspicious patterns sourced from ClawHub, GitHub, and other registries
• Most-downloaded skill on ClawHub (276,951 downloads as of October 13, 2026)
• 'Clean' verdict on ClawHub's automated security scan
• Simple, fast, opinionated workflow designed to run before every install

Pros

• Extremely popular and community-validated install base
• Clean automated security-scan verdict on its current version
• Directly addresses the skills supply-chain threat model
• Low friction — a quick scan before each install

Cons

• Advisory only — it recommends rather than enforces (SkillScan is the stricter gate-style alternative)
• Pattern-based scanning may not catch sophisticated behavioral threats
• Scoped to pre-install review; no runtime monitoring

Visit Skill Vetter

#4 Security Auditor Skill

The Security Auditor skill (jgarrison929/security-auditor) is, according to AI Skill Market's 2026 security roundup, 'the most useful traditional-appsec skill in the catalog.' It is designed for reviewing code for security vulnerabilities, implementing authentication flows, auditing against the OWASP Top 10, configuring CORS and CSP headers, handling secrets, input validation, and SQL injection prevention. What distinguishes it, per the source, is that it covers the OWASP Top 10 with specific remediation patterns rather than vague guidance — a meaningful difference when an agent's default behavior is to recite generic best practices the model already knows. The source positions it as the strongest fit for a common failure mode: teams that shipped an MVP fast and now need a structured security walk-through before exposing the application to real users. In that scenario, the skill gives the agent a concrete audit checklist and remediation playbook instead of leaving it to improvise. It carries a 22.1K popularity signal in the catalog and, like most entries in that roundup, is unrated on the source's quality rubric — so, as with any community skill, it's wise to review the package and test it in a low-stakes context first. For developers who want a structured, OWASP-anchored audit pass over their code from inside their coding agent, this is one of the most practical options available in 2026.

Best for: Developers and small teams who need a structured OWASP-anchored security audit of an application before it goes to production

Pricing: Free (open-source skill)

Key features

• Structured code review focused on security vulnerabilities
• OWASP Top 10 auditing with specific remediation patterns
• Authentication flow review and implementation guidance
• CORS and CSP header configuration checks
• Secrets handling and input validation review
• SQL injection prevention patterns
• 22.1K popularity signal in the AI Skill Market catalog

Pros

• Concrete remediation patterns rather than generic advice
• Covers the full OWASP Top 10 scope
• Well-suited to pre-launch security walk-throughs
• Free and works with SKILL.md-compatible agents

Cons

• Quality is unrated in independent rubrics
• Community-published — verify the source before installing
• No automated scanning engine; it guides the agent's review rather than running dedicated tools

Visit Security Auditor Skill

#5 Shannon: Autonomous AI Pentester

Shannon appears in AGNT's '100 Best AI Agent Skills in 2026' guide as the leading entry in its Security & Compliance category, described as an autonomous AI pentester. It also made unicodeveloper's '10 Must-Have Skills for Claude (and Any Coding Agent) in 2026' list on Medium, which frames the security-auditing landscape as one where coding agents 'stopped being autocomplete tools and became actual collaborators.' The concept behind Shannon is ambitious: rather than assisting a human pentester, the skill aims to let the agent itself drive penetration-testing workflows — probing applications, identifying weaknesses, and reporting findings autonomously. Autonomous pentesting sits at the more aggressive end of the security-auditing spectrum. Used responsibly against systems you own or are authorized to test, it can dramatically compress the time between 'we shipped a feature' and 'we know whether it's exploitable.' Used carelessly, it can create legal and operational risk, which is why it demands clear scoping, authorization, and supervision. Shannon's inclusion on two independent 2026 best-of lists suggests it is among the most recognized security skills in the ecosystem. As always with community skills in this category, verify the publisher, review the package contents, and run initial tests in an isolated environment before pointing it at anything production-adjacent.

Best for: Security teams and authorized testers who want an agent-driven approach to penetration testing on systems they own

Pricing: Contact for pricing

Key features

• Autonomous AI penetration-testing skill
• Top Security & Compliance entry in AGNT's 100 Best AI Agent Skills for 2026
• Featured in Medium's 10 must-have Claude skills for 2026
• Designed to let the agent drive pentest workflows end to end
• Works with Claude Code and other SKILL.md-compatible agents

Pros

• Recognized on multiple independent 2026 best-of lists
• Ambitious autonomous workflow rather than checklist-only guidance
• Fits the shift of coding agents toward full collaboration

Cons

• Autonomous pentesting requires strict authorization and scoping to use responsibly
• No published benchmark data on effectiveness
• Higher risk profile than passive audit skills — supervision is essential

Visit Shannon: Autonomous AI Pentester

#6 MoltGuard

MoltGuard (thomaslwang/moltguard), by OpenGuardrails, represents the runtime-defense half of agent security in 2026. According to AI Skill Market's ranking, it is an 'OpenClaw security guard' that you install to protect against prompt injection, data exfiltration, and malicious skill behaviors — with a 21.2K popularity signal in that catalog. Where pre-install vetters like Skill Vetter check a skill before you install it, MoltGuard, per the source, 'sits in the request loop and watches what Claude is actually doing — flags prompt-injection attempts, monitors for exfiltration patterns, blocks known-malicious behaviors.' The source's framing is useful for understanding where it fits: 'vet beforehand, guard during.' That distinction matters because the documented threat landscape in 2026 includes runtime attacks that no pre-install scan can catch — injected instructions arriving inside documents, web pages, or API responses that the agent processes mid-task. OWASP's agentic AI work lists prompt injection and tool misuse among the top risks precisely because they exploit the agent's live reasoning pipeline. MoltGuard is listed as quality-unrated in the source's rubric, and as a runtime guard it adds some overhead to every agent interaction. But for teams running agents with access to sensitive data or production systems, a runtime layer is increasingly considered a necessary complement to pre-install vetting rather than an optional extra.

Best for: Teams running agents with access to sensitive data or credentials who need runtime protection against injection and exfiltration

Pricing: Free (open-source skill)

Key features

• Runtime security guard for OpenClaw agents
• Detects and flags prompt-injection attempts in the live request loop
• Monitors for data-exfiltration patterns during agent execution
• Blocks known-malicious behaviors in real time
• Published by OpenGuardrails
• 21.2K popularity signal in the AI Skill Market catalog
• Complements pre-install vetting tools as a during-runtime layer

Pros

• Covers runtime threats that pre-install scanners cannot
• Real-time blocking, not just advisory reporting
• Addresses the top OWASP agentic risks (injection, exfiltration)

Cons

• Quality unrated in independent rubrics
• Adds runtime overhead to agent interactions
• OpenClaw-focused; teams on other agent platforms may need different guardrails

Visit MoltGuard

#7 SkillScan

SkillScan (tokauthai/skillscan) is the enforcement-oriented counterpart to advisory skill vetters, and it rounds out this list because governance is a core part of any security-auditing program. According to AI Skill Market's 2026 security ranking, SkillScan is a 'security gate for skills' with a simple policy: 'Every new skill MUST pass SkillScan before use. Activate on any install, load, add, evaluate, or safety question about a skill.' With a 92.9K popularity signal, it was the second-highest-ranked security skill in that catalog — behind only Skill Vetter — and the source draws a clear line between the two: Skill Vetter runs as an advisory scanner that gives recommendations, while SkillScan runs as a gate that blocks anything that hasn't passed. The source suggests installing SkillScan instead of Skill Vetter if you want enforcement rather than recommendations, or installing both if you want a layered policy. That layered approach mirrors how mature security programs work everywhere else in software: advisory scanning for exploration, hard gates for anything that touches production. For teams standardizing on agent skills in 2026 — especially those subject to compliance expectations around software supply chains — a gate-style control is often the difference between an informal habit and an auditable policy. As with all community skills, review the package and validate its behavior before relying on it as your sole control.

Best for: Teams that want enforceable skill-install policy rather than recommendations, especially in compliance-sensitive environments

Pricing: Free (open-source skill)

Key features

• Gate-style security enforcement: every new skill must pass before use
• Triggers on install, load, add, evaluate, or safety questions about any skill
• 92.9K popularity signal — second-ranked security skill in AI Skill Market's 2026 catalog
• Designed as a policy control rather than an advisory scanner
• Pairs with advisory vetters like Skill Vetter for layered defense

Pros

• Enforcement model prevents unvetted skills from being used
• Very strong adoption signal in the security category
• Supports layered security policy alongside advisory tools

Cons

• Stricter gates add friction to everyday skill installation
• Quality unrated in independent rubrics
• Gate quality depends on the underlying detection patterns, which evolve with the threat landscape

Visit SkillScan

Conclusion

Security auditing with AI agents in 2026 is really two problems in one: finding skills that genuinely improve your audit work, and making sure the skills themselves don't become the vulnerability. The picks above cover both ends — Trail of Bits' Static Analysis skill and the Security Auditor skill bring professional auditing methodology into your agent, Shannon pushes toward autonomous pentesting, MoltGuard adds runtime defense, and Skill Vetter and SkillScan police the supply chain at install time. SkillzBench takes the top spot because it tackles the foundation underneath all of them: independent verification, security scanning, commit-pinned versions, sandbox benchmarking with published Lift scores, and per-agent loadouts that enforce least privilege — all free, with ad-supported funding that never touches scores. If you're adopting agent skills for security work this year, start by vetting what you install, then layer in the auditing capabilities that fit your stack. Want to go further? Agent skills can automate the rest of your security workflow — browse and test free ones on SkillzBench, and join the waitlist to get verified, benchmarked skills the moment the platform goes live.

Frequently asked questions

What makes SkillzBench different from other agent skill directories?

SkillzBench combines five things most directories don't: continuous crawling and deduplication of skills from GitHub, per-skill verification (commit-SHA pinning, validation, license checks, security scans, and quality judgment), sandbox benchmarking that measures each skill's Lift in percentage points against a no-skill baseline, a free MCP connector with per-agent loadouts so each agent only sees the skills you toggle, and upstream change tracking with re-scans and alerts. Its 0–100 trust score draws on security, quality, license, maintenance, and community signals, and Lift is always shown separately from ratings. Everything is free, funded by blog advertising that never influences scores. Note that SkillzBench is pre-launch — the waitlist and newsletter are live, but the product is not yet public.

Why should I worry about the security of agent skills themselves?

Because skills are executable instructions with real permissions. Published research found roughly 26% of agent skills carry at least one vulnerability, and a security audit of 22,511 skills across major marketplaces found 6.3 problems per skill on average. The threat is not theoretical: the ClawHavoc campaign saw over 1,200 malicious skills published to a marketplace, and CVE-2026-25253 became the first CVE assigned to an agentic AI system, demonstrating remote code execution through a crafted skill package. OWASP's Agentic Skills Top 10 now documents risks including malicious skills, supply-chain compromise, and over-privileged permissions.

What's the difference between pre-install vetting and runtime protection?

Pre-install vetting tools like Skill Vetter and SkillScan examine a skill's code, permissions, and patterns before you install it — catching red flags like credential access, obfuscated code, or exfiltration patterns in the package itself. Runtime protection like MoltGuard watches what the agent actually does during execution, flagging prompt-injection attempts and exfiltration in real time. You need both: a skill can look clean at install time but process malicious injected instructions later, and runtime guards can't retroactively vet what's already installed. The common framing is 'vet beforehand, guard during.'

Are these security auditing skills free to use?

Most of the skills on this list — Trail of Bits' Static Analysis, Skill Vetter, Security Auditor, MoltGuard, and SkillScan — are open-source and free, since the SKILL.md format is an open standard distributed through community registries. Pricing for Shannon was not published in the sources we reviewed, so check with the publisher. SkillzBench is entirely free — the directory, MCP connector, benchmarks, and alerts are all free to users, with revenue coming from advertising around its blog content rather than from charging users.

Which agent platforms do these skills work with?

Skills in the SKILL.md format are portable across agents that have adopted the standard, including Claude Code, OpenAI Codex, Cursor, Gemini CLI, and GitHub Copilot. Trail of Bits' Static Analysis skill is explicitly tagged as multi-platform in curated directories. MoltGuard is focused on the OpenClaw ecosystem, so teams on other platforms should verify compatibility. SkillzBench is being built to work with any agent via its free MCP connector, with per-agent loadouts letting you control exactly which skills each agent can access.

How do I know if a security skill actually improves my agent's performance?

Most skill listings show popularity metrics like install counts, but popularity doesn't measure effectiveness — a skill can be widely installed and still add little value. SkillzBench's approach is to benchmark every skill in a sandbox with the same model, running tasks with and without the skill, and publish the Lift in percentage points so you can see the measured difference rather than relying on marketing claims or download counts. Until SkillzBench launches, the practical alternatives are testing skills yourself in a low-stakes environment and looking for skills from publishers with strong security reputations, like Trail of Bits.

Is autonomous pentesting with an agent safe and legal?

Only with proper authorization and scoping. Autonomous pentesting skills like Shannon can probe live systems, which means running them against applications you don't own or don't have explicit written permission to test can violate computer fraud laws and organizational policies. Best practice in 2026 is to run such skills only against systems you own or are contractually authorized to test, in isolated or staging environments first, with human supervision of the agent's actions. Treat the agent as a fast junior tester that still needs a responsible human in the loop.