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geo

GEO-first SEO analysis tool. Optimizes websites for AI-powered search engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) while maintaining traditional SEO foundations. Performs full GEO audits, citability scoring, AI crawler analysis, llms.txt generation, brand mention scanning, platform-specific optimization, schema markup, technical SEO, content quality (E-E-A-T), and client-ready GEO report generation. Use when user says "geo", "seo", "audit", "AI search", "AI visibility", "optimize", "citability", "llms.txt", "schema", "brand mentions", "GEO report", or any URL for analysis.

63

Quality

75%

Does it follow best practices?

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SecuritybySnyk

Medium

Suggest reviewing before use

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tessl review fix ./geo/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

50%

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

A well-organized orchestration overview with concrete commands and a real PDF-generation snippet, but it is weakened by a non-essential marketing-stats section, analysis work delegated to non-existent bundle files, and missing validation checkpoints for batch crawl and output operations.

Suggestions

Remove or relocate the 'Market Context (Why GEO Matters)' stats table; it is time-sensitive marketing content that adds tokens without aiding execution.

Add explicit validation/verification checkpoints to the audit workflow (e.g., verify crawl results before scoring, validate generated JSON-LD/schema, confirm the HTML renders before Chrome PDF conversion).

Either ship the referenced bundle files (agents/*.md, skills/*/, templates/*) or inline the actual analysis methodology so the skill is self-contained and the delegation targets resolve.

DimensionReasoningScore

Conciseness

The body is mostly table-driven and avoids explaining concepts Claude already knows, but the 'Market Context (Why GEO Matters)' table of ten time-sensitive market stats (dollar figures, CAGR, '2025', '2031', 'Dec 2025') is marketing padding that does not aid execution, which the guidelines penalize; it is efficient-but-could-be-tightened rather than fully lean.

2 / 3

Actionability

Concrete guidance exists for orchestration and the PDF pipeline (real pandoc + Chrome commands, output-file mappings, quality-gate numbers), but the core analysis work (citability scoring, E-E-A-T assessment, schema validation) is delegated to subagent/skill files (agents/*.md, skills/*/) that do not exist in the bundle, leaving the actual instructions incomplete rather than copy-paste ready.

2 / 3

Workflow Clarity

The audit is clearly sequenced (Phase 1 Discovery, Phase 2 Parallel, Phase 3 Synthesis) with a scoring methodology table, but validation/verification checkpoints are absent for the batch 50-page crawl and output generation, and the guidelines cap batch-operation workflows without validation feedback loops at 2 rather than 3.

2 / 3

Progressive Disclosure

References to bundles are clearly signaled in tables (5 agents/*.md, 14 skills/*/dirs, templates/*.css + .html), but none of those referenced files or directories exist in the bundle, so the apparent one-level-deep split is actually a map to phantom files; structure is present and well organized, but the split content is missing.

2 / 3

Total

8

/

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.

A strong, specific description that enumerates concrete capabilities and pairs them with an explicit, natural-language trigger list covering both 'what' and 'when'. Third-person voice is maintained throughout and it stays concise relative to its scope.

DimensionReasoningScore

Specificity

Lists many concrete actions such as 'full GEO audits, citability scoring, AI crawler analysis, llms.txt generation, brand mention scanning, platform-specific optimization, schema markup, technical SEO, content quality (E-E-A-T), and client-ready GEO report generation', matching the multiple-specific-actions anchor rather than the partial score-2 anchor.

3 / 3

Completeness

Clearly answers both 'what' via the enumerated action list and 'when' via the explicit 'Use when user says ...' clause, so it is not capped at 2 by the missing-trigger guideline.

3 / 3

Trigger Term Quality

Explicit natural trigger list ('geo', 'seo', 'audit', 'AI search', 'AI visibility', 'optimize', 'citability', 'llms.txt', 'schema', 'brand mentions', 'GEO report') plus 'any URL' gives broad coverage of terms a user would actually say, matching the good-coverage anchor.

3 / 3

Distinctiveness Conflict Risk

The GEO-first / AI-search framing plus named engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) carves a clear niche unlikely to conflict with unrelated skills, despite a few generic single words like 'audit' and 'schema'.

3 / 3

Total

12

/

12

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

allowed_tools_field

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

Warning

Total

15

/

16

Passed

Repository
zubair-trabzada/geo-seo-claude
Reviewed

Table of Contents

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