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geo-content

Content quality and E-E-A-T assessment for AI citability — evaluate experience, expertise, authoritativeness, trustworthiness, and content structure

55

Quality

63%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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

Quality

Content

61%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 body is a thorough, largely actionable E-E-A-T assessment framework with concrete scoring rubrics and a ready-to-fill output template. Its main weaknesses are verbosity in places that restate general knowledge, an implicit workflow without explicit validation checkpoints, and a monolithic structure that bundles reference-grade material inline.

Suggestions

Trim sections that restate general knowledge (the AI-citability preamble, basic readability guidance, and generic 'low-quality AI content' phrasing lists) to improve token efficiency.

Add explicit verification checkpoints to the workflow, e.g. 'Confirm each fetched page matches the target URL' and 'Reconcile per-page scores against the E-E-A-T subtotal before writing the report.'

Move the large output template and the detailed point-value scoring tables into reference files (e.g. OUTPUT_TEMPLATE.md, EEAT_RUBRIC.md) and link to them, keeping SKILL.md as a lean overview.

DimensionReasoningScore

Conciseness

The body runs ~345 lines and, while much is genuine domain-specific scoring rubric, several sections restate widely known ideas (e.g. explaining that AI platforms 'do not just find content — they evaluate whether content deserves to be cited', readability basics, and generic AI-content phrasing lists). It is mostly efficient but includes padding that could be trimmed, so 3 rather than 4.

3 / 5

Actionability

Provides concrete, point-valued scoring tables per E-E-A-T dimension, explicit 'How to Score' instructions, word-count benchmarks, and a full output template the model can fill in. Guidance is mostly executable with minor gaps (e.g. readability is 'estimated without a tool' rather than computed), so 4 rather than 5.

4 / 5

Workflow Clarity

The 'How to Use This Skill' section lists a numbered 1–6 sequence, but checkpoints are implicit and there is no validate/retry feedback loop. Although this is a non-destructive assessment skill, the sequence still lacks explicit verification steps (e.g. confirming fetched pages, cross-checking scores), placing it at 3.

3 / 5

Progressive Disclosure

The body is a single file with no bundle files, but it is well organized into clearly headed sections (E-E-A-T, Content Quality, AI Content, Freshness, Topical Authority, Scoring, Output). Structure is good and easy to navigate; it is not a 5 because the long scoring tables and output template could arguably live in separate reference files, and there are no signaled external references.

4 / 5

Total

14

/

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 clearly conveys what the skill does (E-E-A-T and content-quality assessment for AI citability) with strong, specific language, but it omits any explicit 'Use when...' trigger guidance, which caps completeness. Trigger terms are good but lack a few common user phrasings.

Suggestions

Add an explicit 'Use when...' clause, e.g. 'Use when auditing a site's likelihood of being cited by AI search platforms or when the user asks about E-E-A-T, AI citability, or GEO content quality.'

Include more natural user phrasings such as 'AI search', 'get cited by ChatGPT/Perplexity', 'SEO for AI', or 'GEO' to broaden trigger-term coverage.

Mention the concrete output artifact ('generates GEO-CONTENT-ANALYSIS.md') in the description to round out the 'what' and aid distinctiveness.

DimensionReasoningScore

Specificity

Quotes 'Content quality and E-E-A-T assessment for AI citability' and 'evaluate experience, expertise, authoritativeness, trustworthiness, and content structure' — names the domain plus several concrete actions (assess, evaluate each E-E-A-T dimension, content structure). It lists multiple specific actions but stops just short of comprehensive coverage (no mention of scoring/reporting), so 4 rather than 5.

4 / 5

Completeness

The 'what' is clearly stated (assess content quality and E-E-A-T for AI citability), but there is no explicit 'Use when...' clause or equivalent trigger guidance, so per the rubric guideline completeness is capped at 3. It is above 2 because the 'what' is clear and concrete, not vague.

3 / 5

Trigger Term Quality

Includes natural terms 'content quality', 'E-E-A-T', 'citability', 'AI citability', 'experience', 'expertise', 'authoritativeness', 'trustworthiness', and 'content structure'. Good keyword coverage, but common variations a user might say ('AI search', 'SEO', 'GEO', 'get cited by AI') are missing, placing it at 4 rather than 5.

4 / 5

Distinctiveness Conflict Risk

The combination of 'E-E-A-T assessment for AI citability' carves a fairly distinct niche with minimal overlap against general SEO or content skills. Minor overlap risk with broader SEO/content-quality skills keeps it at 4 rather than 5.

4 / 5

Total

15

/

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.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository
zubair-trabzada/geo-seo-claude
Reviewed

Table of Contents

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