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schema-markup

Design, validate, and optimize schema.org structured data for eligibility, correctness, and measurable SEO impact.

53

Quality

60%

Does it follow best practices?

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SecuritybySnyk

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tessl review fix ./plugins/AI-Agents-Safe-Coding-Skills-claude/skills/schema-markup/SKILL.md

The canonical home for this skill is schema-markup in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

53%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.

The skill provides a well-structured eligibility/scoring framework and clear principles, but its JSON-LD example is a stub, validation steps lack runnable commands and an explicit error-recovery loop, and some sections contain boilerplate and over-explanation. Tightening the body and making the examples executable would raise the score.

Suggestions

Replace the stub JSON-LD block with a complete, copy-paste-ready example (e.g. a real Article or Product block) covering the common cases.

Add an explicit validate→fix→retry feedback loop with runnable tool references (e.g. Google Rich Results Test URL / CLI) so deployment failures have a recovery path.

Trim the boilerplate "When to Use" line and the empty '---' section, and consider moving the full schema-type catalog and scoring rubric into a reference file to improve conciseness and progressive disclosure.

DimensionReasoningScore

Conciseness

The body is mostly efficient structured lists, but the supported-types catalog, generic "Use for:" prose, a duplicate empty '---' section (line 9), and a boilerplate "When to Use" line (line 360) add explanation and noise that could be trimmed.

3 / 5

Actionability

Concrete guidance exists (scoring rubric with weights and bands, supported types, validation checklist, output format), but the JSON-LD example is a non-executable stub ({"@type": "..."}) and validation tools are named without commands, leaving key details incomplete.

3 / 5

Workflow Clarity

Phases 0→1 are sequenced with a score threshold (≥70) and a stop rule for "Do Not Implement", but there is no explicit validate→fix→retry feedback loop with runnable commands, which the rubric treats as a checkpoint gap.

3 / 5

Progressive Disclosure

No bundle files exist, so all content lives in a single SKILL.md; it is well-organized with clear section headers and no nested references, though the ~360-line body (including the inline scoring rubric and type catalog) could be partially split into reference files.

4 / 5

Total

13

/

20

Passed

Description

66%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.

The description is specific and names a clear niche with concrete actions, but it omits an explicit "Use when" trigger clause, capping completeness at 3 and leaving it slightly below the strongest examples. Adding a natural trigger phrase (e.g. "Use when adding schema.org structured data or targeting Google rich results") would lift it.

Suggestions

Append an explicit 'Use when...' clause naming natural triggers: 'Use when designing schema.org structured data, targeting Google rich results, or auditing JSON-LD for eligibility.'

Add common synonyms users say aloud, e.g. 'schema markup', 'rich results', and 'JSON-LD', to improve trigger-term coverage.

Add one more concrete action (e.g. 'generate maintainable JSON-LD') to round out the action list toward comprehensive coverage.

DimensionReasoningScore

Specificity

"Design, validate, and optimize" names three concrete actions on "schema.org structured data" with clear outcomes (eligibility, correctness, SEO impact), but does not enumerate the full breadth of actions (e.g. generate/implement JSON-LD) that a 5 would.

4 / 5

Completeness

The description gives a clear "what" but contains no explicit "Use when..." / when-to-use trigger clause, which per the rubric caps completeness at 3.

3 / 5

Trigger Term Quality

Includes natural terms users say ("schema.org structured data", "structured data", "SEO") but omits common synonyms like "rich results", "schema markup", and "JSON-LD" that a user might voice.

4 / 5

Distinctiveness Conflict Risk

The schema.org/structured-data niche is fairly distinct from generic SEO skills, with only minor overlap risk against broad SEO-audit skills.

4 / 5

Total

15

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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