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web-content

SEO and AI discovery (GEO) - schema, ChatGPT/Perplexity optimization

55

Quality

69%

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SecuritybySnyk

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tessl review fix ./skills/web-content/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

65%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 highly actionable reference packed with copy-paste templates, checklists, and tracking code that fully covers the GEO/SEO use cases. Its weaknesses are structural: it is a monolithic topic catalog with no progressive disclosure into bundle files, no explicit workflow sequencing, and a fair amount of schema/E-E-A-T boilerplate and dated statistics that inflate the token cost.

Suggestions

Split the five schema.org JSON-LD templates, per-platform optimization lists, and analytics code into references/ files (e.g. references/schema-templates.md, references/platform-playbooks.md), leaving SKILL.md as a concise overview with clearly signaled one-level-deep links.

Replace the generic E-E-A-T bullets and time-sensitive statistics ('AI traffic grew 1,200% between July 2024 and February 2025') with GEO-specific, evergreen guidance, or move dated stats to a clearly marked data-points reference.

Add an explicit sequenced workflow (choose page type → apply template → generate schema → validate with validator.schema.org / rich-results test → run pre-publish checklist) with validation checkpoints, rather than leaving the process implicit in the topic layout.

DimensionReasoningScore

Conciseness

The body is mostly tight, template-driven reference material, but notable sections restate what Claude already knows: the generic E-E-A-T bullets ('Backlinks from trusted sources', 'Secure site (HTTPS)'), five full schema.org JSON-LD boilerplate blocks, and time-sensitive statistics ('AI traffic grew 1,200% between July 2024 and February 2025') that will age. It is above anchor 2 because the GEO-specific templates (TL;DR boxes, per-platform DO/AVOID lists, GA4 regex) are genuinely additive, but below anchor 4 due to the padded knowledge-restatement and dated stats.

3 / 5

Actionability

The guidance is copy-paste ready across the common cases: complete page-type templates, JSON-LD blocks, a GA4 regex filter, a working JavaScript AI-referral tracker, HTML stat-box/TL;DR markup, and concrete pre-publish checklists with measurable specs ('Title tag (50-60 chars)'). This matches the anchor for fully executable, example-covered guidance.

5 / 5

Workflow Clarity

The content is organized as a topic catalog (page types, formats, schema, platforms) rather than a sequenced process — the implicit workflow (pick page type → apply template → add schema → verify freshness → run checklist) must be assembled by the reader. The 'Before Publishing' checklist provides checkpoints, so this clears anchor 2, but no explicit sequence or validation feedback loop exists, keeping it at anchor 3.

3 / 5

Progressive Disclosure

Section headers are clear and consistent, but the entire ~670-line skill is inlined in SKILL.md with no bundle files (no references/, scripts/, or assets/ exist). Content that clearly belongs in separate reference files — five schema JSON-LD templates, per-platform optimization lists, and the analytics code — is inline, which matches the anchor for good-but-inline structure rather than an overview with well-signaled one-level-deep references.

3 / 5

Total

14

/

20

Passed

Description

61%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 compact, keyword-dense description that identifies a distinct niche and includes several natural trigger terms, but it reads as a topic label rather than a capability statement and lacks an inline 'Use when...' trigger clause. Moving the when-to-use guidance into the description and naming concrete actions would lift it substantially.

Suggestions

Merge the 'when-to-use' guidance into the description itself, e.g. append 'Use when creating or optimizing web pages, blog posts, or FAQs for search rankings and AI citations.'

Convert the noun phrases into concrete actions, e.g. 'Structures pages for AI citation (TL;DR boxes, FAQs, comparison tables) and adds schema.org markup (Organization, Article, FAQPage, Product).'

Add natural synonym trigger terms users actually say, such as 'AI search', 'answer engines', 'generative engine optimization', or 'get cited by ChatGPT/Perplexity'.

DimensionReasoningScore

Specificity

The description names the domain ('SEO and AI discovery (GEO)') and 1-2 concrete sub-targets ('schema, ChatGPT/Perplexity optimization'), but these are noun phrases rather than a list of several specific actions, so coverage is not comprehensive. It sits above anchor 2 (domain named, actions minimal) but below anchor 4 (several specific actions listed).

3 / 5

Completeness

The 'what' is reasonably clear (SEO and AI-discovery optimization with schema markup), but the description field itself contains no 'Use when...' clause — the trigger guidance lives in a separate 'when-to-use' frontmatter field rather than the description. Per the rubric guideline, a missing 'Use when...' clause in the description caps completeness at 3.

3 / 5

Trigger Term Quality

'SEO', 'schema', 'ChatGPT', and 'Perplexity' are natural terms users would say when needing this skill, giving good keyword coverage. It falls short of anchor 5 because common variations like 'AI search', 'answer engine', 'LLM optimization', or 'generative engine optimization' are absent.

4 / 5

Distinctiveness Conflict Risk

The GEO/AI-citation niche ('ChatGPT/Perplexity optimization') is mostly distinct from adjacent content-writing skills, so wrong-skill triggering is unlikely. It is not a 5 because 'SEO' alone is broad and could overlap with closely related SEO/content-marketing skills.

4 / 5

Total

14

/

20

Passed

Validation

87%

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

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (681 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository

alinaqi/maggy

Path

skills/web-content/SKILL.md

Reviewed

Table of Contents

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