The description names the domain (AI/LLM security) and a primary action ('Audit applications for...vulnerabilities'), but it doesn't list multiple concrete actions like 'test for prompt injection, scan system prompts, validate input sanitization, review permission boundaries.' The action is singular and somewhat high-level.

Clearly answers both 'what' (audit applications for AI prompt injection, agent security, and LLM permission boundary vulnerabilities) and 'when' (explicit 'Use when...' clause with extensive trigger terms and broader conditions like 'needs to secure any application with AI features').

Excellent coverage of natural trigger terms users would say: 'prompt injection,' 'LLM security,' 'AI security,' 'jailbreak,' 'indirect prompt injection,' 'prompt leaking,' 'AI red team,' 'LLM vulnerabilities,' 'system prompt extraction,' 'agent security,' 'MCP security,' 'AI privilege escalation,' and more. These are highly natural terms a user would use.

This skill occupies a very clear niche—AI/LLM security auditing—with highly specific trigger terms like 'prompt injection,' 'jailbreak,' 'system prompt extraction,' and 'MCP security' that are unlikely to conflict with general security or general AI skills.

The skill is thorough but verbose in places. The Background section explains what prompt injection is, which Claude likely already knows. Some sections like Step 5 (prompt leaking) include basic extraction attempts that are common knowledge. However, the checklists and tables are efficient formats, and the methodology steps contain genuinely useful structural guidance. Overall, it could be tightened by ~30% without losing value.

The skill provides concrete, executable code examples for both vulnerable and remediated patterns across Python and JSX. Each step includes specific grep patterns, code snippets showing vulnerable vs. better approaches, and detailed checklists. The output format template is copy-paste ready with clear structure for findings.

The 7-step methodology is clearly sequenced from attack surface mapping through defense assessment. Each step builds logically on the previous one. The permission check checklist and defense assessment table serve as validation checkpoints. The prioritized remediation section in the output format provides a clear feedback mechanism for ranking and addressing findings.

The skill is a monolithic document at ~300+ lines with no bundle files to offload detailed content. The cross-references to other skills (threat-modeling, owasp-audit, api-audit, ai-risk-management) are good, but the inline content is dense — the tool/agent audit section, permission boundaries section, and defense assessment could each be separate reference files. The references section at the end lists external resources but doesn't link to any bundled supporting materials.