Conciseness

The body is short (~50 lines) and well organized, but the 13 one-line step descriptions ('Assess data quality, provenance, bias detection, and governance controls throughout the data lifecycle') largely restate what the category headings already convey. Efficient with minor over-explanation that could be trimmed — anchor 4, not 5.

Actionability

The body gives only high-level direction ('Assess...', 'Evaluate...', 'Review...') with no concrete verification methods, test payloads, commands, or examples. It delegates all executable detail to 'plays/ai-security-verification.md' and 'templates/finding.md', neither of which exists in the bundle, so the reader cannot actually execute anything — anchor 2 rather than 3.

Workflow Clarity

The 13 steps are numbered and coherently scoped, but they form a category checklist rather than a workflow: no validation checkpoints, evidence-gathering guidance, or feedback loops for how findings are confirmed and documented. Sequence present with checkpoints missing — anchor 3, not 4.

Progressive Disclosure

Sections are organized and the external references are clearly signaled, but the actual bundle contains neither referenced path — no 'plays/ai-security-verification.md' and no 'templates/finding.md', and no references/, scripts/, or assets/ directories at all. The core instruction 'Follow the full procedure in plays/ai-security-verification.md' points at a nonexistent file, breaking the disclosure structure — anchor 2.

Specificity

The description names the domain and several concrete actions — 'verifying an AI-driven application, ML pipeline, or LLM-integrated system against AISVS', 'preparing for an AI security audit', 'checking security and ethical controls across 13 categories' — in third person, with minor gaps (no output artifacts named). It lists several specific actions but is not comprehensively concrete, matching anchor 4 rather than 5.

Completeness

It explicitly answers both: what ('Comprehensive AI security verification using OWASP AI Security Verification Standard (AISVS) framework') and when ('Use when verifying an AI-driven application, ML pipeline, or LLM-integrated system against AISVS, preparing for an AI security audit...'). Clear what plus an explicit 'Use when' clause with concrete trigger phrases matches anchor 5.

Trigger Term Quality

Natural phrases users would say include 'AI security audit', 'ML pipeline', 'LLM-integrated system', and 'AISVS'. Coverage is good but misses common variations like 'LLM security', 'AI red teaming', or 'model security review', placing it at anchor 4 rather than 5.

Distinctiveness Conflict Risk

The AISVS-specific framing carves a distinct niche, but 'checking security and ethical controls' and audit preparation overlap with general security-review skills. Mostly distinct with minor overlap risk against closely related skills — anchor 4 rather than 5.