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aeo

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

72

Quality

90%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

75%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 strong, command-driven skill body: fully executable invocations for all three tools, a clearly sequenced workflow with an explicit pre-flight gating check, and well-signaled references that all exist on disk. Its main weakness is duplication — the industry thresholds and anti-patterns sections restate reference-file content inline, and the Trigger Phrases section repeats the description — which costs both conciseness and some progressive-disclosure organization.

Suggestions

Replace the inline "Industry-Specific E-E-A-T Thresholds" table with a one-line pointer to the corresponding section in references/aeo_eeat_canon.md (the reference already carries the table plus YMYL rationale).

Trim "Anti-Patterns Rejected" to the six bolded names with one-line summaries, or point to the Anti-Patterns section of references/aeo_eeat_canon.md for the justifications.

Drop the "Trigger Phrases" section (it duplicates the frontmatter description) and the porting-history footer paragraph, which spends tokens on provenance rather than guidance.

DimensionReasoningScore

Conciseness

The body is mostly lean and command-driven with no explanation of concepts Claude already knows, but several sections are tightening candidates: the "Industry-Specific E-E-A-T Thresholds" table (8 industries) and "Anti-Patterns Rejected" substantially duplicate content already in references/aeo_eeat_canon.md, the "Trigger Phrases" section repeats the frontmatter description verbatim in spirit, and the porting-history footer ("2,464 LOC across 9 modules …") adds little operational value. This fits anchor 3 (mostly efficient but could be tightened) better than anchor 2, since the duplicated material is a minority of the file and everything else earns its tokens.

3 / 5

Actionability

Every tool has a copy-paste-ready invocation with real flags and example values: "python3 scripts/aeo_audit.py --url https://example.com/blog/post", "aeo_optimizer.py --input post.md --mode balanced --output post-aeo.md", and a fully parameterized citation_tracker add/report pair; the three scripts all exist in scripts/. Concrete output samples (the markdown report template) and a worked configuration example (--industry healthcare) cover the common cases — anchor 5.

5 / 5

Workflow Clarity

The workflow is a clearly numbered 0–4 sequence, each step paired with a concrete command and its expected result ("→ markdown report with composite score + 4-dimension breakdown"), and step 0 is an explicit gating checkpoint ("a blocked GPTBot/PerplexityBot/ClaudeBot/Google-Extended is the first fix, always"). It falls short of anchor 5 because there are no feedback loops (e.g., re-audit after applying fixes, or verify the optimizer's output before publishing); the operations are non-destructive (optimizer writes to a new output file), so the destructive-operation cap at 3 does not apply.

4 / 5

Progressive Disclosure

The References section clearly signals all five one-level-deep reference files with one-line descriptions of each, and all five exist in references/ (verified), as do the three scripts named in Core Capabilities. The gap keeping it at anchor 4 rather than 5 is that the industry-thresholds table and anti-patterns list are inlined in SKILL.md despite being covered by references/aeo_eeat_canon.md, and the Trigger Phrases section duplicates the frontmatter rather than pointing into the body's structure.

4 / 5

Total

16

/

20

Passed

Description

100%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.

An exemplary description: it states concrete capabilities and outputs, gives an explicit 'Use when' clause with seven natural trigger phrases, and explicitly distinguishes the skill from SEO to avoid conflict with sibling marketing skills. All four dimensions sit at the top anchors; the only nitpick is the absence of adjacent industry synonyms like 'generative engine optimization (GEO)', which does not rise to a coverage gap.

DimensionReasoningScore

Specificity

The description lists multiple concrete actions — "optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources", "auditing existing content for E-E-A-T signals", "tracking which pages get cited by which LLMs", "Output is a markdown audit report (default) or JSON for pipeline integration" — with concrete deliverables and tool constraints ("Stdlib-only Python tools"). This matches the top anchor (multiple specific concrete actions, comprehensive coverage) and exceeds anchor 4, which allows coverage gaps.

5 / 5

Completeness

Both questions are explicitly answered: what ("optimize content to be cited by AI language models … as authoritative sources", distinct from SEO, with stated output formats) and when ("Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy"), with concrete trigger phrases. This is the anchor-5 pattern; anchor 4 would require the 'when' to be less explicit.

5 / 5

Trigger Term Quality

It enumerates seven natural trigger phrases users would actually say: "AEO audit", "optimize for ChatGPT", "get cited by Perplexity", "LLM citation strategy", "answer engine optimization", "content for AI search", "E-E-A-T audit", plus additional use-when scenarios. Coverage includes the canonical term, its abbreviation, platform-specific and signal-specific phrasings — comprehensive with synonyms, matching anchor 5 rather than anchor 4 ("a few natural terms missing").

5 / 5

Distinctiveness Conflict Risk

It carves out a clear niche and explicitly disambiguates from its nearest overlapping skill: "Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings." The trigger terms (AEO audit, E-E-A-T audit, LLM citation strategy) are specific to this domain, minimizing wrong-skill triggering — anchor 5 rather than anchor 4 (minor overlap risk).

5 / 5

Total

20

/

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

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