Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mastepanoski/claude-skills --skill owasp-ai-testinggit clone --depth 1 https://github.com/mastepanoski/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mastepanoski/claude-skills/owasp-ai-testing)<a href="https://agentmods.dev/skills/mastepanoski/claude-skills/owasp-ai-testing"><img src="https://agentmods.dev/badge/skills/mastepanoski/claude-skills/owasp-ai-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mastepanoski/claude-skills/owasp-ai-testing"><img src="https://agentmods.dev/badge/skills/mastepanoski/claude-skills/owasp-ai-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00043 | $0.02259 |
| Opus 5 | $0.00022 | $0.01130 |
| Sonnet 5 | $0.00009 | $0.00452 |
| Haiku 4.5 | $0.00004 | $0.00226 |
Grade A, and why
owasp-ai-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OWASP AI Testing Guide
This skill enables AI agents to perform systematic trustworthiness testing of AI systems using the OWASP AI Testing Guide v1, published November 2025 by the OWASP Foundation.
The AI Testing Guide is the industry's first open standard for AI trustworthiness testing. Unlike vulnerability lists that identify WHAT risks exist, this guide provides a practical, repeatable methodology for HOW to test AI systems. It establishes 32 test cases across 4 layers, each with objectives, payloads, observable responses, and remediation guidance.
The guide's core principle: "Security is not sufficient, AI Trustworthiness is the real objective." AI systems fail for reasons beyond traditional security, including bias, hallucinations, misalignment, opacity, and data quality issues.
Use this skill to execute comprehensive AI testing, validate trustworthiness controls, prepare for audits, and build repeatable test suites for AI systems.
Combine with "OWASP LLM Top 10" for vulnerability identification, "NIST AI RMF" for risk management, or "ISO 42001 AI Governance" for governance compliance.
When to Use This Skill
Invoke this skill when:
- Performing penetration testing of AI/ML systems
- Validating AI trustworthiness before production deployment
- Building automated test suites for AI applications
- Conducting red-team exercises against AI features
- Preparing for AI security audits or certifications
- Testing RAG systems, chatbots, agents, or ML pipelines
- Evaluating model robustness and adversarial resistance
- Assessing data quality, bias, and privacy compliance
- Validating AI supply chain security
- Testing after model updates, fine-tuning, or data changes
Inputs Required
When executing this testing guide, gather:
- ai_system_description: Description of the AI system (type, purpose, architecture, models used) [REQUIRED]
- system_architecture: Technical architecture (APIs, models, vector stores, plugins, data pipelines) [OPTIONAL but recommended]
- testing_scope: Which layers to test (Application, Model, Infrastructure, Data, or All) [OPTIONAL, defaults to All]
- model_details: Model provider, version, fine-tuning details, hosting (cloud/self-hosted) [OPTIONAL]
- data_details: Training data sources, vector databases, data pipelines [OPTIONAL]
- existing_controls: Current security and trustworthiness measures [OPTIONAL]
- risk_context: Data sensitivity, regulatory requirements, deployment context [OPTIONAL]
- testing_authorization: Explicit authorization, environment, and boundaries for active testing [REQUIRED for live or active tests]
- safe_testing_mode: documentation-only, staging, production-readonly, or production-approved [OPTIONAL, defaults to documentation-only unless authorization is clear]
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 213 lines · 43 tokens per session scan A 039e34fc84ee
owasp-ai-testing is a skill published in the GitHub repository mastepanoski/claude-skills (53 stars, last pushed 3mo ago), licensed MIT. It adds 43 tokens to every session and 2,259 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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