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ai-inventory

Generate and analyze AI Bill of Materials (AIBOM) for Python projects using AI/ML components. Identifies AI models, datasets, tools, and frameworks for security and compliance tracking. Use this skill when: - User asks to scan for AI components - User wants to know what AI models a project uses - User mentions "AI BOM", "AI inventory", or "ML security" - User is working with Python AI/ML projects (PyTorch, TensorFlow, HuggingFace) - User needs AI component compliance documentation

72

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

77%

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

The body is highly actionable and clearly sequenced with validation checkpoints, but it leans monolithic: Quick Start duplicates later phases and the long report templates live inline rather than in referenced files. Tightening redundancy and extracting templates into references would lift the weaker dimensions.

Suggestions

De-duplicate the Quick Start so it only previews the flow, keeping the concrete invocations in Phase 2 — the repeated mcp_snyk_snyk_aibom calls cost tokens without adding clarity.

Extract the Phase 3 summary-report table layout and the Phase 5 compliance-report template into separate reference files (e.g. references/report-templates.md) and link to them one level deep, improving both conciseness and progressive disclosure.

Trim the inline report templates to the essential fields and reference the full templates externally, since the current templates restate structure Claude can derive from the validated scan output.

DimensionReasoningScore

Conciseness

Avoids explaining concepts Claude already knows, but the Quick Start block duplicates the Phase 2 tool invocations and the long summary/compliance report templates are reproduced inline; it could be tightened. Not level 3 because of this redundancy and inline template bulk; not level 1 because it never pads with tutorial-style concept explanation.

2 / 3

Actionability

Provides concrete, copy-paste-ready tool invocations such as 'mcp_snyk_snyk_aibom(path=..., json_file_output=...)' plus an illustrative AI/ML package list and populated report tables. Matches the fully-executable anchor; not level 2 because the guidance is complete rather than pseudocode.

3 / 3

Workflow Clarity

Phases 1–5 are clearly sequenced with explicit checkpoints — Step 2.2 'Validate AIBOM Output' gates Phase 3, and Quick Start Step 3 requires verifying component entries before proceeding, with error-recovery blocks. Not level 2 because validation and feedback loops are explicit, not implicit.

3 / 3

Progressive Disclosure

Well organized into phases, but it is a single monolithic file over 50 lines with no bundle files and inline report/compliance templates that could be split out. Not level 3 because content that should be separate is inline with no one-level-deep references; not level 1 because sections are clearly organized, not a wall of text.

2 / 3

Total

10

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12

Passed

Description

100%

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

The description is strong across all dimensions: it states concrete actions, gives an explicit 'Use this skill when' trigger list with natural terms, and carves out a distinctive AIBOM-for-Python niche. It uses third-person voice with no first/second-person or vague fluff.

DimensionReasoningScore

Specificity

Names multiple concrete actions — 'Generate and analyze AI Bill of Materials (AIBOM)' and 'Identifies AI models, datasets, tools, and frameworks' — matching the 'lists multiple specific concrete actions' anchor; not the level below because it is comprehensive rather than naming only some actions.

3 / 3

Completeness

Explicitly answers both what (generate/analyze AIBOM, identify components) and when via a dedicated 'Use this skill when:' trigger list; not level 2 because the when is explicit, not merely implied.

3 / 3

Trigger Term Quality

Covers natural terms users would say — 'AI BOM', 'AI inventory', 'ML security', 'scan for AI components', and framework names (PyTorch, TensorFlow, HuggingFace) — matching the good-coverage anchor rather than only some relevant keywords.

3 / 3

Distinctiveness Conflict Risk

Occupies a clear niche (AIBOM for Python AI/ML projects) with distinct, specific triggers unlikely to fire for unrelated skills; not level 2 because the scope is sharply bounded to AIBOM rather than broadly overlapping.

3 / 3

Total

12

/

12

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
snyk/studio-recipes
Reviewed

Table of Contents

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