Name: ai-security
Rating: 67.2 (1 reviews)
Author: alirezarezvani

ai-security

Use when assessing AI/ML systems for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse. Covers MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring.

Quality

81%

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Securityby

Passed

No findings from the security scan

The canonical home for this skill is ai-security in alirezarezvani/claude-skills

Quality

Content

71%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A well-organized, actionable body with concrete commands, clear workflows, and useful decision checkpoints. Its main weakness is progressive disclosure: it references bundle files (scripts/ai_threat_scanner.py, references/atlas-coverage.md) that are not present, and inlines content that would benefit from being split into reference files.

Suggestions

Ship the referenced bundle files — create scripts/ai_threat_scanner.py and references/atlas-coverage.md — or remove the references so the skill is self-contained and commands are actually executable.

Move the large signature and ATLAS coverage tables into reference files (e.g. references/injection-signatures.md) and keep SKILL.md as an overview pointing to them, deepening the progressive-disclosure structure.

Trim definitional prose (e.g. 'Prompt injection occurs when...', 'Model inversion attacks reconstruct...') that restates concepts Claude already knows, and add per-iteration validation to the batch workflow loop.

Dimension	Reasoning	Score
Conciseness	Mostly dense and efficient — tables, concrete commands, and exit codes earn their place — but a few definitional sentences ('Prompt injection occurs when adversarial input overrides...', 'Model inversion attacks reconstruct training data...') explain concepts Claude already knows and could be trimmed.	4 / 5
Actionability	Provides concrete, copy-paste-ready commands with specific flags, a test-file JSON format, and exit-code semantics; however the referenced `scripts/ai_threat_scanner.py` is not bundled, so the commands are not actually runnable as-is — a minor gap below fully executable.	4 / 5
Workflow Clarity	Three clearly sequenced workflows with explicit decision checkpoints (exit code 2 = block deployment, CI/CD risk gate) and phased structure; the batch `for target` loop lacks per-iteration validation, a minor gap keeping it below 5.	4 / 5
Progressive Disclosure	Good in-body structure (TOC, headed sections, one signaled reference to `references/atlas-coverage.md`), but neither that reference nor `scripts/ai_threat_scanner.py` exist in the bundle, and substantial reference-style tables (signatures, ATLAS coverage) are inlined rather than split out.	3 / 5
	Total	15 / 20 Passed

Description

92%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

A strong, well-scoped description that explicitly states both what the skill does and when to use it, with concrete natural-language trigger terms and a distinct AI/ML security niche. Only minor gap is a few missing keyword synonyms keeping trigger_term_quality just below maximum.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions — 'assessing...for prompt injection, jailbreak vulnerabilities, model inversion risk, data poisoning exposure, or agent tool abuse' plus 'MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring' — giving comprehensive coverage of the skill's capabilities.	5 / 5
Completeness	Explicitly answers both 'what' ('Covers MITRE ATLAS technique mapping, injection signature detection, and adversarial robustness scoring') and 'when' ('Use when assessing AI/ML systems for...') with concrete trigger phrases, matching the 5 anchor.	5 / 5
Trigger Term Quality	Strong natural keywords users would actually say ('prompt injection', 'jailbreak', 'model inversion', 'data poisoning', 'agent tool abuse'), but a few common variants/synonyms are absent, so it sits just below the comprehensive 5 anchor.	4 / 5
Distinctiveness Conflict Risk	Clear niche (AI/ML system security specifically) with distinct triggers; the body further disambiguates from security-pen-testing, red-team, and threat-detection, giving minimal conflict risk.	5 / 5
	Total	19 / 20 Passed

Validation

87%

Warnings & errors only

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

Criteria	Description	Result
relative_links	Relative link issues: 4 suspicious	Warning
referenced_paths_exist	Referenced path issues: 10 missing	Warning

	Total	14 / 16 Passed

Repository: alirezarezvani/claude-skills
Path: .gemini/skills/ai-security/SKILL.md
Commit: 19392f7

Reviewed: ~23 hours ago

Table of Contents

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