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aeo

Use when content should be cited by ChatGPT, Perplexity, Claude or Gemini (answer engine optimization), before rewriting it. Scores E-E-A-T and structure with aeo_audit.py, rewrites with aeo_optimizer.py and logs citations with citation_tracker.py.

Quality

84%

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

78%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, well-structured skill body: executable commands cover the full workflow, the pre-flight bot-access step is a genuine checkpoint, and progressive disclosure is exemplary with all referenced files present and clearly described. The main cost is token efficiency — the SEO-vs-AEO conceptual comparison and the Trigger Phrases section add tokens without adding operational value.

Suggestions

Trim the 'Distinct From SEO' section to the table alone (or move it into references/aeo_vs_seo.md, which already covers the topic) — Claude already understands the SEO/AEO distinction, so the prose explanation is padding.

Remove the 'Trigger Phrases' section — it duplicates the frontmatter description, which is the correct home for trigger guidance.

Add a closing validation step to the workflow (e.g., re-run aeo_audit.py on the optimized output and confirm the composite score improved before publishing) to create a validate→fix→retry feedback loop.

DimensionReasoningScore

Conciseness

The "Distinct From SEO" comparison table plus follow-up prose re-explains a conceptual distinction Claude already knows, and the "Trigger Phrases" section duplicates the frontmatter description. Mostly efficient elsewhere, but multiple sections are trimmable — matching anchor 3 rather than 4; not 2 because there is no tutorial padding and the operational sections earn their tokens.

3 / 5

Actionability

The workflow gives copy-paste-ready commands for every common case ("python3 scripts/aeo_audit.py --url ...", "--mode balanced --output post-aeo.md", "citation_tracker.py --action add --llm perplexity"), plus a concrete report format and an industry-flag example. Matches anchor 5: fully executable guidance covering the common cases.

5 / 5

Workflow Clarity

A clearly numbered 0–4 sequence with per-step commands and expected outputs, including a real pre-flight checkpoint ("a blocked GPTBot/PerplexityBot/ClaudeBot/Google-Extended is the first fix, always"). Not 5 because there is no validate→fix→retry loop (e.g., re-audit after optimizing to confirm the score improved); not 3 because checkpoints are explicit, not implicit, and operations are non-destructive.

4 / 5

Progressive Disclosure

The References section signals each of the 5 bundle files with a one-line content description, all verified to exist, one level deep with no nested references; detailed methodology is properly split out while the body stays an operational overview. Matches anchor 5; not 4 because references are clearly signaled and the split is appropriate — the inline tables are quick-reference material used directly in the workflow.

5 / 5

Total

17

/

20

Passed

Description

83%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 strong description with an explicit third-person what + when structure, tool-named actions, and highly natural LLM-name triggers. Its only gaps are a few missing synonyms (E-E-A-T audit, AI search) and minor overlap risk with sibling SEO skills in the same bundle.

DimensionReasoningScore

Specificity

"Scores E-E-A-T and structure with aeo_audit.py, rewrites with aeo_optimizer.py and logs citations with citation_tracker.py" lists three specific, tool-bound actions — matching the anchor-4 example's shape. Not 5 because secondary capabilities (industry flags, query research) are uncovered; not 3 because the actions are named and concrete rather than generic.

4 / 5

Completeness

"Use when content should be cited by ChatGPT, Perplexity, Claude or Gemini ... before rewriting it" is an explicit when-clause with concrete triggers, and the three-action clause answers what. Both are clearly and explicitly present, matching the anchor-5 example; the when-clause is specific, not merely present, so 4 is too low.

5 / 5

Trigger Term Quality

"cited by ChatGPT, Perplexity, Claude or Gemini" and "answer engine optimization" give good natural keyword coverage since users name the LLMs directly. Not 5 because common synonyms like "E-E-A-T audit", "AI search", and "get cited" appear only in the body, not the description; not 3 since the named LLMs are themselves highly natural triggers.

4 / 5

Distinctiveness Conflict Risk

The LLM-citation framing ("cited by ChatGPT, Perplexity, Claude or Gemini (answer engine optimization)") carves a clear niche distinct from generic SEO. Not 5 because a sibling seo-audit/content skill in the same bundle creates minor overlap for vague "optimize my content" requests; not 3 since the trigger is well outside generic SEO territory.

4 / 5

Total

17

/

20

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

alirezarezvani/claude-skills

Path

marketing-skill/skills/aeo/SKILL.md

Reviewed

Table of Contents

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